From 5bb151902332d0d6cd1ee6266a13e2ae99bdf67e Mon Sep 17 00:00:00 2001
From: maurolepore <maurolepore@gmail.com>
Date: Sat, 13 Jan 2024 22:38:55 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=202Degrees?=
 =?UTF-8?q?Investing/tiltIndicator@13434cbd38af2e88f1c72b0c8660dc1cfe75786?=
 =?UTF-8?q?a=20=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 404.html                                                   | 2 +-
 CONTRIBUTING.html                                          | 2 +-
 LICENSE-text.html                                          | 2 +-
 LICENSE.html                                               | 2 +-
 articles/extending-outputs.html                            | 2 +-
 articles/handling-long-runtime.html                        | 2 +-
 articles/index.html                                        | 2 +-
 articles/tiltIndicator.html                                | 2 +-
 authors.html                                               | 6 +++---
 index.html                                                 | 2 +-
 news/index.html                                            | 7 ++++++-
 pkgdown.yml                                                | 2 +-
 pull_request_template.html                                 | 2 +-
 reference/aka.html                                         | 2 +-
 reference/deprecated.html                                  | 2 +-
 reference/document_default_value.html                      | 2 +-
 reference/e.html                                           | 2 +-
 reference/emissions_profile.html                           | 2 +-
 .../emissions_profile_any_compute_profile_ranking.html     | 2 +-
 reference/emissions_profile_upstream.html                  | 2 +-
 reference/example_companies.html                           | 2 +-
 reference/example_dictionary.html                          | 2 +-
 reference/example_raw_companies.html                       | 2 +-
 reference/extdata_path.html                                | 2 +-
 reference/index.html                                       | 2 +-
 reference/jitter_range.html                                | 2 +-
 reference/nest_levels.html                                 | 2 +-
 reference/percent_noise.html                               | 2 +-
 reference/pipe.html                                        | 2 +-
 reference/rename.html                                      | 2 +-
 reference/rowid.default.html                               | 2 +-
 reference/rowid.html                                       | 2 +-
 reference/sector_profile.html                              | 2 +-
 .../sector_profile_any_pivot_type_sector_subsector.html    | 2 +-
 .../sector_profile_any_polish_output_at_company_level.html | 2 +-
 reference/sector_profile_any_prepare_scenario.html         | 2 +-
 reference/sector_profile_any_prune_companies.html          | 2 +-
 reference/sector_profile_upstream.html                     | 2 +-
 reference/summarize_range.html                             | 2 +-
 reference/tidyeval.html                                    | 2 +-
 reference/tiltIndicator-package.html                       | 2 +-
 reference/unnest_product.html                              | 2 +-
 search.json                                                | 2 +-
 43 files changed, 50 insertions(+), 45 deletions(-)

diff --git a/404.html b/404.html
index 5a4dd8df8..624fb9eae 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://2degreesinvesting.github.io/tiltIndicator/index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index 36a85102e..940978576 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 80402952b..ad454ed33 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE.html b/LICENSE.html
index 0d4473c69..2c6724a44 100644
--- a/LICENSE.html
+++ b/LICENSE.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/extending-outputs.html b/articles/extending-outputs.html
index 61ca9e119..fda855e43 100644
--- a/articles/extending-outputs.html
+++ b/articles/extending-outputs.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/handling-long-runtime.html b/articles/handling-long-runtime.html
index 294de124a..13b3c8448 100644
--- a/articles/handling-long-runtime.html
+++ b/articles/handling-long-runtime.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index d3c92cdbc..95906060f 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/tiltIndicator.html b/articles/tiltIndicator.html
index 610baa861..06ca95b33 100644
--- a/articles/tiltIndicator.html
+++ b/articles/tiltIndicator.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/authors.html b/authors.html
index febbbfdaf..f9b5fedae 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -88,13 +88,13 @@ <h2 id="citation">Citation</h2>
 
       <p>Lepore M, Trompke T, Delacombaz L, Singhal K, Ho L (2024).
 <em>tiltIndicator: Indicators for the 'TILT' Project</em>.
-R package version 0.0.0.9108, <a href="https://github.com/2DegreesInvesting/tiltIndicator" class="external-link">https://github.com/2DegreesInvesting/tiltIndicator</a>. 
+R package version 0.0.0.9109, <a href="https://github.com/2DegreesInvesting/tiltIndicator" class="external-link">https://github.com/2DegreesInvesting/tiltIndicator</a>. 
 </p>
       <pre>@Manual{,
   title = {tiltIndicator: Indicators for the 'TILT' Project},
   author = {Mauro Lepore and Tilman Trompke and Linda Delacombaz and Kalash Singhal and Lyanne Ho},
   year = {2024},
-  note = {R package version 0.0.0.9108},
+  note = {R package version 0.0.0.9109},
   url = {https://github.com/2DegreesInvesting/tiltIndicator},
 }</pre>
     </div>
diff --git a/index.html b/index.html
index ae5a36eb3..fdf83c65f 100644
--- a/index.html
+++ b/index.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index 1d818df8e..077bff6c1 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -54,6 +54,11 @@
       <small>Source: <a href="https://github.com/2DegreesInvesting/tiltIndicator/blob/HEAD/NEWS.md" class="external-link"><code>NEWS.md</code></a></small>
     </div>
 
+    <div class="section level2">
+<h2 class="pkg-version" data-toc-text="0.0.0.9109" id="tiltindicator-0009109">tiltIndicator 0.0.0.9109<a class="anchor" aria-label="anchor" href="#tiltindicator-0009109"></a></h2>
+<ul><li>
+<code><a href="../reference/emissions_profile_any_compute_profile_ranking.html">emissions_profile_any_compute_profile_ranking()</a></code> is now deprecated. This function is now internal (<a href="https://github.com/2DegreesInvesting/tiltIndicator/issues/669" class="external-link">#669</a>).</li>
+</ul></div>
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.0.0.9108" id="tiltindicator-0009108">tiltIndicator 0.0.0.9108<a class="anchor" aria-label="anchor" href="#tiltindicator-0009108"></a></h2>
 <ul><li>
diff --git a/pkgdown.yml b/pkgdown.yml
index 39cf57948..9da3334f2 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -5,7 +5,7 @@ articles:
   extending-outputs: extending-outputs.html
   handling-long-runtime: handling-long-runtime.html
   tiltIndicator: tiltIndicator.html
-last_built: 2024-01-13T22:35Z
+last_built: 2024-01-13T22:38Z
 urls:
   reference: https://2degreesinvesting.github.io/tiltIndicator/reference
   article: https://2degreesinvesting.github.io/tiltIndicator/articles
diff --git a/pull_request_template.html b/pull_request_template.html
index afea2513d..22eac2e33 100644
--- a/pull_request_template.html
+++ b/pull_request_template.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/aka.html b/reference/aka.html
index 01ef0a397..c0eb1fbad 100644
--- a/reference/aka.html
+++ b/reference/aka.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/deprecated.html b/reference/deprecated.html
index 4f710b93c..556482d57 100644
--- a/reference/deprecated.html
+++ b/reference/deprecated.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/document_default_value.html b/reference/document_default_value.html
index d587f28a1..486412806 100644
--- a/reference/document_default_value.html
+++ b/reference/document_default_value.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/e.html b/reference/e.html
index 406dff2af..4b9508942 100644
--- a/reference/e.html
+++ b/reference/e.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/emissions_profile.html b/reference/emissions_profile.html
index e4620bf87..5de3274c4 100644
--- a/reference/emissions_profile.html
+++ b/reference/emissions_profile.html
@@ -64,7 +64,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/emissions_profile_any_compute_profile_ranking.html b/reference/emissions_profile_any_compute_profile_ranking.html
index 40924246e..b632b162b 100644
--- a/reference/emissions_profile_any_compute_profile_ranking.html
+++ b/reference/emissions_profile_any_compute_profile_ranking.html
@@ -14,7 +14,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/emissions_profile_upstream.html b/reference/emissions_profile_upstream.html
index 057b6c112..367ab0294 100644
--- a/reference/emissions_profile_upstream.html
+++ b/reference/emissions_profile_upstream.html
@@ -74,7 +74,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_companies.html b/reference/example_companies.html
index 8557dad0b..f8b906bf5 100644
--- a/reference/example_companies.html
+++ b/reference/example_companies.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_dictionary.html b/reference/example_dictionary.html
index 26e0e5a2f..f70d071e4 100644
--- a/reference/example_dictionary.html
+++ b/reference/example_dictionary.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_raw_companies.html b/reference/example_raw_companies.html
index 8800ed868..72a458019 100644
--- a/reference/example_raw_companies.html
+++ b/reference/example_raw_companies.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/extdata_path.html b/reference/extdata_path.html
index fc5253d05..bf7ec2242 100644
--- a/reference/extdata_path.html
+++ b/reference/extdata_path.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 722cc7ada..a16fc0ebc 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/jitter_range.html b/reference/jitter_range.html
index 7ffde47a1..6dd44847a 100644
--- a/reference/jitter_range.html
+++ b/reference/jitter_range.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">tiltIndicator</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9108</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9109</small>
 
     
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index d26cf2530..c8fa9cd7e 100644
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+++ b/reference/nest_levels.html
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index 6522456dc..e7e57a715 100644
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diff --git a/reference/pipe.html b/reference/pipe.html
index 26ed3dce6..7c07585f1 100644
--- a/reference/pipe.html
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diff --git a/reference/rename.html b/reference/rename.html
index 531409f27..9481a2797 100644
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diff --git a/reference/sector_profile.html b/reference/sector_profile.html
index e97c88f5f..0fbf5e7b9 100644
--- a/reference/sector_profile.html
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diff --git a/reference/sector_profile_any_pivot_type_sector_subsector.html b/reference/sector_profile_any_pivot_type_sector_subsector.html
index 8ea0d417e..7114aa4f6 100644
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diff --git a/reference/sector_profile_any_polish_output_at_company_level.html b/reference/sector_profile_any_polish_output_at_company_level.html
index 933aa9139..46ae403f2 100644
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diff --git a/reference/sector_profile_any_prepare_scenario.html b/reference/sector_profile_any_prepare_scenario.html
index 9b44af490..9d2645f71 100644
--- a/reference/sector_profile_any_prepare_scenario.html
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@@ -10,7 +10,7 @@
     
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diff --git a/reference/sector_profile_any_prune_companies.html b/reference/sector_profile_any_prune_companies.html
index 7b2e1bb3f..c0e24eb40 100644
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diff --git a/reference/sector_profile_upstream.html b/reference/sector_profile_upstream.html
index 0843f3ab4..612358e37 100644
--- a/reference/sector_profile_upstream.html
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diff --git a/reference/summarize_range.html b/reference/summarize_range.html
index 01a17880d..e4c43128a 100644
--- a/reference/summarize_range.html
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diff --git a/reference/tidyeval.html b/reference/tidyeval.html
index 0a0cc9ee1..23595de59 100644
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diff --git a/reference/tiltIndicator-package.html b/reference/tiltIndicator-package.html
index abff60bf5..ea91f69c1 100644
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diff --git a/reference/unnest_product.html b/reference/unnest_product.html
index 74afe9912..71f03a795 100644
--- a/reference/unnest_product.html
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     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 50a44a871..b2b9d0e5b 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://2degreesinvesting.github.io/tiltIndicator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tiltIndicator","title":"Contributing to tiltIndicator","text":"project follows number guides, principles, books. familiar , faster contribution approved. Guides principles: tiltIndicator mvp guide. Tidyverse style guide. Tidyverse design guide. Tidyverse code review principles. Books: Happy Git R. forgot teach R. R data science. Advanced R. R packages.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 tiltIndicator authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Handling a long runtime","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE) library(tidyr, warn.conflicts = FALSE) library(purrr, warn.conflicts = FALSE) library(rappdirs) library(future) library(furrr) library(fs) library(tiltIndicator) library(tiltToyData)  options(readr.show_col_types = FALSE) # Enable computing over multiple workers in parallel plan(multisession)  # Helpers ----  cache_path <- function(..., parent = cache_dir()) {   path(parent, ...) }  cache_dir <- function() {   user_cache_dir(appname = \"tiltIndicator\") }  job_pmap <- function(job, .f) {   job |>     pick_undone() |>     select(data, file) |>     future_pwalk(.f, .progress = TRUE)    map_df(job$file, read_rds) }  nest_chunk <- function(data, .by, chunks) {   data |>     nest(.by = all_of(.by)) |>     mutate(data, chunk = as.integer(cut(row_number(), chunks))) |>     unnest(data) |>     nest(.by = chunk) }  add_file <- function(data, parent = cache_path(), ext = \".rds\") {   dir_create(parent)   mutate(data, file = path(parent, paste0(chunk, ext))) }  pick_undone <- function(data) {   data |>     add_done() |>     filter(!done) }  add_done <- function(data, file) {   mutate(data, done = file_exists(file)) }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"sector-profile-upstream","dir":"Articles","previous_headings":"","what":"Sector profile upstream","title":"Handling a long runtime","text":"","code":"# TODO: Replace with `read_csv(\"/path/to/input/companies.csv\")` companies <- read_csv(toy_sector_profile_upstream_companies()) # TODO: Replace with `read_csv(\"/path/to/input/scenarios.csv\")` scenarios <- read_csv(toy_sector_profile_any_scenarios()) # TODO: Replace with `read_csv(\"/path/to/input/upstream_products.csv\")` inputs <- read_csv(toy_sector_profile_upstream_products())  # Create a \"job\" data frame where each row is a chunk of data sector_profile_upstream_job <- companies |>   nest_chunk(.by = aka(\"id\"), chunks = 3) |>   add_file(parent = cache_path(\"sector_profile_upstream\"))  # Chunks of data will be distributed across workers and saved to a file sector_profile_upstream_job #> # A tibble: 3 × 3 #>   chunk data             file                                                 #>   <int> <list>           <fs::path>                                           #> 1     1 <tibble [4 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/1.rds #> 2     2 <tibble [2 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/2.rds #> 3     3 <tibble [2 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/3.rds  # Run each indicator chunk across multiple workers and output a combined result sector_profile_upstream_result <- sector_profile_upstream_job |>   job_pmap(\\(data, file) write_rds(sector_profile_upstream(data, scenarios, inputs), file))  # TODO: `... |> write_csv(\"/path/to/output/product.csv\")` sector_profile_upstream_result |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  # TODO: `... |> write_csv(\"/path/to/output/company.csv\")` sector_profile_upstream_result |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows  # Each chunk result was saved to a file dir_tree(cache_path(\"sector_profile_upstream\")) #> ~/.cache/tiltIndicator/sector_profile_upstream #> ├── 1.rds #> ├── 2.rds #> └── 3.rds"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"emissions-profile","dir":"Articles","previous_headings":"","what":"Emissions profile","title":"Handling a long runtime","text":"Read companies data, define chunks files later save . lot going : Define function runs indicator data (companies) writes file. additional dataset products won’t passed function rather accessed global environment. Skip chunk already saved (previous, incomplete run). Select columns data file match names *_rds(). allows use *_pmap() succinct way. accomplished. Instead saturating memory stored chunk file. can now read saved files . results many may also need batches work multi-file datasets. Typically ’ll finish writing one “*.csv” file results product level, another one results company level. next time run indicator need cleanup.","code":"# TODO: Replace with `read_csv(\"/path/to/input/companies.csv\")` companies <- read_csv(toy_emissions_profile_any_companies())  # TODO: Experiment to find the number of chunks that works best for you emissions_profile_job <- companies |>   nest_chunk(.by = aka(\"id\"), chunks = 3) |>   add_file(parent = cache_path(\"emissions_profile\"))  # `nest_chunk()` ensures all rows of a company fall in the same chunk slice(emissions_profile_job, 1) |> unnest(data) #> # A tibble: 25 × 9 #>    chunk companies_id  activity_uuid_produc…¹ clustered country ei_activity_name #>    <int> <chr>         <chr>                  <chr>     <chr>   <chr>            #>  1     1 antimonarchy… 76269c17-78d6-420b-99… tent      germany market for shed… #>  2     1 celestial_lo… 76269c17-78d6-420b-99… table hi… spain   market for shed… #>  3     1 nonphilosoph… 76269c17-78d6-420b-99… surface … germany market for deep… #>  4     1 nonphilosoph… 76269c17-78d6-420b-99… surface … germany market for deep… #>  5     1 asteria_mega… 76269c17-78d6-420b-99… tent      austria market for shed… #>  6     1 quasifaithfu… 76269c17-78d6-420b-99… tent      germany market for shed… #>  7     1 spectacular_… 76269c17-78d6-420b-99… open spa… france  market for shed… #>  8     1 contrite_sil… 76269c17-78d6-420b-99… tent      germany market for shed… #>  9     1 harmless_owl… 76269c17-78d6-420b-99… tent      germany market for shed… #> 10     1 fascist_maia… 76269c17-78d6-420b-99… tent      germany market for shed… #> # ℹ 15 more rows #> # ℹ abbreviated name: ¹​activity_uuid_product_uuid #> # ℹ 3 more variables: main_activity <chr>, unit <chr>, file <fs::path>  slice(emissions_profile_job, 2) |> unnest(data) #> # A tibble: 26 × 9 #>    chunk companies_id  activity_uuid_produc…¹ clustered country ei_activity_name #>    <int> <chr>         <chr>                  <chr>     <chr>   <chr>            #>  1     2 carbonless_d… 76269c17-78d6-420b-99… garden f… nether… market for shed… #>  2     2 baldish_anem… 76269c17-78d6-420b-99… furnitur… germany market for shed… #>  3     2 relegable_so… 76269c17-78d6-420b-99… tent      austria market for shed… #>  4     2 psychodelic_… 76269c17-78d6-420b-99… tent      austria market for shed… #>  5     2 fellow_bovine 76269c17-78d6-420b-99… tent      germany market for shed… #>  6     2 armourpierci… 833caa78-30df-4374-90… garden f… germany market for shed… #>  7     2 equilibristi… 76269c17-78d6-420b-99… garden f… nether… market for shed… #>  8     2 angular_oreg… 76269c17-78d6-420b-99… exhibiti… germany market for shed… #>  9     2 ergophilic_f… 76269c17-78d6-420b-99… tent      austria market for shed… #> 10     2 graphicial_y… 76269c17-78d6-420b-99… garden f… nether… market for shed… #> # ℹ 16 more rows #> # ℹ abbreviated name: ¹​activity_uuid_product_uuid #> # ℹ 3 more variables: main_activity <chr>, unit <chr>, file <fs::path> # TODO: Replace with `read_csv(\"/path/to/input/products.csv\")` products <- tiltIndicator::products #> Warning: `products` was deprecated in tiltIndicator 0.0.0.9089. Please use #> `emissions_profile_products_ecoinvent` from tiltToyData.  emissions_profile_rds <- function(data, file) write_rds(emissions_profile(data, products), file)  emissions_profile_job |>   # Skip what's already done (if anything)   pick_undone() |>   # `select(data, file)` matches `emissions_profile_rds(data, file)` to use `*_pwalk()`   select(data, file) |>   # Combined with `plan()` it distributes computations across multiple workers   # The progress bar won't appear in this .Rmd document.   future_pwalk(emissions_profile_rds, .progress = TRUE) dir_tree(cache_path(\"emissions_profile\")) #> ~/.cache/tiltIndicator/emissions_profile #> ├── 1.rds #> ├── 2.rds #> └── 3.rds emissions_profile_result <- map_df(emissions_profile_job$file, read_rds) emissions_profile_result #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows # TODO: `... |> write_csv(\"/path/to/output/product.csv\")` emissions_profile_result |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  # TODO: `... |> write_csv(\"/path/to/output/company.csv\")` emissions_profile_result |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows # WARNING: Deleting the hard-earned .rds files cache_path() |>   dir_ls(recurse = TRUE, type = \"file\") |>   file_delete()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"background-jobs","dir":"Articles","previous_headings":"","what":"Background jobs","title":"Handling a long runtime","text":"may want run background job RStudio can use R session something else process runs background. RStudio may issues. code “~/projects/run.R” may run directly terminal : Rscript ~/projects/run.R. Best remote server, gives stable environment ability briefly rent computer powerful one .","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"emissions-profile","dir":"Articles","previous_headings":"","what":"Emissions profile","title":"The tilt indicators","text":"“emissions profile” measures transition risk product-level company. indicator expressed percentage products high risk, medium risk low risk due products’ relative carbon footprint. assessment first performed product-level can aggregated company-level. “emissions profile” measures relative carbon footprint per product. default option product compared carbon footprint every product. Products higher carbon-footprint face higher risk. identifying carbon footprint one product, products ranked according carbon footprint. ranking method explained Thresholds section. categorization, aggregate products category set relation products company produces. derive “emissions profile”. Please note carbon footprints, emissions used equivalently. Carbon footprint refers emissions occur production stage product emissions inputs. unit CO2e kg. indicator provides share products “low”, “medium”, “high” relative production emissions per company. output indicator contains following: column production emissions column indicating percentile relative () products unit products sector (iii) products segment column indicating whether product “low”, “medium” “high” relative production emissions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example","dir":"Articles","previous_headings":"Emissions profile","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products_ecoinvent())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"emissions-profile-upstream","dir":"Articles","previous_headings":"","what":"Emissions profile upstream","title":"The tilt indicators","text":"indicator “emissions profile upstream” assesses transition risk upstream products due relative carbon footprint upstream products. default option, upstream product compared carbon footprint every upstream product. Upstream products higher carbon footprint face higher risk. company-level, indicator proxies supply chain risk company - based inputs. indicator “emissions profile upstream” therefore similar Product Carbon Transition Risk Indicator, focuses upstream products product company. Upstream products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one upstream product, input products ranked according footprint. ranking method explained Thresholds section. assessing upstream products’ transition risk based carbon footprint product, aggregated company-level. derive percentage upstream products high, medium low transition risk. indicator consists 2 broad steps: Score upstream products: Identifying upstream products product, calculating relative carbon footprint per upstream product. Score companies: Aggregating company-level. sample data set includes inputs co2 footprints product Ecoinvent sectors Europages. NOTE: following columns completely random selection reflect true information: co2 footprints (allowed share licensed data right now) sectors (matching ecoinvent done yet, one sector per product yet)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-1","dir":"Articles","previous_headings":"Emissions profile upstream","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent())  both <- emissions_profile_upstream(companies, inputs)  both |> unnest_product() #> # A tibble: 4,140 × 8 #>    companies_id        grouped_by        risk_category profile_ranking clustered #>    <chr>               <chr>             <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all               medium                  0.469 tent      #>  2 antimonarchy_canine all               low                     0.260 tent      #>  3 antimonarchy_canine all               low                     0.219 tent      #>  4 antimonarchy_canine all               high                    0.938 tent      #>  5 antimonarchy_canine all               medium                  0.635 tent      #>  6 antimonarchy_canine all               low                     0.146 tent      #>  7 antimonarchy_canine input_isic_4digit medium                  0.667 tent      #>  8 antimonarchy_canine input_isic_4digit medium                  0.556 tent      #>  9 antimonarchy_canine input_isic_4digit low                     0.333 tent      #> 10 antimonarchy_canine input_isic_4digit high                    1     tent      #> # ℹ 4,130 more rows #> # ℹ 3 more variables: activity_uuid_product_uuid <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by        risk_category value #>    <chr>               <chr>             <chr>         <dbl> #>  1 antimonarchy_canine all               high          0.167 #>  2 antimonarchy_canine all               medium        0.333 #>  3 antimonarchy_canine all               low           0.5   #>  4 antimonarchy_canine input_isic_4digit high          0.167 #>  5 antimonarchy_canine input_isic_4digit medium        0.5   #>  6 antimonarchy_canine input_isic_4digit low           0.333 #>  7 antimonarchy_canine input_tilt_sector high          0.333 #>  8 antimonarchy_canine input_tilt_sector medium        0.167 #>  9 antimonarchy_canine input_tilt_sector low           0.5   #> 10 antimonarchy_canine input_unit        high          0.333 #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"sector-profile","dir":"Articles","previous_headings":"","what":"Sector profile","title":"The tilt indicators","text":"indicator “sector profile” measures transition risk products based sector’s emissions targets product belongs . sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). assessing product, products category aggregated set relation products company. , therefore, derive company-level information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-2","dir":"Articles","previous_headings":"Sector profile","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"sector-profile-upstream","dir":"Articles","previous_headings":"","what":"Sector profile upstream","title":"The tilt indicators","text":"indicator “sector profile upstream” assesses transition risk input products based sector’s emissions targets input product belongs . indicator can aggregated company level inform supply chain risk SME, based inputs’ transition risk. sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). , therefore, similar Product Sector Risk Indicator focuses input products company needs produce products.input products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one input product, input products ranked according footprint. ranking method explained Thresholds section. assessing input products product, aggregated company-level derive percentage input products required company produce products high, medium low sector transition risk. , therefore, derive company-level information. Please note carbon emissions emissions always mean CO2e.