Fix checks

R/plot_emission_intensity.R

@@ -31,9 +31,9 @@ plot_emission_intensity <- function(data,
                                     span_5yr = FALSE,
                                     convert_label = identity) {
   lifecycle::deprecate_soft(
-      when = "0.4.0",
-      what = "plot_emission_intensity(data = 'must be prepped already')",
-      details = api_warning_details("plot_emission_intensity")
+    when = "0.4.0",
+    what = "plot_emission_intensity(data = 'must be prepped already')",
+    details = api_warning_details("plot_emission_intensity")
   )
   env <- list(data = substitute(data))
   check_plot_emission_intensity(data, env = env)

R/plot_techmix.R

@@ -49,9 +49,9 @@ plot_techmix <- function(data,
                          convert_label = identity,
                          convert_tech_label = identity) {
   lifecycle::deprecate_soft(
-      when = "0.4.0",
-      what = "plot_techmix(data = 'must be prepped already')",
-      details = api_warning_details("plot_techmix")
+    when = "0.4.0",
+    what = "plot_techmix(data = 'must be prepped already')",
+    details = api_warning_details("plot_techmix")
   )
   env <- list(data = substitute(data))
   check_plot_techmix(data, env = env)

R/plot_trajectory.R

@@ -53,9 +53,9 @@ plot_trajectory <- function(data,
                             value_col = "percentage_of_initial_production_by_scope",
                             perc_y_scale = FALSE) {
   lifecycle::deprecate_soft(
-      when = "0.4.0",
-      what = "plot_trajectory(data = 'must be prepped already')",
-      details = api_warning_details("plot_trajectory")
+    when = "0.4.0",
+    what = "plot_trajectory(data = 'must be prepped already')",
+    details = api_warning_details("plot_trajectory")
   )
   env <- list(data = substitute(data))
   check_plot_trajectory(data, value_col = value_col, env = env)

R/utils.R

@@ -38,7 +38,7 @@ abort_if_has_zero_rows <- function(data, env) {
 }
 
 hint_if_missing_names <- function(expr, like) {
-  rlang::with_handlers(
+  withCallingHandlers(
     expr,
     missing_names = function(err) {
       abort(
@@ -81,11 +81,12 @@ abort_if_missing_names <- function(data, expected_names) {
 
   if (!all(unique(expected_names) %in% names(data))) {
     missing_names <- sort(setdiff(expected_names, names(data)))
-    abort(c(
-      "`data` must have all the expected names.",
-      x = glue("Missing names: {toString(missing_names)}.")
-    ),
-    class = "missing_names"
+    abort(
+      c(
+        "`data` must have all the expected names.",
+        x = glue("Missing names: {toString(missing_names)}.")
+      ),
+      class = "missing_names"
     )
   }
 

tests/testthat/_snaps/sda.md

@@ -4,18 +4,18 @@
       sda
     Output
       # A tibble: 110 x 6
-         sector  year region             scenario_source emission_factor_met~1 emiss~2
-         <chr>  <dbl> <chr>              <chr>           <chr>                   <dbl>
-       1 cement  2020 advanced economies demo_2020       projected              0.0230
-       2 cement  2020 developing asia    demo_2020       projected              0.0595
-       3 cement  2020 global             demo_2020       projected              0.664 
-       4 cement  2021 advanced economies demo_2020       projected              0.0232
-       5 cement  2021 developing asia    demo_2020       projected              0.0594
-       6 cement  2021 global             demo_2020       projected              0.665 
-       7 cement  2022 advanced economies demo_2020       projected              0.0233
-       8 cement  2022 developing asia    demo_2020       projected              0.0592
-       9 cement  2022 global             demo_2020       projected              0.666 
-      10 cement  2023 advanced economies demo_2020       projected              0.0235
-      # ... with 100 more rows, and abbreviated variable names
-      #   1: emission_factor_metric, 2: emission_factor_value
+         sector  year region             scenario_source emission_factor_metric
+         <chr>  <dbl> <chr>              <chr>           <chr>                 
+       1 cement  2020 advanced economies demo_2020       projected             
+       2 cement  2020 developing asia    demo_2020       projected             
+       3 cement  2020 global             demo_2020       projected             
+       4 cement  2021 advanced economies demo_2020       projected             
+       5 cement  2021 developing asia    demo_2020       projected             
+       6 cement  2021 global             demo_2020       projected             
+       7 cement  2022 advanced economies demo_2020       projected             
+       8 cement  2022 developing asia    demo_2020       projected             
+       9 cement  2022 global             demo_2020       projected             
+      10 cement  2023 advanced economies demo_2020       projected             
+      # i 100 more rows
+      # i 1 more variable: emission_factor_value <dbl>
 

tests/testthat/test-plot_emission_intensity.R

@@ -80,9 +80,10 @@ test_that("with n metrics in input outputs n lines", {
 
 options(warn = 0)
 
