Change benchmark from tilt_sector and input_tilt_sector to `tilt_…

…subsector` and `input_tilt_subsector` respectively (#799) * change benchmark from tilt_sector to tilt_subsector * Revert-example_dictionary --------- Co-authored-by: Mauro Lepore <[email protected]>
2DegreesInvesting · Jun 26, 2024 · 1bc57dd · 1bc57dd
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diff --git a/R/compute_profile_ranking.R b/R/compute_profile_ranking.R
@@ -23,7 +23,7 @@ epa_compute_profile_ranking <- function(data) {
 
   exclude <- short_isic(data) |
     is.na(get_column(data, aka("isic"))) |
-    is.na(get_column(data, aka("tsector"))) |
+    is.na(get_column(data, aka("tsubsector"))) |
     is.na(get_column(data, aka("xunit")))
 
   list(!exclude, exclude) |>
@@ -41,7 +41,7 @@ epa_compute_profile_ranking_impl <- function(data) {
 }
 
 check_epa_compute_profile_ranking <- function(data) {
-  crucial <- c(aka("tsector"), aka("xunit"), aka("isic"), aka("co2footprint"))
+  crucial <- c(aka("tsubsector"), aka("xunit"), aka("isic"), aka("co2footprint"))
   walk(crucial, \(pattern) check_matches_name(data, pattern))
 }
 
@@ -53,10 +53,10 @@ epa_benchmarks <- function(data) {
   list(
     "all",
     extract_name(data, aka("isic")),
-    extract_name(data, aka("tsector")),
+    extract_name(data, aka("tsubsector")),
     extract_name(data, aka("xunit")),
     c(extract_name(data, aka("xunit")), extract_name(data, aka("isic"))),
-    c(extract_name(data, aka("xunit")), extract_name(data, aka("tsector")))
+    c(extract_name(data, aka("xunit")), extract_name(data, aka("tsubsector")))
   )
 }
 
@@ -78,7 +78,7 @@ assign_na_to_profile_ranking_in_special_cases <- function(data) {
     mutate(profile_ranking = case_when(
       data |> should_be_na_when_isic_has_2_or_3_digits() ~ NA,
       data |> should_be_na_when_missing(aka("isic")) ~ NA,
-      data |> should_be_na_when_missing(aka("tsector")) ~ NA,
+      data |> should_be_na_when_missing(aka("tsubsector")) ~ NA,
       data |> should_be_na_when_missing(aka("xunit")) ~ NA,
       .default = .data$profile_ranking
     ))

diff --git a/R/emissions_profile_any_at_product_level.R b/R/emissions_profile_any_at_product_level.R
@@ -25,7 +25,7 @@ epa_check <- function(x) {
   crucial <- id_pattern()
   check_matches_name(x$companies, crucial)
 
-  crucial <- c(aka("co2footprint"), aka("tsector"), aka("isic"), aka("xunit"))
+  crucial <- c(aka("co2footprint"), aka("tsubsector"), aka("isic"), aka("xunit"))
   walk(crucial, ~ check_matches_name(x$co2, .x))
 
   check_has_no_na(x$co2, find_co2_footprint(x$co2))

diff --git a/man/emissions_profile.Rd b/man/emissions_profile.Rd
diff --git a/tests/testthat/_snaps/emissions_profile.md b/tests/testthat/_snaps/emissions_profile.md
@@ -13,13 +13,13 @@
       6 antimonarchy_canine
       
       [[2]]
-              grouped_by
-      1              all
-      2      isic_4digit
-      3      tilt_sector
-      4             unit
-      5 unit_isic_4digit
-      6 unit_tilt_sector
+                 grouped_by
+      1                 all
+      2         isic_4digit
+      3      tilt_subsector
+      4                unit
+      5    unit_isic_4digit
+      6 unit_tilt_subsector
       
       [[3]]
         risk_category
@@ -100,31 +100,31 @@
       24 antimonarchy_canine
       
       [[2]]
-               grouped_by
-      1               all
-      2               all
-      3               all
-      4               all
-      5       isic_4digit
-      6       isic_4digit
-      7       isic_4digit
-      8       isic_4digit
-      9       tilt_sector
-      10      tilt_sector
-      11      tilt_sector
-      12      tilt_sector
-      13             unit
-      14             unit
-      15             unit
-      16             unit
-      17 unit_isic_4digit
-      18 unit_isic_4digit
-      19 unit_isic_4digit
-      20 unit_isic_4digit
-      21 unit_tilt_sector
-      22 unit_tilt_sector
-      23 unit_tilt_sector
-      24 unit_tilt_sector
+                  grouped_by
+      1                  all
+      2                  all
+      3                  all
+      4                  all
+      5          isic_4digit
+      6          isic_4digit
+      7          isic_4digit
+      8          isic_4digit
+      9       tilt_subsector
+      10      tilt_subsector
+      11      tilt_subsector
+      12      tilt_subsector
+      13                unit
+      14                unit
+      15                unit
+      16                unit
+      17    unit_isic_4digit
+      18    unit_isic_4digit
+      19    unit_isic_4digit
+      20    unit_isic_4digit
+      21 unit_tilt_subsector
+      22 unit_tilt_subsector
+      23 unit_tilt_subsector
+      24 unit_tilt_subsector
       