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-3","dir":"Articles","previous_headings":"Sector profile upstream","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_upstream_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios()) inputs <- read_csv(toy_sector_profile_upstream_products())  both <- sector_profile_upstream(companies, scenarios, inputs) both #> # A tibble: 7 × 3 #>   companies_id                             product             company           #>   <chr>                                    <list>              <list>            #> 1 fleischerei-stiefsohn_00000005219477-001 <tibble [180 × 12]> <tibble [42 × 3]> #> 2 pecheries-basques_fra316541-00101        <tibble [14 × 12]>  <tibble [42 × 3]> #> 3 hoche-butter-gmbh_deu422723-693847001    <tibble [70 × 12]>  <tibble [42 × 3]> #> 4 vicquelin-espaces-verts_fra697272-00101  <tibble [70 × 12]>  <tibble [42 × 3]> #> 5 bst-procontrol-gmbh_00000005104947-001   <tibble [70 × 12]>  <tibble [42 × 3]> #> 6 leider-gmbh_00000005064318-001           <tibble [150 × 12]> <tibble [42 × 3]> #> 7 cheries-baqu_neu316541-00101             <tibble [150 × 12]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"thresholds","dir":"Articles","previous_headings":"","what":"Thresholds","title":"The tilt indicators","text":"Products highest percentile (greater high_threshold) classified high transition risk products. Products medium percentile (greater low_threshold lower equal high_threshold) classified medium transition risk products. Products lowest percentile (lower equal low_threshold) classified low transition risk products. details default low_threshold high_threshold, refer documentation corresponding *_profile_*() function (e.g. sector_profile()).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mauro Lepore. Author, maintainer. Tilman Trompke. Author. Linda Delacombaz. Author. Kalash Singhal. Author. Lyanne Ho. Author. 2 Degrees Investing Initiative. Copyright holder, funder.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lepore M, Trompke T, Delacombaz L, Singhal K, Ho L (2024). tiltIndicator: Indicators 'TILT' Project. R package version 0.0.0.9108, https://github.com/2DegreesInvesting/tiltIndicator.","code":"@Manual{,   title = {tiltIndicator: Indicators for the 'TILT' Project},   author = {Mauro Lepore and Tilman Trompke and Linda Delacombaz and Kalash Singhal and Lyanne Ho},   year = {2024},   note = {R package version 0.0.0.9108},   url = {https://github.com/2DegreesInvesting/tiltIndicator}, }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"tiltindicator","dir":"","previous_headings":"","what":"Indicators for the TILT Project","title":"Indicators for the TILT Project","text":"goal tiltIndicator help develop TILT indicator. repository hosts public code may show fake data.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Indicators for the TILT Project","text":"can install development version tiltIndicator GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"2DegreesInvesting/tiltIndicator\")"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Indicators for the TILT Project","text":"examples see Get started.","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product          company          #>    <chr>                              <list>           <list>           #>  1 antimonarchy_canine                <tibble [1 × 6]> <tibble [1 × 3]> #>  2 celestial_lovebird                 <tibble [1 × 6]> <tibble [1 × 3]> #>  3 nonphilosophical_llama             <tibble [1 × 6]> <tibble [1 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [1 × 6]> <tibble [1 × 3]> #>  5 quasifaithful_amphiuma             <tibble [1 × 6]> <tibble [1 × 3]> #>  6 spectacular_americanriverotter     <tibble [1 × 6]> <tibble [1 × 3]> #>  7 contrite_silkworm                  <tibble [1 × 6]> <tibble [1 × 3]> #>  8 harmless_owlbutterfly              <tibble [1 × 6]> <tibble [1 × 3]> #>  9 fascist_maiasaura                  <tibble [1 × 6]> <tibble [1 × 3]> #> 10 charismatic_islandwhistler         <tibble [1 × 6]> <tibble [1 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 72 × 7 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine        <NA>       <NA>                       NA tent      #>  2 celestial_lovebird         <NA>       <NA>                       NA table hi… #>  3 nonphilosophical_llama     <NA>       <NA>                       NA surface … #>  4 asteria_megalotomusquinqu… <NA>       <NA>                       NA tent      #>  5 quasifaithful_amphiuma     <NA>       <NA>                       NA tent      #>  6 spectacular_americanriver… <NA>       <NA>                       NA open spa… #>  7 contrite_silkworm          <NA>       <NA>                       NA tent      #>  8 harmless_owlbutterfly      <NA>       <NA>                       NA tent      #>  9 fascist_maiasaura          <NA>       <NA>                       NA tent      #> 10 charismatic_islandwhistler <NA>       <NA>                       NA camper p… #> # ℹ 62 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 72 × 4 #>    companies_id                       grouped_by risk_category value #>    <chr>                              <chr>      <chr>         <dbl> #>  1 antimonarchy_canine                <NA>       <NA>             NA #>  2 celestial_lovebird                 <NA>       <NA>             NA #>  3 nonphilosophical_llama             <NA>       <NA>             NA #>  4 asteria_megalotomusquinquespinosus <NA>       <NA>             NA #>  5 quasifaithful_amphiuma             <NA>       <NA>             NA #>  6 spectacular_americanriverotter     <NA>       <NA>             NA #>  7 contrite_silkworm                  <NA>       <NA>             NA #>  8 harmless_owlbutterfly              <NA>       <NA>             NA #>  9 fascist_maiasaura                  <NA>       <NA>             NA #> 10 charismatic_islandwhistler         <NA>       <NA>             NA #> # ℹ 62 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":null,"dir":"Reference","previous_headings":"","what":"Access a column via an internal alias it's 'also known as' — aka","title":"Access a column via an internal alias it's 'also known as' — aka","text":"Accessing columns way makes code maintainable avoiding column names likely change.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access a column via an internal alias it's 'also known as' — aka","text":"","code":"aka(x, dictionary = example_dictionary())"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access a column via an internal alias it's 'also known as' — aka","text":"x character giving internal 'also known ' column name. dictionary dataframe like example_dictionary().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access a column via an internal alias it's 'also known as' — aka","text":"character.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access a column via an internal alias it's 'also known as' — aka","text":"","code":"aka(\"id\") #> [1] \"companies_id\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated — deprecated","title":"Deprecated — deprecated","text":"functions form f_at_product_level() f_at_company_level() now deprecated favor higher-level wrappers (see ?tiltIndicator::rename).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated — deprecated","text":"","code":"istr_at_product_level(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  pstr_at_product_level(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  xctr_at_product_level(   companies,   co2,   low_threshold = 1/3,   high_threshold = 2/3 )  istr_at_company_level(data)  pstr_at_company_level(data)  xctr_at_company_level(data)  xstr_pivot_type_sector_subsector(data)  xstr_prepare_scenario(scenarios)  xstr_prune_companies(data)  xstr_polish_output_at_company_level(data)  companies  inputs  products  istr_companies  istr_inputs  pstr_companies  xstr_scenarios"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated — deprecated","text":"object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 76 rows 7 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 96 rows 11 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 18 rows 8 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 8 rows 6 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 74 rows 10 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 28 rows 10 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 388 rows 8 columns.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Document the default return value — document_default_value","title":"Document the default return value — document_default_value","text":"Document default return value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Document the default return value — document_default_value","text":"","code":"document_default_value()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Document the default return value — document_default_value","text":"","code":"document_default_value() #> [1] \"A data frame with the column `companies_id`, and the nested columns`product` and `company` holding the outputs at product and company level. Unnesting `product` yields a data frame with at least columns `companies_id`, `grouped_by`, `risk_category`. Unnesting `company` yields a data frame with at least columns `companies_id`, `grouped_by`, `risk_category`, `value`.\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":null,"dir":"Reference","previous_headings":"","what":"Avoid R CMD Check warning about undocumented data sets — e","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"R CMD check wants documentation anything defined file. stick objects e can document single e object internal.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"","code":"e"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"object class environment length 11.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"\"emissions profile\" measures transition risk product-level company. indicator expressed percentage products high risk, medium risk low risk due products' relative carbon footprint. assessment first performed product-level can aggregated company-level. \"emissions profile\" measures relative carbon footprint per product. default option product compared carbon footprint every product. Products higher carbon-footprint face higher risk. identifying carbon footprint one product, products ranked according carbon footprint. ranking method explained Thresholds section. categorization, aggregate products category set relation products company produces. derive \"emissions profile\". Please note carbon footprints, emissions used equivalently. Carbon footprint refers emissions occur production stage product emissions inputs. unit CO2e kg. indicator provides share products \"low\", \"medium\", \"high\" relative production emissions per company. output indicator contains following: column production emissions column indicating percentile relative () products unit (ii) products sector (iii) products segment column indicating whether product \"low\", \"medium\" \"high\" relative production emissions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"emissions_profile(companies, co2, low_threshold = 1/3, high_threshold = 2/3)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, co2 dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products_ecoinvent())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":null,"dir":"Reference","previous_headings":"","what":"Add values to categorize — emissions_profile_any_compute_profile_ranking","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"function deprecated internal. Users need interact function .","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"","code":"emissions_profile_any_compute_profile_ranking(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"data \"co2-like\" data frame -- .e. containing products upstream-products (.k.. inputs).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"input data frame additional columns grouped_by profile_ranking one row per benchmark per company.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"","code":"library(tiltToyData) library(readr, warn.conflicts = FALSE) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies())  products <- read_csv(toy_emissions_profile_products_ecoinvent()) products |> emissions_profile_any_compute_profile_ranking() #> Warning: `emissions_profile_any_compute_profile_ranking()` was deprecated in #> tiltIndicator 0.0.0.9109. #> ℹ This function is now internal. #> # A tibble: 108 × 10 #>    grouped_by profile_ranking activity_uuid_product_uuid           co2_footprint #>    <chr>                <dbl> <chr>                                        <dbl> #>  1 all                  0.5   833caa78-30df-4374-900f-7f88ab44075b        11.1   #>  2 all                  0.167 76269c17-78d6-420b-991a-aa38c51b45b7         0.487 #>  3 all                  1     76269c17-78d6-420b-991a-aa38c51b45b7       479.    #>  4 all                  0.556 833caa78-30df-4374-900f-7f88ab44075b        11.6   #>  5 all                  0.278 833caa78-30df-4374-900f-7f88ab44075b         0.531 #>  6 all                  0.778 76269c17-78d6-420b-991a-aa38c51b45b7       329.    #>  7 all                  0.667 76269c17-78d6-420b-991a-aa38c51b45b7        14.1   #>  8 all                  0.111 833caa78-30df-4374-900f-7f88ab44075b         0.468 #>  9 all                  0.944 bf94b5a7-b7a2-46d1-bb95-84bc560b12fb       464.    #> 10 all                  0.389 bf94b5a7-b7a2-46d1-bb95-84bc560b12fb         7.77  #> # ℹ 98 more rows #> # ℹ 6 more variables: ei_activity_name <chr>, ei_geography <chr>, #> #   isic_4digit <chr>, tilt_sector <chr>, tilt_subsector <chr>, unit <chr>  inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent()) inputs |> emissions_profile_any_compute_profile_ranking() #> # A tibble: 576 × 13 #>    grouped_by profile_ranking activity_uuid_product_uuid        ei_activity_name #>    <chr>                <dbl> <chr>                             <chr>            #>  1 all                 0.25   bf94b5a7-b7a2-46d1-bb95-84bc560b… market for deep… #>  2 all                 0.917  bf94b5a7-b7a2-46d1-bb95-84bc560b… market for shed… #>  3 all                 0.552  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  4 all                 0.958  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  5 all                 0.281  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  6 all                 0.302  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  7 all                 0.354  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  8 all                 0.781  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  9 all                 0.385  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #> 10 all                 0.0312 bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #> # ℹ 566 more rows #> # ℹ 9 more variables: ei_geography <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>, #> #   input_ei_activity_name <chr>, input_isic_4digit <chr>, #> #   input_reference_product_name <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>, input_unit <chr>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"emissions profile upstream\" assesses transition risk upstream products due relative carbon footprint upstream products. default option, upstream product compared carbon footprint every upstream product. Upstream products higher carbon footprint face higher risk. company-level, indicator proxies supply chain risk company - based inputs. indicator \"emissions profile upstream\" therefore similar Product Carbon Transition Risk Indicator, focuses upstream products product company. Upstream products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one upstream product, input products ranked according footprint. ranking method explained Thresholds section. assessing upstream products' transition risk based carbon footprint product, aggregated company-level. derive percentage upstream products high, medium low transition risk. indicator consists 2 broad steps: Score upstream products: Identifying upstream products product, calculating relative carbon footprint per upstream product. Score companies: Aggregating company-level. sample data set includes inputs co2 footprints product Ecoinvent sectors Europages. NOTE: following columns completely random selection reflect true information: co2 footprints (allowed share licensed data right now) sectors (matching ecoinvent done yet, one sector per product yet)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"emissions_profile_upstream(   companies,   co2,   low_threshold = 1/3,   high_threshold = 2/3 )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, co2 dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent())  both <- emissions_profile_upstream(companies, inputs)  both |> unnest_product() #> # A tibble: 4,140 × 8 #>    companies_id        grouped_by        risk_category profile_ranking clustered #>    <chr>               <chr>             <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all               medium                  0.469 tent      #>  2 antimonarchy_canine all               low                     0.260 tent      #>  3 antimonarchy_canine all               low                     0.219 tent      #>  4 antimonarchy_canine all               high                    0.938 tent      #>  5 antimonarchy_canine all               medium                  0.635 tent      #>  6 antimonarchy_canine all               low                     0.146 tent      #>  7 antimonarchy_canine input_isic_4digit medium                  0.667 tent      #>  8 antimonarchy_canine input_isic_4digit medium                  0.556 tent      #>  9 antimonarchy_canine input_isic_4digit low                     0.333 tent      #> 10 antimonarchy_canine input_isic_4digit high                    1     tent      #> # ℹ 4,130 more rows #> # ℹ 3 more variables: activity_uuid_product_uuid <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by        risk_category value #>    <chr>               <chr>             <chr>         <dbl> #>  1 antimonarchy_canine all               high          0.167 #>  2 antimonarchy_canine all               medium        0.333 #>  3 antimonarchy_canine all               low           0.5   #>  4 antimonarchy_canine input_isic_4digit high          0.167 #>  5 antimonarchy_canine input_isic_4digit medium        0.5   #>  6 antimonarchy_canine input_isic_4digit low           0.333 #>  7 antimonarchy_canine input_tilt_sector high          0.333 #>  8 antimonarchy_canine input_tilt_sector medium        0.167 #>  9 antimonarchy_canine input_tilt_sector low           0.5   #> 10 antimonarchy_canine input_unit        high          0.333 #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Create example companies — example_companies","title":"Create example companies — example_companies","text":"Create example companies","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create example companies — example_companies","text":"","code":"example_companies(...)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create example companies — example_companies","text":"... Passed tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create example companies — example_companies","text":"tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create example companies — example_companies","text":"","code":"example_companies() #> # A tibble: 1 × 8 #>   companies_id clustered activity_uuid_product_uuid sector subsector tilt_sector #>   <chr>        <chr>     <chr>                      <chr>  <chr>     <chr>       #> 1 a            a         a                          total  energy    a           #> # ℹ 2 more variables: tilt_subsector <chr>, type <chr>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of example data — example_dictionary","title":"Dictionary of example data — example_dictionary","text":"dataset created noramalized set tables allow developers add change example datasets compact consistent way.