Group rows for tiltsector and tiltsubsector columns and change font t…

…o `Roboto Condensed` (#35)

* Group rows for tiltsector and tiltsubsector columns

* Change font to Roboto Condensed

NAMESPACE

@@ -15,6 +15,7 @@ export(example_transition_risk_product_sector_cov)
 export(example_transition_risk_product_transition_risk_cov)
 export(example_transition_risk_product_trs_des_analysis)
 export(show_reduction_targets_for_tilt_subsectors_per_grouping_sector)
+importFrom(dplyr,arrange)
 importFrom(dplyr,distinct)
 importFrom(dplyr,filter)
 importFrom(dplyr,mutate)

R/show_reduction_targets_for_tilt_subsectors_per_scenario_year.R

@@ -11,7 +11,7 @@
 #' @examples
 #' library(dplyr)
 #' sector_product_example <- product_sector |>
-#'   select(c("tilt_subsector", "scenario", "year", "reduction_targets")) |>
+#'   select(c("tilt_sector" ,"tilt_subsector", "scenario", "year", "reduction_targets")) |>
 #'   distinct()
 #' sector_product_example
 #'
@@ -20,7 +20,7 @@
 #' )
 show_reduction_targets_for_tilt_subsectors_per_grouping_sector <- function(sector_product) {
   sector_product |>
-    select(all_of(c("tilt_subsector", "scenario", "year", "reduction_targets"))) |>
+    select(all_of(c("tilt_sector", "tilt_subsector", "scenario", "year", "reduction_targets"))) |>
     distinct() |>
     mutate(grouping_sector = paste(.data$scenario, .data$year, sep = "_")) |>
     select(-c("scenario", "year")) |>
@@ -34,5 +34,6 @@ show_reduction_targets_for_tilt_subsectors_per_grouping_sector <- function(secto
       IPR_2050 = "1.5C RPS_2050",
       WEO_2030 = "NZ 2050_2030",
       WEO_2050 = "NZ 2050_2050",
-    )
+    ) |>
+    arrange(.data$tilt_sector, .data$tilt_subsector)
 }

R/tiltDataAnalysis-package.R

@@ -2,6 +2,7 @@
 "_PACKAGE"
 
 ## usethis namespace: start
+#' @importFrom dplyr arrange
 #' @importFrom dplyr distinct
 #' @importFrom dplyr filter
 #' @importFrom dplyr mutate