       [[3]]
          risk_category
@@ -204,22 +204,22 @@
       no_missing_benchmark
     Output
       # A tibble: 16 x 4
-         companies_id grouped_by       risk_category value
-         <chr>        <chr>            <chr>         <dbl>
-       1 a            all              high            0.4
-       2 a            all              medium          0.2
-       3 a            all              low             0.2
-       4 a            all              <NA>            0.2
-       5 a            tilt_sector      high            0.4
-       6 a            tilt_sector      medium          0.2
-       7 a            tilt_sector      low             0.2
-       8 a            tilt_sector      <NA>            0.2
-       9 a            unit             high            0.4
-      10 a            unit             medium          0.2
-      11 a            unit             low             0.2
-      12 a            unit             <NA>            0.2
-      13 a            unit_tilt_sector high            0.4
-      14 a            unit_tilt_sector medium          0.2
-      15 a            unit_tilt_sector low             0.2
-      16 a            unit_tilt_sector <NA>            0.2
+         companies_id grouped_by          risk_category value
+         <chr>        <chr>               <chr>         <dbl>
+       1 a            all                 high            0.4
+       2 a            all                 medium          0.2
+       3 a            all                 low             0.2
+       4 a            all                 <NA>            0.2
+       5 a            tilt_subsector      high            0.4
+       6 a            tilt_subsector      medium          0.2
+       7 a            tilt_subsector      low             0.2
+       8 a            tilt_subsector      <NA>            0.2
+       9 a            unit                high            0.4
+      10 a            unit                medium          0.2
+      11 a            unit                low             0.2
+      12 a            unit                <NA>            0.2
+      13 a            unit_tilt_subsector high            0.4
+      14 a            unit_tilt_subsector medium          0.2
+      15 a            unit_tilt_subsector low             0.2
+      16 a            unit_tilt_subsector <NA>            0.2
 
diff --git a/tests/testthat/_snaps/emissions_profile_upstream.md b/tests/testthat/_snaps/emissions_profile_upstream.md
@@ -13,13 +13,13 @@
       6 antimonarchy_canine
       
       [[2]]
-                          grouped_by
-      1                          all
-      2            input_isic_4digit
-      3            input_tilt_sector
-      4                   input_unit
-      5 input_unit_input_isic_4digit
-      6 input_unit_input_tilt_sector
+                             grouped_by
+      1                             all
+      2               input_isic_4digit
+      3            input_tilt_subsector
+      4                      input_unit
+      5    input_unit_input_isic_4digit
+      6 input_unit_input_tilt_subsector
       
       [[3]]
         risk_category
@@ -109,31 +109,31 @@
       24 antimonarchy_canine
       
       [[2]]
-                           grouped_by
-      1                           all
-      2                           all
-      3                           all
-      4                           all
-      5             input_isic_4digit
-      6             input_isic_4digit
-      7             input_isic_4digit
-      8             input_isic_4digit
-      9             input_tilt_sector
-      10            input_tilt_sector
-      11            input_tilt_sector
-      12            input_tilt_sector
-      13                   input_unit
-      14                   input_unit
-      15                   input_unit
-      16                   input_unit
-      17 input_unit_input_isic_4digit
-      18 input_unit_input_isic_4digit
-      19 input_unit_input_isic_4digit
-      20 input_unit_input_isic_4digit
-      21 input_unit_input_tilt_sector
-      22 input_unit_input_tilt_sector
-      23 input_unit_input_tilt_sector
-      24 input_unit_input_tilt_sector
+                              grouped_by
+      1                              all
+      2                              all
+      3                              all
+      4                              all
+      5                input_isic_4digit
+      6                input_isic_4digit
+      7                input_isic_4digit
+      8                input_isic_4digit
+      9             input_tilt_subsector
+      10            input_tilt_subsector
+      11            input_tilt_subsector
+      12            input_tilt_subsector
+      13                      input_unit
+      14                      input_unit
+      15                      input_unit
+      16                      input_unit
+      17    input_unit_input_isic_4digit
+      18    input_unit_input_isic_4digit
+      19    input_unit_input_isic_4digit
+      20    input_unit_input_isic_4digit
+      21 input_unit_input_tilt_subsector
+      22 input_unit_input_tilt_subsector
+      23 input_unit_input_tilt_subsector
+      24 input_unit_input_tilt_subsector
       