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dictionary of example data — example_dictionary","text":"","code":"example_dictionary(remove_id = TRUE)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dictionary of example data — example_dictionary","text":"remove_id Remove table id's?","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dictionary of example data — example_dictionary","text":"tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of example data — example_dictionary","text":"","code":"example_dictionary() |> print(n = Inf) #> # A tibble: 29 × 4 #>    aka           column                           table     value  #>    <chr>         <chr>                            <chr>     <chr>  #>  1 id            companies_id                     companies a      #>  2 cluster       clustered                        companies a      #>  3 uid           activity_uuid_product_uuid       companies a      #>  4 xsector       sector                           companies total  #>  5 xsubsector    subsector                        companies energy #>  6 tsector       tilt_sector                      companies a      #>  7 tsubsector    tilt_subsector                   companies a      #>  8 scenario_type type                             companies ipr    #>  9 xsector       sector                           scenarios total  #> 10 xsubsector    subsector                        scenarios energy #> 11 xyear         year                             scenarios 2050   #> 12 co2reduce     reductions                       scenarios 1      #> 13 scenario_type type                             scenarios ipr    #> 14 scenario_name scenario                         scenarios a      #> 15 uid           activity_uuid_product_uuid       products  a      #> 16 tsector       tilt_sector                      products  a      #> 17 xunit         unit                             products  a      #> 18 isic          isic_4digit                      products  '1234' #> 19 co2footprint  co2_footprint                    products  1      #> 20 uid           activity_uuid_product_uuid       inputs    a      #> 21 iuid          input_activity_uuid_product_uuid inputs    a      #> 22 itsector      input_tilt_sector                inputs    a      #> 23 itsubsector   input_tilt_subsector             inputs    a      #> 24 iunit         input_unit                       inputs    a      #> 25 iisic         input_isic_4digit                inputs    '1234' #> 26 ico2footprint input_co2_footprint              inputs    1      #> 27 scenario_type type                             inputs    ipr    #> 28 xsector       sector                           inputs    total  #> 29 xsubsector    subsector                        inputs    energy"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Example raw datasets — example_raw_companies","title":"Example raw datasets — example_raw_companies","text":"Example raw datasets","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example raw datasets — example_raw_companies","text":"","code":"example_raw_companies()  example_raw_weo()  example_raw_ipr()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Example raw datasets — example_raw_companies","text":"dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example raw datasets — example_raw_companies","text":"","code":"example_raw_companies() #> # A tibble: 2 × 5 #>   companies_id ipr_sector ipr_subsector  weo_sector weo_subsector  #>   <chr>        <chr>      <chr>          <chr>      <chr>          #> 1 a            Industry   Iron and Steel Total      Iron and steel #> 2 b            Industry   Chemicals      Total      Chemicals      example_raw_weo() #> # A tibble: 2 × 5 #>   scenario                   weo_sector weo_subsector        year co2_reductions #>   <chr>                      <chr>      <chr>               <dbl>          <dbl> #> 1 Stated Policies Scenario   Total      Biofuels productio…  2020              0 #> 2 Announced Pledges Scenario Total      Biofuels productio…  2020              0 example_raw_ipr() #> # A tibble: 2 × 5 #>   scenario ipr_sector ipr_subsector  year co2_reductions #>   <chr>    <chr>      <lgl>         <dbl>          <dbl> #> 1 1.5C RPS power      NA             2030           0.58 #> 2 1.5C RPS power      NA             2050           1.06"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Get path to child extdata/ — extdata_path","title":"Get path to child extdata/ — extdata_path","text":"Get path child extdata/","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get path to child extdata/ — extdata_path","text":"","code":"extdata_path(path)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get path to child extdata/ — extdata_path","text":"path Character. Path directory inst/extdata/.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get path to child extdata/ — extdata_path","text":"","code":"extdata_path(\"\") #> [1] \"/home/runner/work/_temp/Library/tiltIndicator/extdata/\" list.files(extdata_path(\"\"), recursive = TRUE) #>  [1] \"child/intro-emissions_profile.Rmd\"                      #>  [2] \"child/intro-emissions_profile_upstream.Rmd\"             #>  [3] \"child/intro-general.Rmd\"                                #>  [4] \"child/intro-sector_profile.Rmd\"                         #>  [5] \"child/intro-sector_profile_upstream.Rmd\"                #>  [6] \"child/thresholds.Rmd\"                                   #>  [7] \"roxygen/templates/example-emissions_profile.R\"          #>  [8] \"roxygen/templates/example-emissions_profile_upstream.R\" #>  [9] \"roxygen/templates/example-sector_profile.R\"             #> [10] \"roxygen/templates/example-sector_profile_upstream.R\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand a range adding some random noise — jitter_range","title":"Expand a range adding some random noise — jitter_range","text":"function expands range adding noise left minimum values right maximum values.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand a range adding some random noise — jitter_range","text":"","code":"jitter_range(data, factor = 1, amount = NULL)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand a range adding some random noise — jitter_range","text":"data dataframe columns min max. factor numeric. amount numeric; positive, used amount (see ),     otherwise, = 0 default factor * z/50. Default (NULL): factor * d/5 d     smallest difference x values.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand a range adding some random noise — jitter_range","text":"input dataframe additional columns min_jitter max_jitter.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand a range adding some random noise — jitter_range","text":"","code":"library(tibble) set.seed(123)  data <- tibble(min = -2:2, max = -1:3)  data |> jitter_range(amount = 0.1) #> # A tibble: 5 × 4 #>     min   max min_jitter max_jitter #>   <int> <int>      <dbl>      <dbl> #> 1    -2    -1    -2.18     -0.920   #> 2    -1     0    -1.01      0.00933 #> 3     0     1    -0.0182    1.09    #> 4     1     2     0.990     2.07    #> 5     2     3     1.98      3.27     data |> jitter_range(amount = 2) #> # A tibble: 5 × 4 #>     min   max min_jitter max_jitter #>   <int> <int>      <dbl>      <dbl> #> 1    -2    -1     -3.67      -0.911 #> 2    -1     0     -1.18       1.61  #> 3     0     1     -0.562      1.87  #> 4     1     2      0.157      3.45  #> 5     2     3     -0.823      6.22"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Nest levels — nest_levels","title":"Nest levels — nest_levels","text":"Nest levels","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nest levels — nest_levels","text":"","code":"nest_levels(product, company)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nest levels — nest_levels","text":"","code":"product <- data.frame(companies_id = 1, x = 1) company <- data.frame(companies_id = 1, x = 1) nest_levels(product, company) #> # A tibble: 1 × 3 #>   companies_id product          company          #>          <dbl> <list>           <list>           #> 1            1 <tibble [1 × 1]> <tibble [1 × 1]>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate percent noise — percent_noise","title":"Calculate percent noise — percent_noise","text":"Calculate percent noise","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate percent noise — percent_noise","text":"","code":"percent_noise(x, noisy)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate percent noise — percent_noise","text":"x Numeric vector. noisy Numeric vector.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate percent noise — percent_noise","text":"Numeric vector.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate percent noise — percent_noise","text":"","code":"withr::local_seed(123)  x <- -10:10 noisy <- jitter(x) out <- percent_noise(x, noisy) out #>  [1]  -1.4287999  -0.3797941  -0.4313784  -0.7494546  -2.3170350  -2.8895515 #>  [7]  -2.6696590  -0.4538340  -4.6805472 -14.3131086         Inf   2.3119970 #> [13]   5.9784969   5.0413432   0.6094798   2.3477489   2.4831223   1.4474735 #> [19]   1.9752268   0.5579432   0.6604608  finite <- out[is.finite(out)]  barplot(finite)   mean(finite) #> [1] -0.3449935"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Renamed functions and datasets — rename","title":"Renamed functions and datasets — rename","text":"Conceptual changes: PCTR becomes Emissions Profile. ICTR becomes Emissions Profile Upstream. PSTR becomes Sector Profile. ISTR becomes Sector Profile Upstream. Motivation: names informative easier remember. indicators now share one common word (profile) instead two (transition risk). word \"upstream\" familiar users banks \"inputs\". word \"emissions\" replaces \"carbon\" data use actually take account CO2 equivalents, .e. green house gasses. Compared phrase \"transition risk\", word \"profile\" better reflects indicators used risk assessment also things, broader sustainability assessment, engagement, reporting, etc. Implementation changes: v0.0.0.9084: pstr() -> sector_profile() v0.0.0.9085: istr() -> sector_profile_upstream() v0.0.0.9086: xctr(companies, products) -> emissions_profile() v0.0.0.9087: xctr(companies, inputs) -> emissions_profile_upstream() v0.0.0.9089: datasets names match functions moved tiltToyData v0.0.0.9092: xstr_pivot_type_sector_subsector() -> sector_profile_any_pivot_type_sector_subsector() v0.0.0.9092: xstr_prepare_scenario() -> sector_profile_any_prepare_scenario() v0.0.0.9092: xstr_prune_companies() -> sector_profile_any_prune_companies() v0.0.0.9092: xstr_polish_output_at_company_level() -> sector_profile_any_polish_output_at_company_level()","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renamed functions and datasets — rename","text":"","code":"pstr(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  istr(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  xctr(companies, co2, low_threshold = 1/3, high_threshold = 2/3)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Method — rowid.default","title":"Method — rowid.default","text":"Method","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method — rowid.default","text":"","code":"# S3 method for default rowid()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic — rowid","title":"Generic — rowid","text":"Generic","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic — rowid","text":"","code":"rowid()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"sector profile\" measures transition risk products based sector's emissions targets product belongs . sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). assessing product, products category aggregated set relation products company. , therefore, derive company-level information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"sector_profile(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, scenarios dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium reduction targets. high_threshold numeric value segment medium high reduction targets.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":null,"dir":"Reference","previous_headings":"","what":"Restructure ","title":"Restructure ","text":"Restructure \"sector profile\" companies","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restructure ","text":"","code":"sector_profile_any_pivot_type_sector_subsector(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restructure ","text":"data dataframe columns: ipr_sector ipr_subsector weo_product weo_flow","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restructure ","text":"companies dataset required sector functions.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restructure ","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE)  raw_companies <- example_raw_companies() glimpse(raw_companies) #> Rows: 2 #> Columns: 5 #> $ companies_id  <chr> \"a\", \"b\" #> $ ipr_sector    <chr> \"Industry\", \"Industry\" #> $ ipr_subsector <chr> \"Iron and Steel\", \"Chemicals\" #> $ weo_sector    <chr> \"Total\", \"Total\" #> $ weo_subsector <chr> \"Iron and steel\", \"Chemicals\"  companies <- sector_profile_any_pivot_type_sector_subsector(raw_companies) companies #> # A tibble: 4 × 4 #>   companies_id type  sector   subsector      #>   <chr>        <chr> <chr>    <chr>          #> 1 a            ipr   industry iron and steel #> 2 a            weo   total    iron and steel #> 3 b            ipr   industry chemicals      #> 4 b            weo   total    chemicals"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Polish output at company level — sector_profile_any_polish_output_at_company_level","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"Polish output company level","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"","code":"sector_profile_any_polish_output_at_company_level(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"data output sector_profile*() functions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"","code":"library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  sector_profile(companies, scenarios) |>   unnest_company() |>   sector_profile_any_polish_output_at_company_level() #> # A tibble: 588 × 6 #>    companies_id                         type  scenario year  risk_category value #>    <chr>                                <chr> <chr>    <chr> <chr>         <dbl> #>  1 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  high              1 #>  2 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  medium            0 #>  3 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  low               0 #>  4 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  high              1 #>  5 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  medium            0 #>  6 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  low               0 #>  7 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  high              0 #>  8 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  medium            0 #>  9 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  low               1 #> 10 fleischerei-stiefsohn_0000000521947… weo   announc… 2030  high              0 #> # ℹ 578 more rows  companies_upstream <- read_csv(toy_sector_profile_upstream_companies()) inputs <- read_csv(toy_sector_profile_upstream_products())  sector_profile_upstream(companies_upstream, scenarios, inputs) |>   unnest_company() |>   sector_profile_any_polish_output_at_company_level() #> # A tibble: 294 × 6 #>    companies_id                        type  scenario year  risk_category  value #>    <chr>                               <chr> <chr>    <chr> <chr>          <dbl> #>  1 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  high          0.333  #>  2 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  medium        0.583  #>  3 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  low           0.0833 #>  4 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  high          1      #>  5 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  medium        0      #>  6 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  low           0      #>  7 fleischerei-stiefsohn_000000052194… weo   announc… 2020  high          0      #>  8 fleischerei-stiefsohn_000000052194… weo   announc… 2020  medium        0      #>  9 fleischerei-stiefsohn_000000052194… weo   announc… 2020  low           1      #> 10 fleischerei-stiefsohn_000000052194… weo   announc… 2030  high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":null,"dir":"Reference","previous_headings":"","what":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"Given named list scenarios returns cleaner scenarios dataframe","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"","code":"sector_profile_any_prepare_scenario(scenarios)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"scenarios named list identically structured scenarios.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"single, cleaner dataframe additional column identify rows come scenario.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE)  raw_weo <- example_raw_weo() raw_ipr <- example_raw_ipr() raw_scenarios <- list(weo = raw_weo, ipr = raw_ipr)  sector_profile_any_prepare_scenario(raw_scenarios) #> # A tibble: 4 × 6 #>   scenario                   sector subsector              year reductions type  #>   <chr>                      <chr>  <chr>                 <dbl>      <dbl> <chr> #> 1 stated policies scenario   total  biofuels production …  2020       0    weo   #> 2 announced pledges scenario total  biofuels production …  2020       0    weo   #> 3 1.5c rps                   power  NA                     2030       0.58 ipr   #> 4 1.5c rps                   power  NA                     2050       1.06 ipr"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"company, function drops rows product information missing sector information duplicated.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"","code":"sector_profile_any_prune_companies(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"data Typically \"sector profile\" *companies dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"dataframe maybe fewer rows input data.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"","code":"library(dplyr) # styler: off companies <- tribble(   ~row, ~companies_id, ~clustered, ~activity_uuid_product_uuid, ~tilt_sector,     1L,           \"a\",       \"b1\",                        \"c1\",          \"x\",     2L,           \"a\",         NA,                          NA,          \"x\",     3L,           \"a\",         NA,                          NA,          \"y\",     4L,           \"a\",         NA,                          NA,          \"y\"   ) # styler: off  # Keep row 1: Has product info # Drop row 2: Lacks product info and sector info is duplicated # Keep row 3: Lacks product info but sector info is unique # Drop row 4: Lacks product info and sector info is duplicated companies #> # A tibble: 4 × 5 #>     row companies_id clustered activity_uuid_product_uuid tilt_sector #>   <int> <chr>        <chr>     <chr>                      <chr>       #> 1     1 a            b1        c1                         x           #> 2     2 a            NA        NA                         x           #> 3     3 a            NA        NA                         y           #> 4     4 a            NA        NA                         y            sector_profile_any_prune_companies(companies) #> # A tibble: 2 × 5 #>     row companies_id clustered activity_uuid_product_uuid tilt_sector #>   <int> <chr>        <chr>     <chr>                      <chr>       #> 1     1 a            b1        c1                         x           #> 2     3 a            NA        NA                         y"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"sector profile upstream\" assesses transition risk input products based sector's emissions targets input product belongs . indicator can aggregated company level inform supply chain risk SME, based inputs' transition risk. sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). , therefore, similar Product Sector Risk Indicator focuses input products company needs produce products.input products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one input product, input products ranked according footprint. ranking method explained Thresholds section. assessing input products product, aggregated company-level derive percentage input products required company produce products high, medium low sector transition risk. , therefore, derive company-level information. Please note carbon emissions emissions always mean CO2e.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"sector_profile_upstream(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, scenarios, inputs dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_upstream_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios()) inputs <- read_csv(toy_sector_profile_upstream_products())  both <- sector_profile_upstream(companies, scenarios, inputs) both #> # A tibble: 7 × 3 #>   companies_id                             product             company           #>   <chr>                                    <list>              <list>            #> 1 fleischerei-stiefsohn_00000005219477-001 <tibble [180 × 12]> <tibble [42 × 3]> #> 2 pecheries-basques_fra316541-00101        <tibble [14 × 12]>  <tibble [42 × 3]> #> 3 hoche-butter-gmbh_deu422723-693847001    <tibble [70 × 12]>  <tibble [42 × 3]> #> 4 vicquelin-espaces-verts_fra697272-00101  <tibble [70 × 12]>  <tibble [42 × 3]> #> 5 bst-procontrol-gmbh_00000005104947-001   <tibble [70 × 12]>  <tibble [42 × 3]> #> 6 leider-gmbh_00000005064318-001           <tibble [150 × 12]> <tibble [42 × 3]> #> 7 cheries-baqu_neu316541-00101             <tibble [150 × 12]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the range of a column by groups — summarize_range","title":"Summarize the range of a column by groups — summarize_range","text":"function shortcut dplyr::summarize(data, min = min(x), max = max(x)).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the range of a column by groups — summarize_range","text":"","code":"summarize_range(data, col, .by = NULL)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the range of a column by groups — summarize_range","text":"data dataframe. col Unquoted expression giving name column data. . <tidy-select> Optionally, selection columns group just operation, functioning alternative group_by(). details examples, see ?dplyr_by.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the range of a column by groups — summarize_range","text":"dataframe: rows come underlying groups. columns come grouping keys plus new columns min max. groups dropped.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the range of a column by groups — summarize_range","text":"","code":"library(tibble)  data <- tibble(x = 1:4, group = c(1, 1, 2, 2)) data #> # A tibble: 4 × 2 #>       x group #>   <int> <dbl> #> 1     1     1 #> 2     2     1 #> 3     3     2 #> 4     4     2  summarize_range(data, x, .by = group) #> # A tibble: 2 × 3 #>   group   min   max #>   <dbl> <int> <int> #> 1     1     1     2 #> 2     2     3     4"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tidyeval.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy eval helpers — tidyeval","title":"Tidy eval helpers — tidyeval","text":"page lists tidy eval tools reexported package rlang. learn using tidy eval scripts packages high level, see dplyr programming vignette ggplot2 packages vignette. Metaprogramming section Advanced R may also useful deeper dive. tidy eval operators {{, !!, !!! syntactic constructs specially interpreted tidy eval functions. mostly need {{, !! !!! advanced operators use simple cases. curly-curly operator {{ allows tunnel data-variables passed function arguments inside tidy eval functions. {{ designed individual arguments. pass multiple arguments contained dots, use ... normal way.   enquo() enquos() delay execution one several function arguments. former returns single expression, latter returns list expressions. defused, expressions longer evaluate . must injected back evaluation context !! (single expression) !!! (list expressions).   simple case, code equivalent usage {{ ... . Defusing enquo() enquos() needed complex cases, instance need inspect modify expressions way. .data pronoun object represents current slice data. variable name string, use .data pronoun subset variable [[.   Another tidy eval operator :=. makes possible use glue curly-curly syntax LHS =. technical reasons, R language support complex expressions left =, use := workaround.   Many tidy eval functions like dplyr::mutate() dplyr::summarise() give automatic name unnamed inputs. need create sort automatic names , use as_label(). instance, glue-tunnelling syntax can reproduced manually :   Expressions defused enquo() (tunnelled {{) need simple column names, can arbitrarily complex. as_label() handles cases gracefully. code assumes simple column name, use as_name() instead. safer throws error input name expected.","code":"my_function <- function(data, var, ...) {   data %>%     group_by(...) %>%     summarise(mean = mean({{ var }})) } my_function <- function(data, var, ...) {   # Defuse   var <- enquo(var)   dots <- enquos(...)    # Inject   data %>%     group_by(!!!dots) %>%     summarise(mean = mean(!!var)) } my_var <- \"disp\" mtcars %>% summarise(mean = mean(.data[[my_var]])) my_function <- function(data, var, suffix = \"foo\") {   # Use `{{` to tunnel function arguments and the usual glue   # operator `{` to interpolate plain strings.   data %>%     summarise(\"{{ var }}_mean_{suffix}\" := mean({{ var }})) } my_function <- function(data, var, suffix = \"foo\") {   var <- enquo(var)   prefix <- as_label(var)   data %>%     summarise(\"{prefix}_mean_{suffix}\" := mean(!!var)) }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tiltIndicator-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","title":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","text":"Indicators 'TILT' project.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tiltIndicator-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","text":"Maintainer: Mauro Lepore maurolepore@gmail.com (ORCID) Authors: Tilman Trompke tilman@2degrees-investing.org Linda Delacombaz linda@2degrees-investing.org Kalash Singhal kalash@2degrees-investing.org Lyanne Ho lyho@deloitte.nl contributors: 2 Degrees Investing Initiative contact@2degrees-investing.org [copyright holder, funder]","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest product- and company-level results — unnest_product","title":"Unnest product- and company-level results — unnest_product","text":"Unnest product- company-level results","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest product- and company-level results — unnest_product","text":"","code":"unnest_product(data)  unnest_company(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest product- and company-level results — unnest_product","text":"data nested data frame, e.g. output sector_profile().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unnest product- and company-level results — unnest_product","text":"data frame.