vignettes/articles/descriptive-analysis-amount_of_companies.Rmd

@@ -25,12 +25,12 @@ options(readr.show_col_types = FALSE)
 
 ```{r, echo=FALSE}
 emission_product_example <- product_emission |>
-  select(all_of(c("companies_id", "country", "emission_profile", "tilt_sector", 
+  select(all_of(c("companies_id", "country", "emission_profile", "tilt_sector",
                   "tilt_subsector", "main_activity"))) |>
   distinct() |>
-  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish", 
+  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish",
                                "heliophobic_clownanemonefish", companies_id)) |>
-  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama", 
+  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama",
                              "subdermal_chipmunk", "fascist_maiasaura",
                              "ironhearted_tarpan", "heliophobic_clownanemonefish",
                              "subzero_whiteeye"))
@@ -47,9 +47,9 @@ sector_product_example <- product_sector |>
   select(all_of(c("companies_id", "country", "sector_profile", "tilt_sector", 
                   "tilt_subsector", "main_activity"))) |>
   distinct() |>
-  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish", 
+  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish",
                                "heliophobic_clownanemonefish", companies_id)) |>
-  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama", 
+  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama",
                              "subdermal_chipmunk", "fascist_maiasaura",
                              "ironhearted_tarpan", "heliophobic_clownanemonefish",
                              "subzero_whiteeye"))
@@ -66,9 +66,9 @@ transition_risk_product_example <- product_transition_risk |>
   select(all_of(c("companies_id", "country", "transition_risk_category", "tilt_sector", 
                   "tilt_subsector", "main_activity"))) |>
   distinct() |>
-  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish", 
+  mutate(companies_id = ifelse(companies_id == "insecticidal_clownanemonefish",
                                "heliophobic_clownanemonefish", companies_id)) |>
-  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama", 
+  filter(companies_id %in% c("antimonarchy_canine", "nonphilosophical_llama",
                              "subdermal_chipmunk", "fascist_maiasaura",
                              "ironhearted_tarpan", "heliophobic_clownanemonefish",
                              "subzero_whiteeye"))
@@ -88,7 +88,7 @@ cat("Number of companies for transition risk profile:", n_distinct(transition_ri
 
 ### Number of companies that have atleast one product with `emission_profile`, with `sector_profile`, and with `transition_risk_category`
 
-```{r, echo=FALSE}
+```{r}
 companies_atleast_one_product_with_profile <- function(data, profile) {
   result <- data |>
     select(all_of(c("companies_id", profile))) |>
@@ -107,7 +107,7 @@ cat("Number of companies that have atleast one product with `transition_risk_cat
 
 ### Number of companies that have more than one tilt_sectors for emission, sector, and transition risk profile
 
-```{r, echo=FALSE}
+```{r}
 companies_with_multiple_tilt_sectors <- function(data) {
   result <- data |>
     select(all_of(c("companies_id", "tilt_sector"))) |>
@@ -127,7 +127,7 @@ cat("Number of companies that have more than one tilt_sectors for transition ris
 
 ### Number of companies that have more than one tilt_subsectors for emission, sector, and transition risk profile
 
-```{r, echo=FALSE}
+```{r}
 companies_with_multiple_tilt_subsectors <- function(data) {
   result <- data |>
     select(all_of(c("companies_id", "tilt_subsector"))) |>
@@ -147,7 +147,7 @@ cat("Number of companies that have more than one tilt_subsectors for transition
 
 ### Number of companies per tilt_sector for emission, sector, and transition risk profile
 
-```{r, echo=FALSE}
+```{r}
 companies_per_single_benchmark <- function(data, benchmark) {
   data |>
     select(all_of(c("companies_id", benchmark))) |>
@@ -201,7 +201,7 @@ kable(companies_per_single_benchmark(transition_risk_product_example, "country")
 
 ### Number of companies per country and tilt_sector emission, sector, and transition risk profile
 
-```{r, echo=FALSE}
+```{r}
 companies_per_double_benchmark <- function(data, benchmark_first, benchmark_second) {
   data |>
     select(all_of(c("companies_id", benchmark_first, benchmark_second))) |>

vignettes/articles/show-reduction_targets_for_tilt_subsectors_per_grouping_sector.Rmd

@@ -9,6 +9,14 @@ knitr::opts_chunk$set(
 )
 ```
 
+```{css, echo=FALSE}
+@import url('https://fonts.googleapis.com/css2?family=Roboto+Condensed:wght@400;700&display=swap');
+
+table {
+  font-family: 'Roboto Condensed', sans-serif;
+}
+```
+
 This article outlines how to show reduction targets for all `tilt_subsectors` per
 grouping_sector using product-level output of Sector profile indicator.
 
@@ -24,7 +32,7 @@ options(readr.show_col_types = FALSE)
 
 ```{r}
 sector_product_example <- product_sector |>
-  select(c("tilt_subsector", "scenario", "year", "reduction_targets")) |>
+  select(c("tilt_sector", "tilt_subsector", "scenario", "year", "reduction_targets")) |>
   distinct()
 
 kable(sector_product_example |> head(20))
@@ -37,8 +45,9 @@ result <- show_reduction_targets_for_tilt_subsectors_per_grouping_sector(
    sector_product_example
  )
 
-kable(result, align = "lcccc", caption = "Reduction targets for all `tilt_subsectors` per grouping_sector", escape = F) |>
-  kable_classic(full_width = F, html_font = "Cambria") |>
-  column_spec(1, width = "6cm") |>
-  column_spec(2:5, width = "2.5cm")
+kable(result, align = "llcccc", caption = "Reduction targets for all `tilt_subsectors` per grouping_sector", escape = F) |>
+  kable_classic(full_width = F, html_font = "Roboto Condensed") |>
+  column_spec(2, width = "6cm") |>
+  column_spec(3:6, width = "2.5cm") |>
+  collapse_rows(columns = 1, valign = "top")
 ```