       [[3]]
          risk_category
@@ -213,22 +213,22 @@
       no_missing_benchmark
     Output
       # A tibble: 16 x 4
-         companies_id grouped_by                   risk_category value
-         <chr>        <chr>                        <chr>         <dbl>
-       1 a            all                          high            0.4
-       2 a            all                          medium          0.2
-       3 a            all                          low             0.2
-       4 a            all                          <NA>            0.2
-       5 a            input_tilt_sector            high            0.4
-       6 a            input_tilt_sector            medium          0.2
-       7 a            input_tilt_sector            low             0.2
-       8 a            input_tilt_sector            <NA>            0.2
-       9 a            input_unit                   high            0.4
-      10 a            input_unit                   medium          0.2
-      11 a            input_unit                   low             0.2
-      12 a            input_unit                   <NA>            0.2
-      13 a            input_unit_input_tilt_sector high            0.4
-      14 a            input_unit_input_tilt_sector medium          0.2
-      15 a            input_unit_input_tilt_sector low             0.2
-      16 a            input_unit_input_tilt_sector <NA>            0.2
+         companies_id grouped_by                      risk_category value
+         <chr>        <chr>                           <chr>         <dbl>
+       1 a            all                             high            0.4
+       2 a            all                             medium          0.2
+       3 a            all                             low             0.2
+       4 a            all                             <NA>            0.2
+       5 a            input_tilt_subsector            high            0.4
+       6 a            input_tilt_subsector            medium          0.2
+       7 a            input_tilt_subsector            low             0.2
+       8 a            input_tilt_subsector            <NA>            0.2
+       9 a            input_unit                      high            0.4
+      10 a            input_unit                      medium          0.2
+      11 a            input_unit                      low             0.2
+      12 a            input_unit                      <NA>            0.2
+      13 a            input_unit_input_tilt_subsector high            0.4
+      14 a            input_unit_input_tilt_subsector medium          0.2
+      15 a            input_unit_input_tilt_subsector low             0.2
+      16 a            input_unit_input_tilt_subsector <NA>            0.2
 
diff --git a/tests/testthat/test-emissions_profile.R b/tests/testthat/test-emissions_profile.R
@@ -57,7 +57,7 @@ test_that("at product level, `NA` in a benchmark yields `NA` in `risk_category`
   expect_true(is.na(out$risk_category))
   expect_true(is.na(out$profile_ranking))
 
-  benchmark <- "tilt_sector"
+  benchmark <- "tilt_subsector"
   co2 <- example_products("{ benchmark }" := NA)
   out <- emissions_profile(companies, co2) |>
     unnest_product() |>
@@ -94,7 +94,7 @@ test_that("at product level, `NA` in a benchmark yields `NA`s only in the corres
   expect_false(is.na(filter(out, clustered == "a")$risk_category))
   expect_true(is.na(filter(out, clustered == "b")$risk_category))
 
-  benchmark <- "tilt_sector"
+  benchmark <- "tilt_subsector"
   co2 <- example_products(
     !!aka("uid") := c("a", "b"),
     "{ benchmark }" := c("a", NA)
@@ -215,7 +215,7 @@ test_that("at company level, `NA` in a benchmark yields `value = 1` where `group
     pull(sum) |>
     expect_equal(1)
 
-  benchmark <- "tilt_sector"
+  benchmark <- "tilt_subsector"
   co2 <- example_products("{ benchmark }" := c(NA))
   out <- emissions_profile(companies, co2) |> unnest_company()
   # For each benchmark `value` adds to 1