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest product- and company-level results — unnest_product","text":"","code":"library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009108","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9108","title":"tiltIndicator 0.0.0.9108","text":"emissions_profile_any_compute_profile_ranking() now handles special cases (#644 @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009107","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9107","title":"tiltIndicator 0.0.0.9107","text":"column *isic_4digit can now values length (#630 @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009106","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9106","title":"tiltIndicator 0.0.0.9106","text":"percent deviation caused jitter*() now even (#627).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009105","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9105","title":"tiltIndicator 0.0.0.9105","text":"New helpers summarize_range() jitter_range() (#622, @AnneSchoenauer).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009104","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9104","title":"tiltIndicator 0.0.0.9104","text":"functions now use companies_id still accept company_id warning (#621).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009102","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9102","title":"tiltIndicator 0.0.0.9102","text":"functions product level now output new column profile_ranking (#613, @AnneSchoenauer).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009101","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9101","title":"tiltIndicator 0.0.0.9101","text":"results product level, clustered longer NA risk_category NA (#614, @AnneSchoenauer, @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009100","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9100","title":"tiltIndicator 0.0.0.9100","text":"Get started now shows sector_profile*() documentation (#619).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009099","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9099","title":"tiltIndicator 0.0.0.9099","text":"Rename emissions_profile_any_add_values_to_categorize emissions_profile_any_compute_profile_ranking (#609). old name retired without deprecation since users.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009098","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9098","title":"tiltIndicator 0.0.0.9098","text":"emissions_profile_any_add_values_to_categorize() now relocates new columns left.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009097","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9097","title":"tiltIndicator 0.0.0.9097","text":"emissions_profile*() uses co2$values_to_categorize present (#605).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009096","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9096","title":"tiltIndicator 0.0.0.9096","text":"New pre-processing helper emissions_profile_any_add_values_to_categorize() (#602).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009095","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9095","title":"tiltIndicator 0.0.0.9095","text":"values grouped_by now less surprising (see related principle) (#601). now simply refer full name corresponding columns “co2” dataset (products inputs) passed emissions_profile*() functions. example, passing column products$tilt_sector now yields value “tilt_sector” group_by – got cropped “tilt_sec”.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009094","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9094","title":"tiltIndicator 0.0.0.9094","text":"name rowid now reserved. input dataset uses , result error. *rowid column now must unique. Duplicated names now result error.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009093","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9093","title":"tiltIndicator 0.0.0.9093","text":"profile functions allow passing *rowid columns input tables output product level (#511).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009092","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9092","title":"tiltIndicator 0.0.0.9092","text":"Rename pre- post-processing helpers (#503).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009091","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9091","title":"tiltIndicator 0.0.0.9091","text":"Rename indicators public documentation (#496).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009090","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9090","title":"tiltIndicator 0.0.0.9090","text":"emissions_profile*() now handles numeric values *isic_4digit (#494).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009089","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9089","title":"tiltIndicator 0.0.0.9089","text":"Deprecate datasets. moved tiltToyData new names (#493).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009088","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9088","title":"tiltIndicator 0.0.0.9088","text":"*_at_product_level() *_at_company_level() now deprecated (#491).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009087","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9087","title":"tiltIndicator 0.0.0.9087","text":"xctr(data, inputs) now deprecated favor new emissions_profile_upstream() (#481).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009086","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9086","title":"tiltIndicator 0.0.0.9086","text":"xctr(data, products) now deprecated favor new emissions_profile() (#481).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009085","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9085","title":"tiltIndicator 0.0.0.9085","text":"istr() now deprecated favor new sector_profile_upstream() (#480).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009084","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9084","title":"tiltIndicator 0.0.0.9084","text":"pstr() now deprecated favor new sector_profile() (#479).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009083","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9083","title":"tiltIndicator 0.0.0.9083","text":"Results company level now preserve unmatched companies (#466).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009082","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9082","title":"tiltIndicator 0.0.0.9082","text":"article “Handling long runtime” now shows enhanced approach (#450).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009081","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9081","title":"tiltIndicator 0.0.0.9081","text":"pstr() now warns companies semicolon ‘;’ sector subsector (#449).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009080","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9080","title":"tiltIndicator 0.0.0.9080","text":"products, activity_uuid_product_uuid now unique (#447).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009079","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9079","title":"tiltIndicator 0.0.0.9079","text":"xctr() now outputs results levels (#443).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009078","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9078","title":"tiltIndicator 0.0.0.9078","text":"istr() now outputs results levels (#442).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009077","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9077","title":"tiltIndicator 0.0.0.9077","text":"pstr() now outputs results levels (#441). New unnest_product() unnest_company() help get results product company levels nested outputs like one pstr().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009076","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9076","title":"tiltIndicator 0.0.0.9076","text":"Ensure outputs duplicate (#438)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009075","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9075","title":"tiltIndicator 0.0.0.9075","text":"XSTR longer errors duplicated scenarios (#437).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009074","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9074","title":"tiltIndicator 0.0.0.9074","text":"indicators product level, company match outputs NA, match outputs 1 row NAs columns (except companeis_id (#436).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009073","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9073","title":"tiltIndicator 0.0.0.9073","text":"indicators company level, company match 3 values sum 1 NA level grouped_by, company match 1 value NA total (#434).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009072","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9072","title":"tiltIndicator 0.0.0.9072","text":"xstr_prepare_scenario() duplicated scenario data now throws error (#431). avoids running indicators corrupt input data alerts preparation must fixed.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009071","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9071","title":"tiltIndicator 0.0.0.9071","text":"istr() now sensitive low_threshold high_threshold (#420).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009070","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9070","title":"tiltIndicator 0.0.0.9070","text":"New xstr_scenarios replaces istr_scenarios pstr_scenarios (@kalashsinghal #413).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009069","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9069","title":"tiltIndicator 0.0.0.9069","text":"pstr_prepare_scenario() now named xstr_prepare_scenario() (#385).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009068","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9068","title":"tiltIndicator 0.0.0.9068","text":"pstr_polish_output_at_copmany_level() now named xstr_polish_output_at_copmany_level() (#383).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009067","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9067","title":"tiltIndicator 0.0.0.9067","text":"xstr_prune_companies() now expects column company_id (#380).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009066","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9066","title":"tiltIndicator 0.0.0.9066","text":"New helper xstr_prune_companies() drop rows product info ‘NA’ & sector info duplicated (#379).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009065","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9065","title":"tiltIndicator 0.0.0.9065","text":"istr_inputs now includes columns required output (@kalashsinghal #376).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009064","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9064","title":"tiltIndicator 0.0.0.9064","text":"XSTR NAs reductions longer error handled specially (@lindadelacombaz #350). ISTR sample data code now use new structure (@kalashsinghal #353). ISTR default thresholds now XCTR (@lindadelacombaz #348).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009063","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9063","title":"tiltIndicator 0.0.0.9063","text":"XXTR functions now stop companies 0-rows (#340).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009062","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9062","title":"tiltIndicator 0.0.0.9062","text":"XSTR XCTR functions now stop scenarios co2 0-row (#337, #338).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009061","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9061","title":"tiltIndicator 0.0.0.9061","text":"PSTR default thresholds now XCTR (@lindadelacombaz #329).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009060","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9060","title":"tiltIndicator 0.0.0.9060","text":"New pstr_polish_output_at_company_level() (#327)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009059","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9059","title":"tiltIndicator 0.0.0.9059","text":"article “Handling long runtime” now updated based experience running pstr*() (#314).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009058","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9058","title":"tiltIndicator 0.0.0.9058","text":"scenario type must sector subsector, else error (#311).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009057","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9057","title":"tiltIndicator 0.0.0.9057","text":"pstr_prepare_scenario() now handles “weo” data correctly (@kalashsinghal #309).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009056","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9056","title":"tiltIndicator 0.0.0.9056","text":"type “ipr”, company grouped_by, value sums 1 (#307).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009055","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9055","title":"tiltIndicator 0.0.0.9055","text":"column grouped_by now includes scenario type (#306). makes column grouped_by contain information scenarios, year, type – meaning columns removed keep output simpler, without loosing information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009054","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9054","title":"tiltIndicator 0.0.0.9054","text":"pstr_at_product_level() now output columns google sheet template (#303). company_id + grouped_by now gets one low, medium & high risk_category (#278).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009053","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9053","title":"tiltIndicator 0.0.0.9053","text":"ISTR old argument scenario now named scenarios, consistently PSTR (#299).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009052","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9052","title":"tiltIndicator 0.0.0.9052","text":"xstr*(), NAs reductions now trigger error (#298).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009051","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9051","title":"tiltIndicator 0.0.0.9051","text":"PSTR example datasets now updated (@kalashsinghal #287).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009050","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9050","title":"tiltIndicator 0.0.0.9050","text":"company & benchmark now gets unique risk_category (@Tilmon #286).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009049","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9049","title":"tiltIndicator 0.0.0.9049","text":"New article handling long runtime (#283) pstr*() gain arguments low_threshold high_threshold (@kalashsinghal #273). pstr*() values now expressed proportion (@lindadelacombaz #274).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009048","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9048","title":"tiltIndicator 0.0.0.9048","text":"xctr_at_product_level() now drops NAs unmatched products (#267).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009047","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9047","title":"tiltIndicator 0.0.0.9047","text":"ictr*() pctr*() now retired (#264).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009046","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9046","title":"tiltIndicator 0.0.0.9046","text":"data now simpler: ictr_companies pctr_companies now retired. Instead use new dataset companies. pctr_ecoinvent_co2 now renamed products. ictr_inputs now renamed inputs.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009045","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9045","title":"tiltIndicator 0.0.0.9045","text":"New xctr*() replace ictr*() pctr*() (#256). functions ictr*() pctr*() internal backward compatibility retired soon.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009044","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9044","title":"tiltIndicator 0.0.0.9044","text":"ICTR PCTR product level longer output needless columns (#251).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009043","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9043","title":"tiltIndicator 0.0.0.9043","text":"ICTR PCTR argument low_threshold now default 1/3 high_threshold 2/3 (#249).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009042","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9042","title":"tiltIndicator 0.0.0.9042","text":"company 3 different products varying footprints now gets correct value (@Tilmon #248).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009041","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9041","title":"tiltIndicator 0.0.0.9041","text":"ICTR PCTR now handle duplicated co2 data (#230).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009040","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9040","title":"tiltIndicator 0.0.0.9040","text":"*ctr_at_product_level() now outputs clustered *_uuid (#242).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009039","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9039","title":"tiltIndicator 0.0.0.9039","text":"ICTR PCTR example datasets now updated (@kalashsinghal #237). pctr_at_product_level() now returns visibly (#239). ICTR PCTR now handle duplicated companies data (#230). ICTR & PCTR ranking benchmarks now updated (@kalashsinghal #229). ICTR & PCTR high_threshold now computed correctly (@kalashsinghal #229).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009037","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9037","title":"tiltIndicator 0.0.0.9037","text":"product-level functions now output three columns (#228, #227): companies_id grouped_by risk_category","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009036","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9036","title":"tiltIndicator 0.0.0.9036","text":"Datasets family now documented together (#224).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009035","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9035","title":"tiltIndicator 0.0.0.9035","text":"company-level functions now output four columns: companies_id grouped_by risk_category value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009034","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9034","title":"tiltIndicator 0.0.0.9034","text":"ICTR PCTR example datasets now updated (@kalashsinghal #217).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009033","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9033","title":"tiltIndicator 0.0.0.9033","text":"top level functions now output first four columns (#214): companies_id grouped_by risk_category value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009032","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9032","title":"tiltIndicator 0.0.0.9032","text":"ictr_at_product_level() pctr_at_product_level() now output company data (#213).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009031","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9031","title":"tiltIndicator 0.0.0.9031","text":"PSTR use new data (@lindadelacombaz #196).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009030","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9030","title":"tiltIndicator 0.0.0.9030","text":"ictr() pctr() first argument now named co2. New internal-ish functions xctr family (#207).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009029","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9029","title":"tiltIndicator 0.0.0.9029","text":"PCTR, company matches input, shares now NA (#205).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009028","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9028","title":"tiltIndicator 0.0.0.9028","text":"ICTR, company matches input, shares now NA (@kalashsinghal #202).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009027","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9027","title":"tiltIndicator 0.0.0.9027","text":"Even companies *uuid absent inputs/co2, shares now sum 1 (@kalashsinghal #197).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009026","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9026","title":"tiltIndicator 0.0.0.9026","text":"indicators now output ungrouped data (#193)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009025","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9025","title":"tiltIndicator 0.0.0.9025","text":"indicators now export single similar top-level interface (#189). old functions still available now considered developer-oriented therefore visible website. output also similar: first column always id, second column always transition_risk, following column(s) provide score(s). indicators now output id company score (#190).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009024","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9024","title":"tiltIndicator 0.0.0.9024","text":"ICTR example data now reflects real data closely (@kalashsinghal #170).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009023","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9023","title":"tiltIndicator 0.0.0.9023","text":"ictr_score_companies() now errors inputs_co2 NAs (@kalashsinghal #150). New article Get started (#152).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009022","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9022","title":"tiltIndicator 0.0.0.9022","text":"Add istr mvp ( @Lyanneho #144).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009021","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9021","title":"tiltIndicator 0.0.0.9021","text":"BREAKING CHANGES ictr_inputs ictr_companies loose non-crucial columns(@kalashsingal #117). pctr_ecoinvent_co2 pctr_companies loose non-crucial columns (#116). BUG FIXES ictr_score_companies() ictr_score_companies() now return three rows per company regardless number rows co2 data (#122). pctr_score_companies() ictr_score_companies() now return three rows per company (@kalashsinghal #111).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009018","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9018","title":"tiltIndicator 0.0.0.9018","text":"Document PCTR functions (@kalashsinghal, #104). New ICTR functions datasets (@kalashsinghal, #90).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009017","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9017","title":"tiltIndicator 0.0.0.9017","text":"Add ICTR MVP (#90).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009016","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9016","title":"tiltIndicator 0.0.0.9016","text":"un-prefixed pstr datasets now retired (#79).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009014","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9014","title":"tiltIndicator 0.0.0.9014","text":"Remove pstr_plot_company() (#84). name PSTR datasets now include prefix “pstr_” (#74). developer-oriented functions now internal (#67). Remove internal article (#66).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009013","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9013","title":"tiltIndicator 0.0.0.9013","text":"New pctr_*() family functions datasets (#60, #61).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009012","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9012","title":"tiltIndicator 0.0.0.9012","text":"Add pctr (@Tilmon, #56)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009011","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9011","title":"tiltIndicator 0.0.0.9011","text":"FIX: article pstr now shows expected content (#52). FIX: mvp_path() nonexistent path now throws error (#51).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009010","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9010","title":"tiltIndicator 0.0.0.9010","text":"Document pstr_*() functions (@lindadelacombaz, #50) dev: Rename helper render_to_list() (#43). dev: Prune needless helpers (#42). dev: Rename data-files related pstr (#41). dev: Fix CODEOWNERS.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009009","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9009","title":"tiltIndicator 0.0.0.9009","text":"dev: Rename data-files related pstr ’s easier express Linda’s ownership (#41).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009008","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9008","title":"tiltIndicator 0.0.0.9008","text":"dev: Use CODEOWNERS (#39).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009007","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9007","title":"tiltIndicator 0.0.0.9007","text":"pstr_at_company_level() fix missing argument companies (#38). dev: Address R CMD Check undefined global variables (#37).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009006","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9006","title":"tiltIndicator 0.0.0.9006","text":"dev: Use new argument left_join().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009004","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9004","title":"tiltIndicator 0.0.0.9004","text":"dev: Address dplyr warnings (#32).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009003","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9003","title":"tiltIndicator 0.0.0.9003","text":"New pstr_*() family product sector transition risk. New pstr_*() family PSTR functions. New article “Product sector transition risk” (@lindadelacombaz, #18, #22).","code":""}]