-test_that("throws expected warning about API change",{
+test_that("throws expected warning about API change", {
   data <- head(filter(sda, sector == "cement"))
   expect_snapshot_error(
-    plot_emission_intensity(data), class = "warning"
-    )
+    plot_emission_intensity(data),
+    class = "warning"
+  )
 })

tests/testthat/test-plot_techmix.R

@@ -292,8 +292,9 @@ test_that("with no scenario for start year of 'projected' doesn't plot scenario
 
 options(warn = 0)
 
-test_that("throws expected warning about API change",{
+test_that("throws expected warning about API change", {
   expect_snapshot_error(
-    plot_techmix(example_tech_mix()), class = "warning"
-    )
+    plot_techmix(example_tech_mix()),
+    class = "warning"
+  )
 })

tests/testthat/test-plot_trajectory.R

@@ -165,7 +165,7 @@ test_that("with 0 as extreme value plots areas correctly", {
       technology == "oilcap",
       region == "global",
       scenario_source == "demo_2020"
-    )  %>%
+    ) %>%
     mutate(
       percentage_of_initial_production_by_scope = if_else(
         percentage_of_initial_production_by_scope >= 0,
@@ -184,7 +184,7 @@ test_that("with 0 as extreme value plots areas correctly", {
       technology == "renewablescap",
       region == "global",
       scenario_source == "demo_2020"
-    )  %>%
+    ) %>%
     mutate(
       percentage_of_initial_production_by_scope = if_else(
         percentage_of_initial_production_by_scope <= 0,
@@ -200,8 +200,9 @@ test_that("with 0 as extreme value plots areas correctly", {
 
 options(warn = 0)
 
-test_that("throws expected warning about API change",{
+test_that("throws expected warning about API change", {
   expect_snapshot_error(
-    plot_trajectory(example_market_share()), class = "warning"
-    )
+    plot_trajectory(example_market_share()),
+    class = "warning"
+  )
 })

tests/testthat/test-qplot_emission_intensity.R

@@ -5,7 +5,7 @@ test_that("outputs pretty labels", {
   metrics <- sort(unique(p$data$label))
   pretty <- sort(
     c("Projected", "Corporate Economy", "Adjusted Scenario Demo", "Target Demo")
-    )
+  )
   expect_equal(metrics, pretty)
 })
 

vignettes/r2dii-plot.Rmd

@@ -95,8 +95,8 @@ Use `qplot_trajectory()` with `market_share`-like data.
 data <- matched %>%
   target_market_share(abcd, scenario = scenario_demo_2020, region_isos = region) %>%
   filter(
-    technology == "renewablescap", 
-    region == "global", 
+    technology == "renewablescap",
+    region == "global",
     scenario_source == "demo_2020"
   )
 
@@ -142,7 +142,7 @@ data <- matched %>%
   target_sda(abcd, co2_intensity_scenario = scenario, region_isos = region) %>%
   filter(
     sector == "cement",
-    region == "global", 
+    region == "global",
     scenario_source == "demo_2020"
   )
 
@@ -158,9 +158,9 @@ data <- matched %>%
   target_sda(abcd, co2_intensity_scenario = scenario, region_isos = region) %>%
   filter(
     sector == "cement",
-    region == "global", 
+    region == "global",
     scenario_source == "demo_2020"
-    )
+  )
 
 plot_emission_intensity(data, convert_label = to_title, span_5yr = TRUE) +
   labs(
@@ -196,7 +196,7 @@ colours and legend labels.
 data <- sda %>%
   filter(
     sector == "cement",
-    region == "global", 
+    region == "global",
     scenario_source == "demo_2020",
     year <= 2030
   )
@@ -243,7 +243,7 @@ plot_trajectory(data) +
     x = "Year",
     y = "Production normalized to start year"
   ) +
-  theme(plot.margin = unit(c(0.5, 6, 0.5, 1), "cm")) #so the long labels fit
+  theme(plot.margin = unit(c(0.5, 6, 0.5, 1), "cm")) # so the long labels fit
 ```
 
 * `plot_techmix()` - change time span, add 'label' column, apply custom colours