diff --git a/tests/testthat/test-emissions_profile_any_at_company_level.R b/tests/testthat/test-emissions_profile_any_at_company_level.R
@@ -91,15 +91,15 @@ test_that("for each company & benchmark, each risk category is unique (#285)", {
     "-fred-sl_00000005407085-741049001", "fish, deep-frozen", "26104519-4d49-5d85-bc74-e8e03d1a7914_cdbf0bef-39f7-46c8-87a2-3f9f679b5bb7",  "kg"
   )
   co2 <- tribble(
-                                                    ~activity_uuid_product_uuid,  ~unit,            ~tilt_sector, ~isic_4digit,   ~co2_footprint,
+                                                    ~activity_uuid_product_uuid,  ~unit,            ~tilt_subsector, ~isic_4digit,   ~co2_footprint,
     # In companies
-    "0fe31e67-346a-504c-a03d-64f85ccc2a64_a459eea1-4e62-4daf-9135-1aea9805aa90",   "kg",                      NA,       "0311", 2.83222756713596,
-    "26104519-4d49-5d85-bc74-e8e03d1a7914_cdbf0bef-39f7-46c8-87a2-3f9f679b5bb7",   "kg",                      NA,       "0311",  2.1156617059259,
+    "0fe31e67-346a-504c-a03d-64f85ccc2a64_a459eea1-4e62-4daf-9135-1aea9805aa90",   "kg",                         NA,       "0311", 2.83222756713596,
+    "26104519-4d49-5d85-bc74-e8e03d1a7914_cdbf0bef-39f7-46c8-87a2-3f9f679b5bb7",   "kg",                         NA,       "0311",  2.1156617059259,
     # Not in companies
-    "0faa7ecb-fef2-5117-8993-387c1898ffc8_c33b5236-001e-49b5-aa3d-810c0214f9ce",   "kg",      "Steel and Metals",       "2410", 4.94911765272901,
-    "9b414d69-2bd2-5b44-bd5d-56672896aac5_0f2ea065-f26c-4356-a261-39ef2799aea4", "unit", "Construction Industry",       "4322", 11266.1570789735,
-    "74c3b4f6-dc3d-5e13-badf-70b4c3a965d3_54186f39-acc2-4c84-95e7-fbb067bde4cd",   "ha",                      NA,       "0161", 51.6463779571345,
-    "72651603-406a-545d-a03d-1d1caf656efb_765e7edf-19bc-4110-bb7c-32df8d749c54",   "m3", "Non-metallic Minerals",       "2395", 424.269497499198
+    "0faa7ecb-fef2-5117-8993-387c1898ffc8_c33b5236-001e-49b5-aa3d-810c0214f9ce",   "kg",         "Steel and Metals",       "2410", 4.94911765272901,
+    "9b414d69-2bd2-5b44-bd5d-56672896aac5_0f2ea065-f26c-4356-a261-39ef2799aea4", "unit",    "Construction Industry",       "4322", 11266.1570789735,
+    "74c3b4f6-dc3d-5e13-badf-70b4c3a965d3_54186f39-acc2-4c84-95e7-fbb067bde4cd",   "ha",                         NA,       "0161", 51.6463779571345,
+    "72651603-406a-545d-a03d-1d1caf656efb_765e7edf-19bc-4110-bb7c-32df8d749c54",   "m3",    "Non-metallic Minerals",       "2395", 424.269497499198
   )
   # styler: on
 

diff --git a/tests/testthat/test-emissions_profile_any_at_product_level.R b/tests/testthat/test-emissions_profile_any_at_product_level.R
@@ -52,7 +52,7 @@ test_that("if `co2` lacks crucial columns, errors gracefully", {
   bad <- select(products, -ends_with(crucial))
   expect_error(emissions_profile_any_at_product_level(companies, bad), crucial)
 
-  crucial <- aka("tsector")
+  crucial <- aka("tsubsector")
   bad <- select(products, -ends_with(crucial))
   expect_error(emissions_profile_any_at_product_level(companies, bad), crucial)
 
@@ -163,7 +163,7 @@ test_that("if `inputs` lacks crucial columns, errors gracefully", {
   bad <- select(inputs, -ends_with(crucial))
   expect_error(emissions_profile_any_at_product_level(companies, bad), crucial)
 
-  crucial <- aka("tsector")
+  crucial <- aka("tsubsector")
   bad <- select(inputs, -ends_with(crucial))
   expect_error(emissions_profile_any_at_product_level(companies, bad), crucial)
 

diff --git a/tests/testthat/test-emissions_profile_upstream.R b/tests/testthat/test-emissions_profile_upstream.R
@@ -181,7 +181,7 @@ test_that("at product level, `NA` in a benchmark yields `NA`s only in the corres
   expect_false(is.na(filter(out, clustered == "a")$risk_category))
   expect_true(is.na(filter(out, clustered == "b")$risk_category))
 
-  benchmark <- "input_tilt_sector"
+  benchmark <- "input_tilt_subsector"
   co2 <- example_inputs(
     !!aka("uid") := c("a", "b"),
     "{ benchmark }" := c("a", NA)
@@ -301,7 +301,7 @@ test_that("at company level, `NA` in a benchmark yields `value = 1` where `group
     pull(sum) |>
     expect_equal(1)
 
-  benchmark <- "input_tilt_sector"
+  benchmark <- "input_tilt_subsector"
   co2 <- example_inputs("{ benchmark }" := c(NA))
   out <- emissions_profile_upstream(companies, co2) |> unnest_company()
   # For each benchmark `value` adds to 1