+[{"path":"https://2degreesinvesting.github.io/tiltIndicator/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tiltIndicator","title":"Contributing to tiltIndicator","text":"project follows number guides, principles, books. familiar , faster contribution approved. Guides principles: tiltIndicator mvp guide. Tidyverse style guide. Tidyverse design guide. Tidyverse code review principles. Books: Happy Git R. forgot teach R. R data science. Advanced R. R packages.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 tiltIndicator authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"setup","dir":"Articles","previous_headings":"","what":"Setup","title":"Handling a long runtime","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE) library(tidyr, warn.conflicts = FALSE) library(purrr, warn.conflicts = FALSE) library(rappdirs) library(future) library(furrr) library(fs) library(tiltIndicator) library(tiltToyData)  options(readr.show_col_types = FALSE) # Enable computing over multiple workers in parallel plan(multisession)  # Helpers ----  cache_path <- function(..., parent = cache_dir()) {   path(parent, ...) }  cache_dir <- function() {   user_cache_dir(appname = \"tiltIndicator\") }  job_pmap <- function(job, .f) {   job |>     pick_undone() |>     select(data, file) |>     future_pwalk(.f, .progress = TRUE)    map_df(job$file, read_rds) }  nest_chunk <- function(data, .by, chunks) {   data |>     nest(.by = all_of(.by)) |>     mutate(data, chunk = as.integer(cut(row_number(), chunks))) |>     unnest(data) |>     nest(.by = chunk) }  add_file <- function(data, parent = cache_path(), ext = \".rds\") {   dir_create(parent)   mutate(data, file = path(parent, paste0(chunk, ext))) }  pick_undone <- function(data) {   data |>     add_done() |>     filter(!done) }  add_done <- function(data, file) {   mutate(data, done = file_exists(file)) }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"sector-profile-upstream","dir":"Articles","previous_headings":"","what":"Sector profile upstream","title":"Handling a long runtime","text":"","code":"# TODO: Replace with `read_csv(\"/path/to/input/companies.csv\")` companies <- read_csv(toy_sector_profile_upstream_companies()) # TODO: Replace with `read_csv(\"/path/to/input/scenarios.csv\")` scenarios <- read_csv(toy_sector_profile_any_scenarios()) # TODO: Replace with `read_csv(\"/path/to/input/upstream_products.csv\")` inputs <- read_csv(toy_sector_profile_upstream_products())  # Create a \"job\" data frame where each row is a chunk of data sector_profile_upstream_job <- companies |>   nest_chunk(.by = aka(\"id\"), chunks = 3) |>   add_file(parent = cache_path(\"sector_profile_upstream\"))  # Chunks of data will be distributed across workers and saved to a file sector_profile_upstream_job #> # A tibble: 3 × 3 #>   chunk data             file                                                 #>   <int> <list>           <fs::path>                                           #> 1     1 <tibble [4 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/1.rds #> 2     2 <tibble [2 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/2.rds #> 3     3 <tibble [2 × 6]> ~/.cache/tiltIndicator/sector_profile_upstream/3.rds  # Run each indicator chunk across multiple workers and output a combined result sector_profile_upstream_result <- sector_profile_upstream_job |>   job_pmap(\\(data, file) write_rds(sector_profile_upstream(data, scenarios, inputs), file))  # TODO: `... |> write_csv(\"/path/to/output/product.csv\")` sector_profile_upstream_result |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  # TODO: `... |> write_csv(\"/path/to/output/company.csv\")` sector_profile_upstream_result |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows  # Each chunk result was saved to a file dir_tree(cache_path(\"sector_profile_upstream\")) #> ~/.cache/tiltIndicator/sector_profile_upstream #> ├── 1.rds #> ├── 2.rds #> └── 3.rds"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"emissions-profile","dir":"Articles","previous_headings":"","what":"Emissions profile","title":"Handling a long runtime","text":"Read companies data, define chunks files later save . lot going : Define function runs indicator data (companies) writes file. additional dataset products won’t passed function rather accessed global environment. Skip chunk already saved (previous, incomplete run). Select columns data file match names *_rds(). allows use *_pmap() succinct way. accomplished. Instead saturating memory stored chunk file. can now read saved files . results many may also need batches work multi-file datasets. Typically ’ll finish writing one “*.csv” file results product level, another one results company level. next time run indicator need cleanup.","code":"# TODO: Replace with `read_csv(\"/path/to/input/companies.csv\")` companies <- read_csv(toy_emissions_profile_any_companies())  # TODO: Experiment to find the number of chunks that works best for you emissions_profile_job <- companies |>   nest_chunk(.by = aka(\"id\"), chunks = 3) |>   add_file(parent = cache_path(\"emissions_profile\"))  # `nest_chunk()` ensures all rows of a company fall in the same chunk slice(emissions_profile_job, 1) |> unnest(data) #> # A tibble: 25 × 9 #>    chunk companies_id  activity_uuid_produc…¹ clustered country ei_activity_name #>    <int> <chr>         <chr>                  <chr>     <chr>   <chr>            #>  1     1 antimonarchy… 76269c17-78d6-420b-99… tent      germany market for shed… #>  2     1 celestial_lo… 76269c17-78d6-420b-99… table hi… spain   market for shed… #>  3     1 nonphilosoph… 76269c17-78d6-420b-99… surface … germany market for deep… #>  4     1 nonphilosoph… 76269c17-78d6-420b-99… surface … germany market for deep… #>  5     1 asteria_mega… 76269c17-78d6-420b-99… tent      austria market for shed… #>  6     1 quasifaithfu… 76269c17-78d6-420b-99… tent      germany market for shed… #>  7     1 spectacular_… 76269c17-78d6-420b-99… open spa… france  market for shed… #>  8     1 contrite_sil… 76269c17-78d6-420b-99… tent      germany market for shed… #>  9     1 harmless_owl… 76269c17-78d6-420b-99… tent      germany market for shed… #> 10     1 fascist_maia… 76269c17-78d6-420b-99… tent      germany market for shed… #> # ℹ 15 more rows #> # ℹ abbreviated name: ¹​activity_uuid_product_uuid #> # ℹ 3 more variables: main_activity <chr>, unit <chr>, file <fs::path>  slice(emissions_profile_job, 2) |> unnest(data) #> # A tibble: 26 × 9 #>    chunk companies_id  activity_uuid_produc…¹ clustered country ei_activity_name #>    <int> <chr>         <chr>                  <chr>     <chr>   <chr>            #>  1     2 carbonless_d… 76269c17-78d6-420b-99… garden f… nether… market for shed… #>  2     2 baldish_anem… 76269c17-78d6-420b-99… furnitur… germany market for shed… #>  3     2 relegable_so… 76269c17-78d6-420b-99… tent      austria market for shed… #>  4     2 psychodelic_… 76269c17-78d6-420b-99… tent      austria market for shed… #>  5     2 fellow_bovine 76269c17-78d6-420b-99… tent      germany market for shed… #>  6     2 armourpierci… 833caa78-30df-4374-90… garden f… germany market for shed… #>  7     2 equilibristi… 76269c17-78d6-420b-99… garden f… nether… market for shed… #>  8     2 angular_oreg… 76269c17-78d6-420b-99… exhibiti… germany market for shed… #>  9     2 ergophilic_f… 76269c17-78d6-420b-99… tent      austria market for shed… #> 10     2 graphicial_y… 76269c17-78d6-420b-99… garden f… nether… market for shed… #> # ℹ 16 more rows #> # ℹ abbreviated name: ¹​activity_uuid_product_uuid #> # ℹ 3 more variables: main_activity <chr>, unit <chr>, file <fs::path> # TODO: Replace with `read_csv(\"/path/to/input/products.csv\")` products <- tiltIndicator::products #> Warning: `products` was deprecated in tiltIndicator 0.0.0.9089. Please use #> `emissions_profile_products_ecoinvent` from tiltToyData.  emissions_profile_rds <- function(data, file) write_rds(emissions_profile(data, products), file)  emissions_profile_job |>   # Skip what's already done (if anything)   pick_undone() |>   # `select(data, file)` matches `emissions_profile_rds(data, file)` to use `*_pwalk()`   select(data, file) |>   # Combined with `plan()` it distributes computations across multiple workers   # The progress bar won't appear in this .Rmd document.   future_pwalk(emissions_profile_rds, .progress = TRUE) dir_tree(cache_path(\"emissions_profile\")) #> ~/.cache/tiltIndicator/emissions_profile #> ├── 1.rds #> ├── 2.rds #> └── 3.rds emissions_profile_result <- map_df(emissions_profile_job$file, read_rds) emissions_profile_result #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows # TODO: `... |> write_csv(\"/path/to/output/product.csv\")` emissions_profile_result |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  # TODO: `... |> write_csv(\"/path/to/output/company.csv\")` emissions_profile_result |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows # WARNING: Deleting the hard-earned .rds files cache_path() |>   dir_ls(recurse = TRUE, type = \"file\") |>   file_delete()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/handling-long-runtime.html","id":"background-jobs","dir":"Articles","previous_headings":"","what":"Background jobs","title":"Handling a long runtime","text":"may want run background job RStudio can use R session something else process runs background. RStudio may issues. code “~/projects/run.R” may run directly terminal : Rscript ~/projects/run.R. Best remote server, gives stable environment ability briefly rent computer powerful one .","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"emissions-profile","dir":"Articles","previous_headings":"","what":"Emissions profile","title":"The tilt indicators","text":"“emissions profile” measures transition risk product-level company. indicator expressed percentage products high risk, medium risk low risk due products’ relative carbon footprint. assessment first performed product-level can aggregated company-level. “emissions profile” measures relative carbon footprint per product. default option product compared carbon footprint every product. Products higher carbon-footprint face higher risk. identifying carbon footprint one product, products ranked according carbon footprint. ranking method explained Thresholds section. categorization, aggregate products category set relation products company produces. derive “emissions profile”. Please note carbon footprints, emissions used equivalently. Carbon footprint refers emissions occur production stage product emissions inputs. unit CO2e kg. indicator provides share products “low”, “medium”, “high” relative production emissions per company. output indicator contains following: column production emissions column indicating percentile relative () products unit products sector (iii) products segment column indicating whether product “low”, “medium” “high” relative production emissions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example","dir":"Articles","previous_headings":"Emissions profile","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products_ecoinvent())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"emissions-profile-upstream","dir":"Articles","previous_headings":"","what":"Emissions profile upstream","title":"The tilt indicators","text":"indicator “emissions profile upstream” assesses transition risk upstream products due relative carbon footprint upstream products. default option, upstream product compared carbon footprint every upstream product. Upstream products higher carbon footprint face higher risk. company-level, indicator proxies supply chain risk company - based inputs. indicator “emissions profile upstream” therefore similar Product Carbon Transition Risk Indicator, focuses upstream products product company. Upstream products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one upstream product, input products ranked according footprint. ranking method explained Thresholds section. assessing upstream products’ transition risk based carbon footprint product, aggregated company-level. derive percentage upstream products high, medium low transition risk. indicator consists 2 broad steps: Score upstream products: Identifying upstream products product, calculating relative carbon footprint per upstream product. Score companies: Aggregating company-level. sample data set includes inputs co2 footprints product Ecoinvent sectors Europages. NOTE: following columns completely random selection reflect true information: co2 footprints (allowed share licensed data right now) sectors (matching ecoinvent done yet, one sector per product yet)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-1","dir":"Articles","previous_headings":"Emissions profile upstream","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent())  both <- emissions_profile_upstream(companies, inputs)  both |> unnest_product() #> # A tibble: 4,140 × 8 #>    companies_id        grouped_by        risk_category profile_ranking clustered #>    <chr>               <chr>             <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all               medium                  0.469 tent      #>  2 antimonarchy_canine all               low                     0.260 tent      #>  3 antimonarchy_canine all               low                     0.219 tent      #>  4 antimonarchy_canine all               high                    0.938 tent      #>  5 antimonarchy_canine all               medium                  0.635 tent      #>  6 antimonarchy_canine all               low                     0.146 tent      #>  7 antimonarchy_canine input_isic_4digit medium                  0.667 tent      #>  8 antimonarchy_canine input_isic_4digit medium                  0.556 tent      #>  9 antimonarchy_canine input_isic_4digit low                     0.333 tent      #> 10 antimonarchy_canine input_isic_4digit high                    1     tent      #> # ℹ 4,130 more rows #> # ℹ 3 more variables: activity_uuid_product_uuid <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by        risk_category value #>    <chr>               <chr>             <chr>         <dbl> #>  1 antimonarchy_canine all               high          0.167 #>  2 antimonarchy_canine all               medium        0.333 #>  3 antimonarchy_canine all               low           0.5   #>  4 antimonarchy_canine input_isic_4digit high          0.167 #>  5 antimonarchy_canine input_isic_4digit medium        0.5   #>  6 antimonarchy_canine input_isic_4digit low           0.333 #>  7 antimonarchy_canine input_tilt_sector high          0.333 #>  8 antimonarchy_canine input_tilt_sector medium        0.167 #>  9 antimonarchy_canine input_tilt_sector low           0.5   #> 10 antimonarchy_canine input_unit        high          0.333 #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"sector-profile","dir":"Articles","previous_headings":"","what":"Sector profile","title":"The tilt indicators","text":"indicator “sector profile” measures transition risk products based sector’s emissions targets product belongs . sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). assessing product, products category aggregated set relation products company. , therefore, derive company-level information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-2","dir":"Articles","previous_headings":"Sector profile","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"sector-profile-upstream","dir":"Articles","previous_headings":"","what":"Sector profile upstream","title":"The tilt indicators","text":"indicator “sector profile upstream” assesses transition risk input products based sector’s emissions targets input product belongs . indicator can aggregated company level inform supply chain risk SME, based inputs’ transition risk. sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). , therefore, similar Product Sector Risk Indicator focuses input products company needs produce products.input products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one input product, input products ranked according footprint. ranking method explained Thresholds section. assessing input products product, aggregated company-level derive percentage input products required company produce products high, medium low sector transition risk. , therefore, derive company-level information. Please note carbon emissions emissions always mean CO2e.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"example-3","dir":"Articles","previous_headings":"Sector profile upstream","what":"Example","title":"The tilt indicators","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_upstream_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios()) inputs <- read_csv(toy_sector_profile_upstream_products())  both <- sector_profile_upstream(companies, scenarios, inputs) both #> # A tibble: 7 × 3 #>   companies_id                             product             company           #>   <chr>                                    <list>              <list>            #> 1 fleischerei-stiefsohn_00000005219477-001 <tibble [180 × 12]> <tibble [42 × 3]> #> 2 pecheries-basques_fra316541-00101        <tibble [14 × 12]>  <tibble [42 × 3]> #> 3 hoche-butter-gmbh_deu422723-693847001    <tibble [70 × 12]>  <tibble [42 × 3]> #> 4 vicquelin-espaces-verts_fra697272-00101  <tibble [70 × 12]>  <tibble [42 × 3]> #> 5 bst-procontrol-gmbh_00000005104947-001   <tibble [70 × 12]>  <tibble [42 × 3]> #> 6 leider-gmbh_00000005064318-001           <tibble [150 × 12]> <tibble [42 × 3]> #> 7 cheries-baqu_neu316541-00101             <tibble [150 × 12]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/articles/tiltIndicator.html","id":"thresholds","dir":"Articles","previous_headings":"","what":"Thresholds","title":"The tilt indicators","text":"Products highest percentile (greater high_threshold) classified high transition risk products. Products medium percentile (greater low_threshold lower equal high_threshold) classified medium transition risk products. Products lowest percentile (lower equal low_threshold) classified low transition risk products. details default low_threshold high_threshold, refer documentation corresponding *_profile_*() function (e.g. sector_profile()).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Mauro Lepore. Author, maintainer. Tilman Trompke. Author. Linda Delacombaz. Author. Kalash Singhal. Author. Lyanne Ho. Author. 2 Degrees Investing Initiative. Copyright holder, funder.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Lepore M, Trompke T, Delacombaz L, Singhal K, Ho L (2024). tiltIndicator: Indicators 'TILT' Project. R package version 0.0.0.9109, https://github.com/2DegreesInvesting/tiltIndicator.","code":"@Manual{,   title = {tiltIndicator: Indicators for the 'TILT' Project},   author = {Mauro Lepore and Tilman Trompke and Linda Delacombaz and Kalash Singhal and Lyanne Ho},   year = {2024},   note = {R package version 0.0.0.9109},   url = {https://github.com/2DegreesInvesting/tiltIndicator}, }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"tiltindicator","dir":"","previous_headings":"","what":"Indicators for the TILT Project","title":"Indicators for the TILT Project","text":"goal tiltIndicator help develop TILT indicator. repository hosts public code may show fake data.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Indicators for the TILT Project","text":"can install development version tiltIndicator GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"2DegreesInvesting/tiltIndicator\")"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Indicators for the TILT Project","text":"examples see Get started.","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product          company          #>    <chr>                              <list>           <list>           #>  1 antimonarchy_canine                <tibble [1 × 6]> <tibble [1 × 3]> #>  2 celestial_lovebird                 <tibble [1 × 6]> <tibble [1 × 3]> #>  3 nonphilosophical_llama             <tibble [1 × 6]> <tibble [1 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [1 × 6]> <tibble [1 × 3]> #>  5 quasifaithful_amphiuma             <tibble [1 × 6]> <tibble [1 × 3]> #>  6 spectacular_americanriverotter     <tibble [1 × 6]> <tibble [1 × 3]> #>  7 contrite_silkworm                  <tibble [1 × 6]> <tibble [1 × 3]> #>  8 harmless_owlbutterfly              <tibble [1 × 6]> <tibble [1 × 3]> #>  9 fascist_maiasaura                  <tibble [1 × 6]> <tibble [1 × 3]> #> 10 charismatic_islandwhistler         <tibble [1 × 6]> <tibble [1 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 72 × 7 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine        <NA>       <NA>                       NA tent      #>  2 celestial_lovebird         <NA>       <NA>                       NA table hi… #>  3 nonphilosophical_llama     <NA>       <NA>                       NA surface … #>  4 asteria_megalotomusquinqu… <NA>       <NA>                       NA tent      #>  5 quasifaithful_amphiuma     <NA>       <NA>                       NA tent      #>  6 spectacular_americanriver… <NA>       <NA>                       NA open spa… #>  7 contrite_silkworm          <NA>       <NA>                       NA tent      #>  8 harmless_owlbutterfly      <NA>       <NA>                       NA tent      #>  9 fascist_maiasaura          <NA>       <NA>                       NA tent      #> 10 charismatic_islandwhistler <NA>       <NA>                       NA camper p… #> # ℹ 62 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 72 × 4 #>    companies_id                       grouped_by risk_category value #>    <chr>                              <chr>      <chr>         <dbl> #>  1 antimonarchy_canine                <NA>       <NA>             NA #>  2 celestial_lovebird                 <NA>       <NA>             NA #>  3 nonphilosophical_llama             <NA>       <NA>             NA #>  4 asteria_megalotomusquinquespinosus <NA>       <NA>             NA #>  5 quasifaithful_amphiuma             <NA>       <NA>             NA #>  6 spectacular_americanriverotter     <NA>       <NA>             NA #>  7 contrite_silkworm                  <NA>       <NA>             NA #>  8 harmless_owlbutterfly              <NA>       <NA>             NA #>  9 fascist_maiasaura                  <NA>       <NA>             NA #> 10 charismatic_islandwhistler         <NA>       <NA>             NA #> # ℹ 62 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":null,"dir":"Reference","previous_headings":"","what":"Access a column via an internal alias it's 'also known as' — aka","title":"Access a column via an internal alias it's 'also known as' — aka","text":"Accessing columns way makes code maintainable avoiding column names likely change.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access a column via an internal alias it's 'also known as' — aka","text":"","code":"aka(x, dictionary = example_dictionary())"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access a column via an internal alias it's 'also known as' — aka","text":"x character giving internal 'also known ' column name. dictionary dataframe like example_dictionary().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access a column via an internal alias it's 'also known as' — aka","text":"character.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/aka.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Access a column via an internal alias it's 'also known as' — aka","text":"","code":"aka(\"id\") #> [1] \"companies_id\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated — deprecated","title":"Deprecated — deprecated","text":"functions form f_at_product_level() f_at_company_level() now deprecated favor higher-level wrappers (see ?tiltIndicator::rename).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated — deprecated","text":"","code":"istr_at_product_level(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  pstr_at_product_level(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  xctr_at_product_level(   companies,   co2,   low_threshold = 1/3,   high_threshold = 2/3 )  istr_at_company_level(data)  pstr_at_company_level(data)  xctr_at_company_level(data)  xstr_pivot_type_sector_subsector(data)  xstr_prepare_scenario(scenarios)  xstr_prune_companies(data)  xstr_polish_output_at_company_level(data)  companies  inputs  products  istr_companies  istr_inputs  pstr_companies  xstr_scenarios"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/deprecated.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Deprecated — deprecated","text":"object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 76 rows 7 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 96 rows 11 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 18 rows 8 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 8 rows 6 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 74 rows 10 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 28 rows 10 columns. object class spec_tbl_df (inherits tbl_df, tbl, data.frame) 388 rows 8 columns.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":null,"dir":"Reference","previous_headings":"","what":"Document the default return value — document_default_value","title":"Document the default return value — document_default_value","text":"Document default return value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Document the default return value — document_default_value","text":"","code":"document_default_value()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/document_default_value.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Document the default return value — document_default_value","text":"","code":"document_default_value() #> [1] \"A data frame with the column `companies_id`, and the nested columns`product` and `company` holding the outputs at product and company level. Unnesting `product` yields a data frame with at least columns `companies_id`, `grouped_by`, `risk_category`. Unnesting `company` yields a data frame with at least columns `companies_id`, `grouped_by`, `risk_category`, `value`.\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":null,"dir":"Reference","previous_headings":"","what":"Avoid R CMD Check warning about undocumented data sets — e","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"R CMD check wants documentation anything defined file. stick objects e can document single e object internal.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"","code":"e"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/e.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Avoid R CMD Check warning about undocumented data sets — e","text":"object class environment length 11.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"\"emissions profile\" measures transition risk product-level company. indicator expressed percentage products high risk, medium risk low risk due products' relative carbon footprint. assessment first performed product-level can aggregated company-level. \"emissions profile\" measures relative carbon footprint per product. default option product compared carbon footprint every product. Products higher carbon-footprint face higher risk. identifying carbon footprint one product, products ranked according carbon footprint. ranking method explained Thresholds section. categorization, aggregate products category set relation products company produces. derive \"emissions profile\". Please note carbon footprints, emissions used equivalently. Carbon footprint refers emissions occur production stage product emissions inputs. unit CO2e kg. indicator provides share products \"low\", \"medium\", \"high\" relative production emissions per company. output indicator contains following: column production emissions column indicating percentile relative () products unit (ii) products sector (iii) products segment column indicating whether product \"low\", \"medium\" \"high\" relative production emissions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"emissions_profile(companies, co2, low_threshold = 1/3, high_threshold = 2/3)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, co2 dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) products <- read_csv(toy_emissions_profile_products_ecoinvent())  both <- emissions_profile(companies, products) both #> # A tibble: 72 × 3 #>    companies_id                       product           company           #>    <chr>                              <list>            <list>            #>  1 antimonarchy_canine                <tibble [36 × 6]> <tibble [18 × 3]> #>  2 celestial_lovebird                 <tibble [36 × 6]> <tibble [18 × 3]> #>  3 nonphilosophical_llama             <tibble [72 × 6]> <tibble [18 × 3]> #>  4 asteria_megalotomusquinquespinosus <tibble [36 × 6]> <tibble [18 × 3]> #>  5 quasifaithful_amphiuma             <tibble [36 × 6]> <tibble [18 × 3]> #>  6 spectacular_americanriverotter     <tibble [36 × 6]> <tibble [18 × 3]> #>  7 contrite_silkworm                  <tibble [36 × 6]> <tibble [18 × 3]> #>  8 harmless_owlbutterfly              <tibble [36 × 6]> <tibble [18 × 3]> #>  9 fascist_maiasaura                  <tibble [36 × 6]> <tibble [18 × 3]> #> 10 charismatic_islandwhistler         <tibble [36 × 6]> <tibble [18 × 3]> #> # ℹ 62 more rows  both |> unnest_product() #> # A tibble: 2,736 × 7 #>    companies_id        grouped_by  risk_category profile_ranking clustered #>    <chr>               <chr>       <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all         low                    0.167  tent      #>  2 antimonarchy_canine all         high                   1      tent      #>  3 antimonarchy_canine all         high                   0.778  tent      #>  4 antimonarchy_canine all         medium                 0.667  tent      #>  5 antimonarchy_canine all         low                    0.0556 tent      #>  6 antimonarchy_canine all         medium                 0.611  tent      #>  7 antimonarchy_canine isic_4digit medium                 0.5    tent      #>  8 antimonarchy_canine isic_4digit high                   1      tent      #>  9 antimonarchy_canine isic_4digit low                    0.333  tent      #> 10 antimonarchy_canine isic_4digit high                   1      tent      #> # ℹ 2,726 more rows #> # ℹ 2 more variables: activity_uuid_product_uuid <chr>, co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by  risk_category value #>    <chr>               <chr>       <chr>         <dbl> #>  1 antimonarchy_canine all         high          0.333 #>  2 antimonarchy_canine all         medium        0.333 #>  3 antimonarchy_canine all         low           0.333 #>  4 antimonarchy_canine isic_4digit high          0.5   #>  5 antimonarchy_canine isic_4digit medium        0.167 #>  6 antimonarchy_canine isic_4digit low           0.333 #>  7 antimonarchy_canine tilt_sector high          0.5   #>  8 antimonarchy_canine tilt_sector medium        0     #>  9 antimonarchy_canine tilt_sector low           0.5   #> 10 antimonarchy_canine unit        high          0.5   #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":null,"dir":"Reference","previous_headings":"","what":"Add values to categorize — emissions_profile_any_compute_profile_ranking","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"function deprecated internal. Users need interact function .","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"","code":"emissions_profile_any_compute_profile_ranking(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"data \"co2-like\" data frame -- .e. containing products upstream-products (.k.. inputs).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"input data frame additional columns grouped_by profile_ranking one row per benchmark per company.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_any_compute_profile_ranking.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add values to categorize — emissions_profile_any_compute_profile_ranking","text":"","code":"library(tiltToyData) library(readr, warn.conflicts = FALSE) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies())  products <- read_csv(toy_emissions_profile_products_ecoinvent()) products |> emissions_profile_any_compute_profile_ranking() #> Warning: `emissions_profile_any_compute_profile_ranking()` was deprecated in #> tiltIndicator 0.0.0.9109. #> ℹ This function is now internal. #> # A tibble: 108 × 10 #>    grouped_by profile_ranking activity_uuid_product_uuid           co2_footprint #>    <chr>                <dbl> <chr>                                        <dbl> #>  1 all                  0.5   833caa78-30df-4374-900f-7f88ab44075b        11.1   #>  2 all                  0.167 76269c17-78d6-420b-991a-aa38c51b45b7         0.487 #>  3 all                  1     76269c17-78d6-420b-991a-aa38c51b45b7       479.    #>  4 all                  0.556 833caa78-30df-4374-900f-7f88ab44075b        11.6   #>  5 all                  0.278 833caa78-30df-4374-900f-7f88ab44075b         0.531 #>  6 all                  0.778 76269c17-78d6-420b-991a-aa38c51b45b7       329.    #>  7 all                  0.667 76269c17-78d6-420b-991a-aa38c51b45b7        14.1   #>  8 all                  0.111 833caa78-30df-4374-900f-7f88ab44075b         0.468 #>  9 all                  0.944 bf94b5a7-b7a2-46d1-bb95-84bc560b12fb       464.    #> 10 all                  0.389 bf94b5a7-b7a2-46d1-bb95-84bc560b12fb         7.77  #> # ℹ 98 more rows #> # ℹ 6 more variables: ei_activity_name <chr>, ei_geography <chr>, #> #   isic_4digit <chr>, tilt_sector <chr>, tilt_subsector <chr>, unit <chr>  inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent()) inputs |> emissions_profile_any_compute_profile_ranking() #> # A tibble: 576 × 13 #>    grouped_by profile_ranking activity_uuid_product_uuid        ei_activity_name #>    <chr>                <dbl> <chr>                             <chr>            #>  1 all                 0.25   bf94b5a7-b7a2-46d1-bb95-84bc560b… market for deep… #>  2 all                 0.917  bf94b5a7-b7a2-46d1-bb95-84bc560b… market for shed… #>  3 all                 0.552  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  4 all                 0.958  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  5 all                 0.281  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  6 all                 0.302  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  7 all                 0.354  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  8 all                 0.781  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #>  9 all                 0.385  bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #> 10 all                 0.0312 bf94b5a7-b7a2-46d1-bb95-84bc560b… iron-nickel-chr… #> # ℹ 566 more rows #> # ℹ 9 more variables: ei_geography <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>, #> #   input_ei_activity_name <chr>, input_isic_4digit <chr>, #> #   input_reference_product_name <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>, input_unit <chr>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"emissions profile upstream\" assesses transition risk upstream products due relative carbon footprint upstream products. default option, upstream product compared carbon footprint every upstream product. Upstream products higher carbon footprint face higher risk. company-level, indicator proxies supply chain risk company - based inputs. indicator \"emissions profile upstream\" therefore similar Product Carbon Transition Risk Indicator, focuses upstream products product company. Upstream products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one upstream product, input products ranked according footprint. ranking method explained Thresholds section. assessing upstream products' transition risk based carbon footprint product, aggregated company-level. derive percentage upstream products high, medium low transition risk. indicator consists 2 broad steps: Score upstream products: Identifying upstream products product, calculating relative carbon footprint per upstream product. Score companies: Aggregating company-level. sample data set includes inputs co2 footprints product Ecoinvent sectors Europages. NOTE: following columns completely random selection reflect true information: co2 footprints (allowed share licensed data right now) sectors (matching ecoinvent done yet, one sector per product yet)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"emissions_profile_upstream(   companies,   co2,   low_threshold = 1/3,   high_threshold = 2/3 )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, co2 dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/emissions_profile_upstream.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_emissions_profile_any_companies()) inputs <- read_csv(toy_emissions_profile_upstream_products_ecoinvent())  both <- emissions_profile_upstream(companies, inputs)  both |> unnest_product() #> # A tibble: 4,140 × 8 #>    companies_id        grouped_by        risk_category profile_ranking clustered #>    <chr>               <chr>             <chr>                   <dbl> <chr>     #>  1 antimonarchy_canine all               medium                  0.469 tent      #>  2 antimonarchy_canine all               low                     0.260 tent      #>  3 antimonarchy_canine all               low                     0.219 tent      #>  4 antimonarchy_canine all               high                    0.938 tent      #>  5 antimonarchy_canine all               medium                  0.635 tent      #>  6 antimonarchy_canine all               low                     0.146 tent      #>  7 antimonarchy_canine input_isic_4digit medium                  0.667 tent      #>  8 antimonarchy_canine input_isic_4digit medium                  0.556 tent      #>  9 antimonarchy_canine input_isic_4digit low                     0.333 tent      #> 10 antimonarchy_canine input_isic_4digit high                    1     tent      #> # ℹ 4,130 more rows #> # ℹ 3 more variables: activity_uuid_product_uuid <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_co2_footprint <dbl>  both |> unnest_company() #> # A tibble: 1,296 × 4 #>    companies_id        grouped_by        risk_category value #>    <chr>               <chr>             <chr>         <dbl> #>  1 antimonarchy_canine all               high          0.167 #>  2 antimonarchy_canine all               medium        0.333 #>  3 antimonarchy_canine all               low           0.5   #>  4 antimonarchy_canine input_isic_4digit high          0.167 #>  5 antimonarchy_canine input_isic_4digit medium        0.5   #>  6 antimonarchy_canine input_isic_4digit low           0.333 #>  7 antimonarchy_canine input_tilt_sector high          0.333 #>  8 antimonarchy_canine input_tilt_sector medium        0.167 #>  9 antimonarchy_canine input_tilt_sector low           0.5   #> 10 antimonarchy_canine input_unit        high          0.333 #> # ℹ 1,286 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Create example companies — example_companies","title":"Create example companies — example_companies","text":"Create example companies","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create example companies — example_companies","text":"","code":"example_companies(...)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create example companies — example_companies","text":"... Passed tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create example companies — example_companies","text":"tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create example companies — example_companies","text":"","code":"example_companies() #> # A tibble: 1 × 8 #>   companies_id clustered activity_uuid_product_uuid sector subsector tilt_sector #>   <chr>        <chr>     <chr>                      <chr>  <chr>     <chr>       #> 1 a            a         a                          total  energy    a           #> # ℹ 2 more variables: tilt_subsector <chr>, type <chr>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":null,"dir":"Reference","previous_headings":"","what":"Dictionary of example data — example_dictionary","title":"Dictionary of example data — example_dictionary","text":"dataset created noramalized set tables allow developers add change example datasets compact consistent way.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dictionary of example data — example_dictionary","text":"","code":"example_dictionary(remove_id = TRUE)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dictionary of example data — example_dictionary","text":"remove_id Remove table id's?","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dictionary of example data — example_dictionary","text":"tibble().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_dictionary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dictionary of example data — example_dictionary","text":"","code":"example_dictionary() |> print(n = Inf) #> # A tibble: 29 × 4 #>    aka           column                           table     value  #>    <chr>         <chr>                            <chr>     <chr>  #>  1 id            companies_id                     companies a      #>  2 cluster       clustered                        companies a      #>  3 uid           activity_uuid_product_uuid       companies a      #>  4 xsector       sector                           companies total  #>  5 xsubsector    subsector                        companies energy #>  6 tsector       tilt_sector                      companies a      #>  7 tsubsector    tilt_subsector                   companies a      #>  8 scenario_type type                             companies ipr    #>  9 xsector       sector                           scenarios total  #> 10 xsubsector    subsector                        scenarios energy #> 11 xyear         year                             scenarios 2050   #> 12 co2reduce     reductions                       scenarios 1      #> 13 scenario_type type                             scenarios ipr    #> 14 scenario_name scenario                         scenarios a      #> 15 uid           activity_uuid_product_uuid       products  a      #> 16 tsector       tilt_sector                      products  a      #> 17 xunit         unit                             products  a      #> 18 isic          isic_4digit                      products  '1234' #> 19 co2footprint  co2_footprint                    products  1      #> 20 uid           activity_uuid_product_uuid       inputs    a      #> 21 iuid          input_activity_uuid_product_uuid inputs    a      #> 22 itsector      input_tilt_sector                inputs    a      #> 23 itsubsector   input_tilt_subsector             inputs    a      #> 24 iunit         input_unit                       inputs    a      #> 25 iisic         input_isic_4digit                inputs    '1234' #> 26 ico2footprint input_co2_footprint              inputs    1      #> 27 scenario_type type                             inputs    ipr    #> 28 xsector       sector                           inputs    total  #> 29 xsubsector    subsector                        inputs    energy"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Example raw datasets — example_raw_companies","title":"Example raw datasets — example_raw_companies","text":"Example raw datasets","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Example raw datasets — example_raw_companies","text":"","code":"example_raw_companies()  example_raw_weo()  example_raw_ipr()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Example raw datasets — example_raw_companies","text":"dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/example_raw_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Example raw datasets — example_raw_companies","text":"","code":"example_raw_companies() #> # A tibble: 2 × 5 #>   companies_id ipr_sector ipr_subsector  weo_sector weo_subsector  #>   <chr>        <chr>      <chr>          <chr>      <chr>          #> 1 a            Industry   Iron and Steel Total      Iron and steel #> 2 b            Industry   Chemicals      Total      Chemicals      example_raw_weo() #> # A tibble: 2 × 5 #>   scenario                   weo_sector weo_subsector        year co2_reductions #>   <chr>                      <chr>      <chr>               <dbl>          <dbl> #> 1 Stated Policies Scenario   Total      Biofuels productio…  2020              0 #> 2 Announced Pledges Scenario Total      Biofuels productio…  2020              0 example_raw_ipr() #> # A tibble: 2 × 5 #>   scenario ipr_sector ipr_subsector  year co2_reductions #>   <chr>    <chr>      <lgl>         <dbl>          <dbl> #> 1 1.5C RPS power      NA             2030           0.58 #> 2 1.5C RPS power      NA             2050           1.06"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Get path to child extdata/ — extdata_path","title":"Get path to child extdata/ — extdata_path","text":"Get path child extdata/","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get path to child extdata/ — extdata_path","text":"","code":"extdata_path(path)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get path to child extdata/ — extdata_path","text":"path Character. Path directory inst/extdata/.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/extdata_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get path to child extdata/ — extdata_path","text":"","code":"extdata_path(\"\") #> [1] \"/home/runner/work/_temp/Library/tiltIndicator/extdata/\" list.files(extdata_path(\"\"), recursive = TRUE) #>  [1] \"child/intro-emissions_profile.Rmd\"                      #>  [2] \"child/intro-emissions_profile_upstream.Rmd\"             #>  [3] \"child/intro-general.Rmd\"                                #>  [4] \"child/intro-sector_profile.Rmd\"                         #>  [5] \"child/intro-sector_profile_upstream.Rmd\"                #>  [6] \"child/thresholds.Rmd\"                                   #>  [7] \"roxygen/templates/example-emissions_profile.R\"          #>  [8] \"roxygen/templates/example-emissions_profile_upstream.R\" #>  [9] \"roxygen/templates/example-sector_profile.R\"             #> [10] \"roxygen/templates/example-sector_profile_upstream.R\""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand a range adding some random noise — jitter_range","title":"Expand a range adding some random noise — jitter_range","text":"function expands range adding noise left minimum values right maximum values.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand a range adding some random noise — jitter_range","text":"","code":"jitter_range(data, factor = 1, amount = NULL)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand a range adding some random noise — jitter_range","text":"data dataframe columns min max. factor numeric. amount numeric; positive, used amount (see ),     otherwise, = 0 default factor * z/50. Default (NULL): factor * d/5 d     smallest difference x values.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand a range adding some random noise — jitter_range","text":"input dataframe additional columns min_jitter max_jitter.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/jitter_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand a range adding some random noise — jitter_range","text":"","code":"library(tibble) set.seed(123)  data <- tibble(min = -2:2, max = -1:3)  data |> jitter_range(amount = 0.1) #> # A tibble: 5 × 4 #>     min   max min_jitter max_jitter #>   <int> <int>      <dbl>      <dbl> #> 1    -2    -1    -2.18     -0.920   #> 2    -1     0    -1.01      0.00933 #> 3     0     1    -0.0182    1.09    #> 4     1     2     0.990     2.07    #> 5     2     3     1.98      3.27     data |> jitter_range(amount = 2) #> # A tibble: 5 × 4 #>     min   max min_jitter max_jitter #>   <int> <int>      <dbl>      <dbl> #> 1    -2    -1     -3.67      -0.911 #> 2    -1     0     -1.18       1.61  #> 3     0     1     -0.562      1.87  #> 4     1     2      0.157      3.45  #> 5     2     3     -0.823      6.22"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Nest levels — nest_levels","title":"Nest levels — nest_levels","text":"Nest levels","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Nest levels — nest_levels","text":"","code":"nest_levels(product, company)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/nest_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Nest levels — nest_levels","text":"","code":"product <- data.frame(companies_id = 1, x = 1) company <- data.frame(companies_id = 1, x = 1) nest_levels(product, company) #> # A tibble: 1 × 3 #>   companies_id product          company          #>          <dbl> <list>           <list>           #> 1            1 <tibble [1 × 1]> <tibble [1 × 1]>"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate percent noise — percent_noise","title":"Calculate percent noise — percent_noise","text":"Calculate percent noise","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate percent noise — percent_noise","text":"","code":"percent_noise(x, noisy)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate percent noise — percent_noise","text":"x Numeric vector. noisy Numeric vector.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate percent noise — percent_noise","text":"Numeric vector.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/percent_noise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate percent noise — percent_noise","text":"","code":"withr::local_seed(123)  x <- -10:10 noisy <- jitter(x) out <- percent_noise(x, noisy) out #>  [1]  -1.4287999  -0.3797941  -0.4313784  -0.7494546  -2.3170350  -2.8895515 #>  [7]  -2.6696590  -0.4538340  -4.6805472 -14.3131086         Inf   2.3119970 #> [13]   5.9784969   5.0413432   0.6094798   2.3477489   2.4831223   1.4474735 #> [19]   1.9752268   0.5579432   0.6604608  finite <- out[is.finite(out)]  barplot(finite)   mean(finite) #> [1] -0.3449935"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rename.html","id":null,"dir":"Reference","previous_headings":"","what":"Renamed functions and datasets — rename","title":"Renamed functions and datasets — rename","text":"Conceptual changes: PCTR becomes Emissions Profile. ICTR becomes Emissions Profile Upstream. PSTR becomes Sector Profile. ISTR becomes Sector Profile Upstream. Motivation: names informative easier remember. indicators now share one common word (profile) instead two (transition risk). word \"upstream\" familiar users banks \"inputs\". word \"emissions\" replaces \"carbon\" data use actually take account CO2 equivalents, .e. green house gasses. Compared phrase \"transition risk\", word \"profile\" better reflects indicators used risk assessment also things, broader sustainability assessment, engagement, reporting, etc. Implementation changes: v0.0.0.9084: pstr() -> sector_profile() v0.0.0.9085: istr() -> sector_profile_upstream() v0.0.0.9086: xctr(companies, products) -> emissions_profile() v0.0.0.9087: xctr(companies, inputs) -> emissions_profile_upstream() v0.0.0.9089: datasets names match functions moved tiltToyData v0.0.0.9092: xstr_pivot_type_sector_subsector() -> sector_profile_any_pivot_type_sector_subsector() v0.0.0.9092: xstr_prepare_scenario() -> sector_profile_any_prepare_scenario() v0.0.0.9092: xstr_prune_companies() -> sector_profile_any_prune_companies() v0.0.0.9092: xstr_polish_output_at_company_level() -> sector_profile_any_polish_output_at_company_level()","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rename.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renamed functions and datasets — rename","text":"","code":"pstr(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  istr(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )  xctr(companies, co2, low_threshold = 1/3, high_threshold = 2/3)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.default.html","id":null,"dir":"Reference","previous_headings":"","what":"Method — rowid.default","title":"Method — rowid.default","text":"Method","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.default.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Method — rowid.default","text":"","code":"# S3 method for default rowid()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic — rowid","title":"Generic — rowid","text":"Generic","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/rowid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic — rowid","text":"","code":"rowid()"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"sector profile\" measures transition risk products based sector's emissions targets product belongs . sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). assessing product, products category aggregated set relation products company. , therefore, derive company-level information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"sector_profile(   companies,   scenarios,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, scenarios dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium reduction targets. high_threshold numeric value segment medium high reduction targets.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":null,"dir":"Reference","previous_headings":"","what":"Restructure ","title":"Restructure ","text":"Restructure \"sector profile\" companies","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restructure ","text":"","code":"sector_profile_any_pivot_type_sector_subsector(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restructure ","text":"data dataframe columns: ipr_sector ipr_subsector weo_product weo_flow","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Restructure ","text":"companies dataset required sector functions.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_pivot_type_sector_subsector.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restructure ","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE)  raw_companies <- example_raw_companies() glimpse(raw_companies) #> Rows: 2 #> Columns: 5 #> $ companies_id  <chr> \"a\", \"b\" #> $ ipr_sector    <chr> \"Industry\", \"Industry\" #> $ ipr_subsector <chr> \"Iron and Steel\", \"Chemicals\" #> $ weo_sector    <chr> \"Total\", \"Total\" #> $ weo_subsector <chr> \"Iron and steel\", \"Chemicals\"  companies <- sector_profile_any_pivot_type_sector_subsector(raw_companies) companies #> # A tibble: 4 × 4 #>   companies_id type  sector   subsector      #>   <chr>        <chr> <chr>    <chr>          #> 1 a            ipr   industry iron and steel #> 2 a            weo   total    iron and steel #> 3 b            ipr   industry chemicals      #> 4 b            weo   total    chemicals"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Polish output at company level — sector_profile_any_polish_output_at_company_level","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"Polish output company level","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"","code":"sector_profile_any_polish_output_at_company_level(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"data output sector_profile*() functions.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_polish_output_at_company_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polish output at company level — sector_profile_any_polish_output_at_company_level","text":"","code":"library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  sector_profile(companies, scenarios) |>   unnest_company() |>   sector_profile_any_polish_output_at_company_level() #> # A tibble: 588 × 6 #>    companies_id                         type  scenario year  risk_category value #>    <chr>                                <chr> <chr>    <chr> <chr>         <dbl> #>  1 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  high              1 #>  2 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  medium            0 #>  3 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2030  low               0 #>  4 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  high              1 #>  5 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  medium            0 #>  6 fleischerei-stiefsohn_0000000521947… ipr   1.5c rps 2050  low               0 #>  7 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  high              0 #>  8 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  medium            0 #>  9 fleischerei-stiefsohn_0000000521947… weo   announc… 2020  low               1 #> 10 fleischerei-stiefsohn_0000000521947… weo   announc… 2030  high              0 #> # ℹ 578 more rows  companies_upstream <- read_csv(toy_sector_profile_upstream_companies()) inputs <- read_csv(toy_sector_profile_upstream_products())  sector_profile_upstream(companies_upstream, scenarios, inputs) |>   unnest_company() |>   sector_profile_any_polish_output_at_company_level() #> # A tibble: 294 × 6 #>    companies_id                        type  scenario year  risk_category  value #>    <chr>                               <chr> <chr>    <chr> <chr>          <dbl> #>  1 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  high          0.333  #>  2 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  medium        0.583  #>  3 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2030  low           0.0833 #>  4 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  high          1      #>  5 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  medium        0      #>  6 fleischerei-stiefsohn_000000052194… ipr   1.5c rps 2050  low           0      #>  7 fleischerei-stiefsohn_000000052194… weo   announc… 2020  high          0      #>  8 fleischerei-stiefsohn_000000052194… weo   announc… 2020  medium        0      #>  9 fleischerei-stiefsohn_000000052194… weo   announc… 2020  low           1      #> 10 fleischerei-stiefsohn_000000052194… weo   announc… 2030  high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":null,"dir":"Reference","previous_headings":"","what":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"Given named list scenarios returns cleaner scenarios dataframe","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"","code":"sector_profile_any_prepare_scenario(scenarios)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"scenarios named list identically structured scenarios.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"single, cleaner dataframe additional column identify rows come scenario.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prepare_scenario.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Given a named list of scenarios returns a cleaner scenarios dataframe — sector_profile_any_prepare_scenario","text":"","code":"library(dplyr, warn.conflicts = FALSE) library(readr, warn.conflicts = FALSE)  raw_weo <- example_raw_weo() raw_ipr <- example_raw_ipr() raw_scenarios <- list(weo = raw_weo, ipr = raw_ipr)  sector_profile_any_prepare_scenario(raw_scenarios) #> # A tibble: 4 × 6 #>   scenario                   sector subsector              year reductions type  #>   <chr>                      <chr>  <chr>                 <dbl>      <dbl> <chr> #> 1 stated policies scenario   total  biofuels production …  2020       0    weo   #> 2 announced pledges scenario total  biofuels production …  2020       0    weo   #> 3 1.5c rps                   power  NA                     2030       0.58 ipr   #> 4 1.5c rps                   power  NA                     2050       1.06 ipr"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":null,"dir":"Reference","previous_headings":"","what":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"company, function drops rows product information missing sector information duplicated.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"","code":"sector_profile_any_prune_companies(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"data Typically \"sector profile\" *companies dataframe.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"dataframe maybe fewer rows input data.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_any_prune_companies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Drop rows where the product info is NA & sector info is duplicated — sector_profile_any_prune_companies","text":"","code":"library(dplyr) # styler: off companies <- tribble(   ~row, ~companies_id, ~clustered, ~activity_uuid_product_uuid, ~tilt_sector,     1L,           \"a\",       \"b1\",                        \"c1\",          \"x\",     2L,           \"a\",         NA,                          NA,          \"x\",     3L,           \"a\",         NA,                          NA,          \"y\",     4L,           \"a\",         NA,                          NA,          \"y\"   ) # styler: off  # Keep row 1: Has product info # Drop row 2: Lacks product info and sector info is duplicated # Keep row 3: Lacks product info but sector info is unique # Drop row 4: Lacks product info and sector info is duplicated companies #> # A tibble: 4 × 5 #>     row companies_id clustered activity_uuid_product_uuid tilt_sector #>   <int> <chr>        <chr>     <chr>                      <chr>       #> 1     1 a            b1        c1                         x           #> 2     2 a            NA        NA                         x           #> 3     3 a            NA        NA                         y           #> 4     4 a            NA        NA                         y            sector_profile_any_prune_companies(companies) #> # A tibble: 2 × 5 #>     row companies_id clustered activity_uuid_product_uuid tilt_sector #>   <int> <chr>        <chr>     <chr>                      <chr>       #> 1     1 a            b1        c1                         x           #> 2     3 a            NA        NA                         y"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the indicator ","title":"Calculate the indicator ","text":"indicator \"sector profile upstream\" assesses transition risk input products based sector's emissions targets input product belongs . indicator can aggregated company level inform supply chain risk SME, based inputs' transition risk. sector emission reduction targets vary across scenarios (e.g., net zero scenario 1.5° scenario) time horizon (e.g., reduction needed 2030, 2040, 2050 achieve targets). , therefore, similar Product Sector Risk Indicator focuses input products company needs produce products.input products , example, resources, packaging materials, energy enabling services (tractor use farm) produce product. identifying carbon footprint one input product, input products ranked according footprint. ranking method explained Thresholds section. assessing input products product, aggregated company-level derive percentage input products required company produce products high, medium low sector transition risk. , therefore, derive company-level information. Please note carbon emissions emissions always mean CO2e.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the indicator ","text":"","code":"sector_profile_upstream(   companies,   scenarios,   inputs,   low_threshold = ifelse(scenarios$year == 2030, 1/9, 1/3),   high_threshold = ifelse(scenarios$year == 2030, 2/9, 2/3) )"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the indicator ","text":"companies, scenarios, inputs dataframe like dataset matching name tiltToyData (see Reference). low_threshold numeric value segment low medium transition risk products. high_threshold numeric value segment medium high transition risk products.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the indicator ","text":"data frame column companies_id, nested columnsproduct company holding outputs product company level. Unnesting product yields data frame least columns companies_id, grouped_by, risk_category. Unnesting company yields data frame least columns companies_id, grouped_by, risk_category, value. column input datasets ending *rowid also passed output product level. exception column named exactly rowid-- reserved name throws error. Note feature makes sense company level potentially multiple rows input datasets summarized single row output company level.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/sector_profile_upstream.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the indicator ","text":"","code":"library(tiltIndicator) library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_upstream_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios()) inputs <- read_csv(toy_sector_profile_upstream_products())  both <- sector_profile_upstream(companies, scenarios, inputs) both #> # A tibble: 7 × 3 #>   companies_id                             product             company           #>   <chr>                                    <list>              <list>            #> 1 fleischerei-stiefsohn_00000005219477-001 <tibble [180 × 12]> <tibble [42 × 3]> #> 2 pecheries-basques_fra316541-00101        <tibble [14 × 12]>  <tibble [42 × 3]> #> 3 hoche-butter-gmbh_deu422723-693847001    <tibble [70 × 12]>  <tibble [42 × 3]> #> 4 vicquelin-espaces-verts_fra697272-00101  <tibble [70 × 12]>  <tibble [42 × 3]> #> 5 bst-procontrol-gmbh_00000005104947-001   <tibble [70 × 12]>  <tibble [42 × 3]> #> 6 leider-gmbh_00000005064318-001           <tibble [150 × 12]> <tibble [42 × 3]> #> 7 cheries-baqu_neu316541-00101             <tibble [150 × 12]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 704 × 13 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… weo_state… low                     0     stove     #>  2 fleischerei-stiefsohn_000… weo_state… low                    -0.192 stove     #>  3 fleischerei-stiefsohn_000… weo_state… low                    -0.517 stove     #>  4 fleischerei-stiefsohn_000… weo_state… low                    -0.689 stove     #>  5 fleischerei-stiefsohn_000… weo_annou… low                     0     stove     #>  6 fleischerei-stiefsohn_000… weo_annou… high                    0.301 stove     #>  7 fleischerei-stiefsohn_000… weo_annou… high                    1.83  stove     #>  8 fleischerei-stiefsohn_000… weo_annou… high                    3.17  stove     #>  9 fleischerei-stiefsohn_000… weo_net z… low                     0     stove     #> 10 fleischerei-stiefsohn_000… weo_net z… high                    0.909 stove     #> # ℹ 694 more rows #> # ℹ 8 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, #> #   input_activity_uuid_product_uuid <chr>, input_tilt_sector <chr>, #> #   input_tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 294 × 4 #>    companies_id                             grouped_by      risk_category  value #>    <chr>                                    <chr>           <chr>          <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          0.333  #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0.583  #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0.0833 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… high          1      #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… medium        0      #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_2… low           0      #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0      #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced … medium        0      #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced … low           1      #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced … high          0.0769 #> # ℹ 284 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize the range of a column by groups — summarize_range","title":"Summarize the range of a column by groups — summarize_range","text":"function shortcut dplyr::summarize(data, min = min(x), max = max(x)).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize the range of a column by groups — summarize_range","text":"","code":"summarize_range(data, col, .by = NULL)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize the range of a column by groups — summarize_range","text":"data dataframe. col Unquoted expression giving name column data. . <tidy-select> Optionally, selection columns group just operation, functioning alternative group_by(). details examples, see ?dplyr_by.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize the range of a column by groups — summarize_range","text":"dataframe: rows come underlying groups. columns come grouping keys plus new columns min max. groups dropped.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/summarize_range.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize the range of a column by groups — summarize_range","text":"","code":"library(tibble)  data <- tibble(x = 1:4, group = c(1, 1, 2, 2)) data #> # A tibble: 4 × 2 #>       x group #>   <int> <dbl> #> 1     1     1 #> 2     2     1 #> 3     3     2 #> 4     4     2  summarize_range(data, x, .by = group) #> # A tibble: 2 × 3 #>   group   min   max #>   <dbl> <int> <int> #> 1     1     1     2 #> 2     2     3     4"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tidyeval.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy eval helpers — tidyeval","title":"Tidy eval helpers — tidyeval","text":"page lists tidy eval tools reexported package rlang. learn using tidy eval scripts packages high level, see dplyr programming vignette ggplot2 packages vignette. Metaprogramming section Advanced R may also useful deeper dive. tidy eval operators {{, !!, !!! syntactic constructs specially interpreted tidy eval functions. mostly need {{, !! !!! advanced operators use simple cases. curly-curly operator {{ allows tunnel data-variables passed function arguments inside tidy eval functions. {{ designed individual arguments. pass multiple arguments contained dots, use ... normal way.   enquo() enquos() delay execution one several function arguments. former returns single expression, latter returns list expressions. defused, expressions longer evaluate . must injected back evaluation context !! (single expression) !!! (list expressions).   simple case, code equivalent usage {{ ... . Defusing enquo() enquos() needed complex cases, instance need inspect modify expressions way. .data pronoun object represents current slice data. variable name string, use .data pronoun subset variable [[.   Another tidy eval operator :=. makes possible use glue curly-curly syntax LHS =. technical reasons, R language support complex expressions left =, use := workaround.   Many tidy eval functions like dplyr::mutate() dplyr::summarise() give automatic name unnamed inputs. need create sort automatic names , use as_label(). instance, glue-tunnelling syntax can reproduced manually :   Expressions defused enquo() (tunnelled {{) need simple column names, can arbitrarily complex. as_label() handles cases gracefully. code assumes simple column name, use as_name() instead. safer throws error input name expected.","code":"my_function <- function(data, var, ...) {   data %>%     group_by(...) %>%     summarise(mean = mean({{ var }})) } my_function <- function(data, var, ...) {   # Defuse   var <- enquo(var)   dots <- enquos(...)    # Inject   data %>%     group_by(!!!dots) %>%     summarise(mean = mean(!!var)) } my_var <- \"disp\" mtcars %>% summarise(mean = mean(.data[[my_var]])) my_function <- function(data, var, suffix = \"foo\") {   # Use `{{` to tunnel function arguments and the usual glue   # operator `{` to interpolate plain strings.   data %>%     summarise(\"{{ var }}_mean_{suffix}\" := mean({{ var }})) } my_function <- function(data, var, suffix = \"foo\") {   var <- enquo(var)   prefix <- as_label(var)   data %>%     summarise(\"{prefix}_mean_{suffix}\" := mean(!!var)) }"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tiltIndicator-package.html","id":null,"dir":"Reference","previous_headings":"","what":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","title":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","text":"Indicators 'TILT' project.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/tiltIndicator-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"tiltIndicator: Indicators for the 'TILT' Project — tiltIndicator-package","text":"Maintainer: Mauro Lepore maurolepore@gmail.com (ORCID) Authors: Tilman Trompke tilman@2degrees-investing.org Linda Delacombaz linda@2degrees-investing.org Kalash Singhal kalash@2degrees-investing.org Lyanne Ho lyho@deloitte.nl contributors: 2 Degrees Investing Initiative contact@2degrees-investing.org [copyright holder, funder]","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":null,"dir":"Reference","previous_headings":"","what":"Unnest product- and company-level results — unnest_product","title":"Unnest product- and company-level results — unnest_product","text":"Unnest product- company-level results","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unnest product- and company-level results — unnest_product","text":"","code":"unnest_product(data)  unnest_company(data)"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unnest product- and company-level results — unnest_product","text":"data nested data frame, e.g. output sector_profile().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unnest product- and company-level results — unnest_product","text":"data frame.","code":""},{"path":[]},{"path":"https://2degreesinvesting.github.io/tiltIndicator/reference/unnest_product.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unnest product- and company-level results — unnest_product","text":"","code":"library(tiltToyData) library(readr) options(readr.show_col_types = FALSE)  companies <- read_csv(toy_sector_profile_companies()) scenarios <- read_csv(toy_sector_profile_any_scenarios())  both <- sector_profile(companies, scenarios) both #> # A tibble: 14 × 3 #>    companies_id                             product            company           #>    <chr>                                    <list>             <list>            #>  1 fleischerei-stiefsohn_00000005219477-001 <tibble [14 × 10]> <tibble [42 × 3]> #>  2 pecheries-basques_fra316541-00101        <tibble [14 × 10]> <tibble [42 × 3]> #>  3 hoche-butter-gmbh_deu422723-693847001    <tibble [14 × 10]> <tibble [42 × 3]> #>  4 hoche-butter-gmbh_deu422723-693847002    <tibble [14 × 10]> <tibble [42 × 3]> #>  5 hoche-butter-gmbh_deu422723-693847003    <tibble [14 × 10]> <tibble [42 × 3]> #>  6 vicquelin-espaces-verts_fra697272-00101  <tibble [14 × 10]> <tibble [42 × 3]> #>  7 vicquelin-espaces-verts_fra697272-00102  <tibble [14 × 10]> <tibble [42 × 3]> #>  8 vicquelin-espaces-verts_fra697272-00103  <tibble [14 × 10]> <tibble [42 × 3]> #>  9 fleisohn_0000000492-001                  <tibble [14 × 10]> <tibble [42 × 3]> #> 10 bst-procontrol-gmbh_00000005104947-001   <tibble [14 × 10]> <tibble [42 × 3]> #> 11 leider-gmbh_00000005064318-001           <tibble [14 × 10]> <tibble [42 × 3]> #> 12 leider-gmbh_00000005064318-002           <tibble [14 × 10]> <tibble [42 × 3]> #> 13 cheries-baqu_neu316541-00101             <tibble [14 × 10]> <tibble [42 × 3]> #> 14 ca-coity-trg-aua-gmbh_00000384-001       <tibble [14 × 10]> <tibble [42 × 3]>  both |> unnest_product() #> # A tibble: 196 × 11 #>    companies_id               grouped_by risk_category profile_ranking clustered #>    <chr>                      <chr>      <chr>                   <dbl> <chr>     #>  1 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.23   steel     #>  2 fleischerei-stiefsohn_000… ipr_1.5c … high                   0.96   steel     #>  3 fleischerei-stiefsohn_000… weo_state… low                    0      steel     #>  4 fleischerei-stiefsohn_000… weo_annou… low                    0      steel     #>  5 fleischerei-stiefsohn_000… weo_net z… low                    0      steel     #>  6 fleischerei-stiefsohn_000… weo_state… low                   -0.0752 steel     #>  7 fleischerei-stiefsohn_000… weo_annou… low                    0.0781 steel     #>  8 fleischerei-stiefsohn_000… weo_net z… high                   0.233  steel     #>  9 fleischerei-stiefsohn_000… weo_state… low                   -0.0270 steel     #> 10 fleischerei-stiefsohn_000… weo_annou… medium                 0.336  steel     #> # ℹ 186 more rows #> # ℹ 6 more variables: activity_uuid_product_uuid <chr>, tilt_sector <chr>, #> #   scenario <chr>, year <dbl>, type <chr>, tilt_subsector <chr>  both |> unnest_company() #> # A tibble: 588 × 4 #>    companies_id                             grouped_by       risk_category value #>    <chr>                                    <chr>            <chr>         <dbl> #>  1 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  2 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  3 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  4 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… high              1 #>  5 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… medium            0 #>  6 fleischerei-stiefsohn_00000005219477-001 ipr_1.5c rps_20… low               0 #>  7 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #>  8 fleischerei-stiefsohn_00000005219477-001 weo_announced p… medium            0 #>  9 fleischerei-stiefsohn_00000005219477-001 weo_announced p… low               1 #> 10 fleischerei-stiefsohn_00000005219477-001 weo_announced p… high              0 #> # ℹ 578 more rows"},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009109","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9109","title":"tiltIndicator 0.0.0.9109","text":"emissions_profile_any_compute_profile_ranking() now deprecated. function now internal (#669).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009108","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9108","title":"tiltIndicator 0.0.0.9108","text":"emissions_profile_any_compute_profile_ranking() now handles special cases (#644 @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009107","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9107","title":"tiltIndicator 0.0.0.9107","text":"column *isic_4digit can now values length (#630 @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009106","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9106","title":"tiltIndicator 0.0.0.9106","text":"percent deviation caused jitter*() now even (#627).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009105","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9105","title":"tiltIndicator 0.0.0.9105","text":"New helpers summarize_range() jitter_range() (#622, @AnneSchoenauer).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009104","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9104","title":"tiltIndicator 0.0.0.9104","text":"functions now use companies_id still accept company_id warning (#621).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009102","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9102","title":"tiltIndicator 0.0.0.9102","text":"functions product level now output new column profile_ranking (#613, @AnneSchoenauer).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009101","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9101","title":"tiltIndicator 0.0.0.9101","text":"results product level, clustered longer NA risk_category NA (#614, @AnneSchoenauer, @kalashsinghal).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009100","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9100","title":"tiltIndicator 0.0.0.9100","text":"Get started now shows sector_profile*() documentation (#619).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009099","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9099","title":"tiltIndicator 0.0.0.9099","text":"Rename emissions_profile_any_add_values_to_categorize emissions_profile_any_compute_profile_ranking (#609). old name retired without deprecation since users.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009098","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9098","title":"tiltIndicator 0.0.0.9098","text":"emissions_profile_any_add_values_to_categorize() now relocates new columns left.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009097","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9097","title":"tiltIndicator 0.0.0.9097","text":"emissions_profile*() uses co2$values_to_categorize present (#605).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009096","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9096","title":"tiltIndicator 0.0.0.9096","text":"New pre-processing helper emissions_profile_any_add_values_to_categorize() (#602).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009095","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9095","title":"tiltIndicator 0.0.0.9095","text":"values grouped_by now less surprising (see related principle) (#601). now simply refer full name corresponding columns “co2” dataset (products inputs) passed emissions_profile*() functions. example, passing column products$tilt_sector now yields value “tilt_sector” group_by – got cropped “tilt_sec”.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009094","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9094","title":"tiltIndicator 0.0.0.9094","text":"name rowid now reserved. input dataset uses , result error. *rowid column now must unique. Duplicated names now result error.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009093","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9093","title":"tiltIndicator 0.0.0.9093","text":"profile functions allow passing *rowid columns input tables output product level (#511).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009092","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9092","title":"tiltIndicator 0.0.0.9092","text":"Rename pre- post-processing helpers (#503).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009091","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9091","title":"tiltIndicator 0.0.0.9091","text":"Rename indicators public documentation (#496).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009090","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9090","title":"tiltIndicator 0.0.0.9090","text":"emissions_profile*() now handles numeric values *isic_4digit (#494).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009089","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9089","title":"tiltIndicator 0.0.0.9089","text":"Deprecate datasets. moved tiltToyData new names (#493).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009088","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9088","title":"tiltIndicator 0.0.0.9088","text":"*_at_product_level() *_at_company_level() now deprecated (#491).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009087","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9087","title":"tiltIndicator 0.0.0.9087","text":"xctr(data, inputs) now deprecated favor new emissions_profile_upstream() (#481).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009086","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9086","title":"tiltIndicator 0.0.0.9086","text":"xctr(data, products) now deprecated favor new emissions_profile() (#481).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009085","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9085","title":"tiltIndicator 0.0.0.9085","text":"istr() now deprecated favor new sector_profile_upstream() (#480).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009084","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9084","title":"tiltIndicator 0.0.0.9084","text":"pstr() now deprecated favor new sector_profile() (#479).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009083","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9083","title":"tiltIndicator 0.0.0.9083","text":"Results company level now preserve unmatched companies (#466).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009082","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9082","title":"tiltIndicator 0.0.0.9082","text":"article “Handling long runtime” now shows enhanced approach (#450).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009081","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9081","title":"tiltIndicator 0.0.0.9081","text":"pstr() now warns companies semicolon ‘;’ sector subsector (#449).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009080","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9080","title":"tiltIndicator 0.0.0.9080","text":"products, activity_uuid_product_uuid now unique (#447).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009079","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9079","title":"tiltIndicator 0.0.0.9079","text":"xctr() now outputs results levels (#443).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009078","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9078","title":"tiltIndicator 0.0.0.9078","text":"istr() now outputs results levels (#442).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009077","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9077","title":"tiltIndicator 0.0.0.9077","text":"pstr() now outputs results levels (#441). New unnest_product() unnest_company() help get results product company levels nested outputs like one pstr().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009076","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9076","title":"tiltIndicator 0.0.0.9076","text":"Ensure outputs duplicate (#438)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009075","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9075","title":"tiltIndicator 0.0.0.9075","text":"XSTR longer errors duplicated scenarios (#437).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009074","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9074","title":"tiltIndicator 0.0.0.9074","text":"indicators product level, company match outputs NA, match outputs 1 row NAs columns (except companeis_id (#436).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009073","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9073","title":"tiltIndicator 0.0.0.9073","text":"indicators company level, company match 3 values sum 1 NA level grouped_by, company match 1 value NA total (#434).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009072","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9072","title":"tiltIndicator 0.0.0.9072","text":"xstr_prepare_scenario() duplicated scenario data now throws error (#431). avoids running indicators corrupt input data alerts preparation must fixed.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009071","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9071","title":"tiltIndicator 0.0.0.9071","text":"istr() now sensitive low_threshold high_threshold (#420).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009070","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9070","title":"tiltIndicator 0.0.0.9070","text":"New xstr_scenarios replaces istr_scenarios pstr_scenarios (@kalashsinghal #413).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009069","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9069","title":"tiltIndicator 0.0.0.9069","text":"pstr_prepare_scenario() now named xstr_prepare_scenario() (#385).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009068","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9068","title":"tiltIndicator 0.0.0.9068","text":"pstr_polish_output_at_copmany_level() now named xstr_polish_output_at_copmany_level() (#383).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009067","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9067","title":"tiltIndicator 0.0.0.9067","text":"xstr_prune_companies() now expects column company_id (#380).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009066","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9066","title":"tiltIndicator 0.0.0.9066","text":"New helper xstr_prune_companies() drop rows product info ‘NA’ & sector info duplicated (#379).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009065","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9065","title":"tiltIndicator 0.0.0.9065","text":"istr_inputs now includes columns required output (@kalashsinghal #376).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009064","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9064","title":"tiltIndicator 0.0.0.9064","text":"XSTR NAs reductions longer error handled specially (@lindadelacombaz #350). ISTR sample data code now use new structure (@kalashsinghal #353). ISTR default thresholds now XCTR (@lindadelacombaz #348).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009063","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9063","title":"tiltIndicator 0.0.0.9063","text":"XXTR functions now stop companies 0-rows (#340).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009062","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9062","title":"tiltIndicator 0.0.0.9062","text":"XSTR XCTR functions now stop scenarios co2 0-row (#337, #338).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009061","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9061","title":"tiltIndicator 0.0.0.9061","text":"PSTR default thresholds now XCTR (@lindadelacombaz #329).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009060","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9060","title":"tiltIndicator 0.0.0.9060","text":"New pstr_polish_output_at_company_level() (#327)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009059","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9059","title":"tiltIndicator 0.0.0.9059","text":"article “Handling long runtime” now updated based experience running pstr*() (#314).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009058","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9058","title":"tiltIndicator 0.0.0.9058","text":"scenario type must sector subsector, else error (#311).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009057","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9057","title":"tiltIndicator 0.0.0.9057","text":"pstr_prepare_scenario() now handles “weo” data correctly (@kalashsinghal #309).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009056","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9056","title":"tiltIndicator 0.0.0.9056","text":"type “ipr”, company grouped_by, value sums 1 (#307).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009055","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9055","title":"tiltIndicator 0.0.0.9055","text":"column grouped_by now includes scenario type (#306). makes column grouped_by contain information scenarios, year, type – meaning columns removed keep output simpler, without loosing information.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009054","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9054","title":"tiltIndicator 0.0.0.9054","text":"pstr_at_product_level() now output columns google sheet template (#303). company_id + grouped_by now gets one low, medium & high risk_category (#278).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009053","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9053","title":"tiltIndicator 0.0.0.9053","text":"ISTR old argument scenario now named scenarios, consistently PSTR (#299).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009052","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9052","title":"tiltIndicator 0.0.0.9052","text":"xstr*(), NAs reductions now trigger error (#298).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009051","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9051","title":"tiltIndicator 0.0.0.9051","text":"PSTR example datasets now updated (@kalashsinghal #287).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009050","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9050","title":"tiltIndicator 0.0.0.9050","text":"company & benchmark now gets unique risk_category (@Tilmon #286).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009049","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9049","title":"tiltIndicator 0.0.0.9049","text":"New article handling long runtime (#283) pstr*() gain arguments low_threshold high_threshold (@kalashsinghal #273). pstr*() values now expressed proportion (@lindadelacombaz #274).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009048","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9048","title":"tiltIndicator 0.0.0.9048","text":"xctr_at_product_level() now drops NAs unmatched products (#267).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009047","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9047","title":"tiltIndicator 0.0.0.9047","text":"ictr*() pctr*() now retired (#264).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009046","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9046","title":"tiltIndicator 0.0.0.9046","text":"data now simpler: ictr_companies pctr_companies now retired. Instead use new dataset companies. pctr_ecoinvent_co2 now renamed products. ictr_inputs now renamed inputs.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009045","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9045","title":"tiltIndicator 0.0.0.9045","text":"New xctr*() replace ictr*() pctr*() (#256). functions ictr*() pctr*() internal backward compatibility retired soon.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009044","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9044","title":"tiltIndicator 0.0.0.9044","text":"ICTR PCTR product level longer output needless columns (#251).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009043","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9043","title":"tiltIndicator 0.0.0.9043","text":"ICTR PCTR argument low_threshold now default 1/3 high_threshold 2/3 (#249).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009042","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9042","title":"tiltIndicator 0.0.0.9042","text":"company 3 different products varying footprints now gets correct value (@Tilmon #248).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009041","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9041","title":"tiltIndicator 0.0.0.9041","text":"ICTR PCTR now handle duplicated co2 data (#230).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009040","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9040","title":"tiltIndicator 0.0.0.9040","text":"*ctr_at_product_level() now outputs clustered *_uuid (#242).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009039","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9039","title":"tiltIndicator 0.0.0.9039","text":"ICTR PCTR example datasets now updated (@kalashsinghal #237). pctr_at_product_level() now returns visibly (#239). ICTR PCTR now handle duplicated companies data (#230). ICTR & PCTR ranking benchmarks now updated (@kalashsinghal #229). ICTR & PCTR high_threshold now computed correctly (@kalashsinghal #229).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009037","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9037","title":"tiltIndicator 0.0.0.9037","text":"product-level functions now output three columns (#228, #227): companies_id grouped_by risk_category","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009036","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9036","title":"tiltIndicator 0.0.0.9036","text":"Datasets family now documented together (#224).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009035","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9035","title":"tiltIndicator 0.0.0.9035","text":"company-level functions now output four columns: companies_id grouped_by risk_category value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009034","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9034","title":"tiltIndicator 0.0.0.9034","text":"ICTR PCTR example datasets now updated (@kalashsinghal #217).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009033","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9033","title":"tiltIndicator 0.0.0.9033","text":"top level functions now output first four columns (#214): companies_id grouped_by risk_category value","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009032","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9032","title":"tiltIndicator 0.0.0.9032","text":"ictr_at_product_level() pctr_at_product_level() now output company data (#213).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009031","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9031","title":"tiltIndicator 0.0.0.9031","text":"PSTR use new data (@lindadelacombaz #196).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009030","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9030","title":"tiltIndicator 0.0.0.9030","text":"ictr() pctr() first argument now named co2. New internal-ish functions xctr family (#207).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009029","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9029","title":"tiltIndicator 0.0.0.9029","text":"PCTR, company matches input, shares now NA (#205).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009028","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9028","title":"tiltIndicator 0.0.0.9028","text":"ICTR, company matches input, shares now NA (@kalashsinghal #202).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009027","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9027","title":"tiltIndicator 0.0.0.9027","text":"Even companies *uuid absent inputs/co2, shares now sum 1 (@kalashsinghal #197).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009026","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9026","title":"tiltIndicator 0.0.0.9026","text":"indicators now output ungrouped data (#193)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009025","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9025","title":"tiltIndicator 0.0.0.9025","text":"indicators now export single similar top-level interface (#189). old functions still available now considered developer-oriented therefore visible website. output also similar: first column always id, second column always transition_risk, following column(s) provide score(s). indicators now output id company score (#190).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009024","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9024","title":"tiltIndicator 0.0.0.9024","text":"ICTR example data now reflects real data closely (@kalashsinghal #170).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009023","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9023","title":"tiltIndicator 0.0.0.9023","text":"ictr_score_companies() now errors inputs_co2 NAs (@kalashsinghal #150). New article Get started (#152).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009022","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9022","title":"tiltIndicator 0.0.0.9022","text":"Add istr mvp ( @Lyanneho #144).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009021","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9021","title":"tiltIndicator 0.0.0.9021","text":"BREAKING CHANGES ictr_inputs ictr_companies loose non-crucial columns(@kalashsingal #117). pctr_ecoinvent_co2 pctr_companies loose non-crucial columns (#116). BUG FIXES ictr_score_companies() ictr_score_companies() now return three rows per company regardless number rows co2 data (#122). pctr_score_companies() ictr_score_companies() now return three rows per company (@kalashsinghal #111).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009018","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9018","title":"tiltIndicator 0.0.0.9018","text":"Document PCTR functions (@kalashsinghal, #104). New ICTR functions datasets (@kalashsinghal, #90).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009017","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9017","title":"tiltIndicator 0.0.0.9017","text":"Add ICTR MVP (#90).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009016","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9016","title":"tiltIndicator 0.0.0.9016","text":"un-prefixed pstr datasets now retired (#79).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009014","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9014","title":"tiltIndicator 0.0.0.9014","text":"Remove pstr_plot_company() (#84). name PSTR datasets now include prefix “pstr_” (#74). developer-oriented functions now internal (#67). Remove internal article (#66).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009013","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9013","title":"tiltIndicator 0.0.0.9013","text":"New pctr_*() family functions datasets (#60, #61).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009012","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9012","title":"tiltIndicator 0.0.0.9012","text":"Add pctr (@Tilmon, #56)","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009011","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9011","title":"tiltIndicator 0.0.0.9011","text":"FIX: article pstr now shows expected content (#52). FIX: mvp_path() nonexistent path now throws error (#51).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009010","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9010","title":"tiltIndicator 0.0.0.9010","text":"Document pstr_*() functions (@lindadelacombaz, #50) dev: Rename helper render_to_list() (#43). dev: Prune needless helpers (#42). dev: Rename data-files related pstr (#41). dev: Fix CODEOWNERS.","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009009","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9009","title":"tiltIndicator 0.0.0.9009","text":"dev: Rename data-files related pstr ’s easier express Linda’s ownership (#41).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009008","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9008","title":"tiltIndicator 0.0.0.9008","text":"dev: Use CODEOWNERS (#39).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009007","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9007","title":"tiltIndicator 0.0.0.9007","text":"pstr_at_company_level() fix missing argument companies (#38). dev: Address R CMD Check undefined global variables (#37).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009006","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9006","title":"tiltIndicator 0.0.0.9006","text":"dev: Use new argument left_join().","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009004","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9004","title":"tiltIndicator 0.0.0.9004","text":"dev: Address dplyr warnings (#32).","code":""},{"path":"https://2degreesinvesting.github.io/tiltIndicator/news/index.html","id":"tiltindicator-0009003","dir":"Changelog","previous_headings":"","what":"tiltIndicator 0.0.0.9003","title":"tiltIndicator 0.0.0.9003","text":"New pstr_*() family product sector transition risk. New pstr_*() family PSTR functions. New article “Product sector transition risk” (@lindadelacombaz, #18, #22).","code":""}]