Upkeep spring 2025 (#6440)

* update last upkeep field

* bump required R version

* replace `%>%` with `|>`

* Convert some simple anonymous functions to shorthand

* add ROR

* bump licence year

* tidy description

* tidy GHA

* document

* exempt `cut3()` from short function form

* redocument

Author: teunbrand

.github/workflows/test-coverage.yaml

@@ -35,14 +35,16 @@ jobs:
             clean = FALSE,
             install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
           )
+          print(cov)
           covr::to_cobertura(cov)
         shell: Rscript {0}
 
-      - uses: codecov/codecov-action@v4
+      - uses: codecov/codecov-action@v5
         with:
-          fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
-          file: ./cobertura.xml
-          plugin: noop
+          # Fail if error if not on PR, or if on PR and token is given
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }}
+          files: ./cobertura.xml
+          plugins: noop
           disable_search: true
           token: ${{ secrets.CODECOV_TOKEN }}
 

DESCRIPTION

@@ -20,7 +20,8 @@ Authors@R: c(
            comment = c(ORCID = "0000-0002-9415-4582")),
     person("Teun", "van den Brand", role = "aut",
            comment = c(ORCID = "0000-0002-9335-7468")),
-    person("Posit, PBC", role = c("cph", "fnd"))
+    person("Posit, PBC", role = c("cph", "fnd"),
+           comment = c(ROR = "03wc8by49"))
   )
 Description: A system for 'declaratively' creating graphics, based on "The
     Grammar of Graphics". You provide the data, tell 'ggplot2' how to map
@@ -30,7 +31,7 @@ License: MIT + file LICENSE
 URL: https://ggplot2.tidyverse.org, https://github.com/tidyverse/ggplot2
 BugReports: https://github.com/tidyverse/ggplot2/issues
 Depends:
-    R (>= 4.0)
+    R (>= 4.1)
 Imports:
     cli,
     grDevices,
@@ -77,7 +78,7 @@ VignetteBuilder:
     knitr
 Config/Needs/website: ggtext, tidyr, forcats, tidyverse/tidytemplate
 Config/testthat/edition: 3
-Config/usethis/last-upkeep: 2024-10-24
+Config/usethis/last-upkeep: 2025-04-23
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2024
+YEAR: 2025
 COPYRIGHT HOLDER: ggplot2 core developer team

LICENSE.md

@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2024 ggplot2 core developer team
+Copyright (c) 2025 ggplot2 core developer team
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

R/annotation-logticks.R

@@ -43,11 +43,11 @@
 #' a <- ggplot(msleep, aes(bodywt, brainwt)) +
 #'  geom_point(na.rm = TRUE) +
 #'  scale_x_log10(
-#'    breaks = scales::trans_breaks("log10", function(x) 10^x),
+#'    breaks = scales::trans_breaks("log10", \(x) 10^x),
 #'    labels = scales::trans_format("log10", scales::math_format(10^.x))
 #'  ) +
 #'  scale_y_log10(
-#'    breaks = scales::trans_breaks("log10", function(x) 10^x),
+#'    breaks = scales::trans_breaks("log10", \(x) 10^x),
 #'    labels = scales::trans_format("log10", scales::math_format(10^.x))
 #'  ) +
 #'  theme_bw()

R/geom-function.R

@@ -65,11 +65,9 @@ GeomFunction <- ggproto("GeomFunction", GeomPath,
 #'   geom_function(aes(colour = "t, df = 1"), fun = dt, args = list(df = 1))
 #'
 #' # Using a custom anonymous function
-#' base + geom_function(fun = function(x) 0.5 * exp(-abs(x)))
+#' base + geom_function(fun = \(x) 0.5 * exp(-abs(x)))
 #' # or using lambda syntax:
 #' # base + geom_function(fun = ~ 0.5 * exp(-abs(.x)))
-#' # or in R4.1.0 and above:
-#' # base + geom_function(fun = \(x) 0.5 * exp(-abs(x)))
 #' # or using a custom named function:
 #' # f <- function(x) 0.5 * exp(-abs(x))
 #' # base + geom_function(fun = f)

R/geom-histogram.R

@@ -132,7 +132,7 @@
 #' # different ranges because the function will be called once per facet
 #' ggplot(economics_long, aes(value)) +
 #'   facet_wrap(~variable, scales = 'free_x') +
-#'   geom_histogram(binwidth = function(x) 2 * IQR(x) / (length(x)^(1/3)))
+#'   geom_histogram(binwidth = \(x) 2 * IQR(x) / (length(x)^(1/3)))
 geom_histogram <- make_constructor(
   GeomBar, stat = "bin", position = "stack",
   # Passed to bin stat:

R/geom-map.R

@@ -55,7 +55,7 @@ NULL
 #'
 #'   crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
 #'
-#'   # Equivalent to crimes %>% tidyr::pivot_longer(Murder:Rape)
+#'   # Equivalent to crimes |> tidyr::pivot_longer(Murder:Rape)
 #'   vars <- lapply(names(crimes)[-1], function(j) {
 #'     data.frame(state = crimes$state, variable = j, value = crimes[[j]])
 #'   })

R/labeller.R

@@ -85,7 +85,7 @@
 #' p + facet_grid(. ~ cyl2, labeller = label_parsed)
 #'
 #' # Include optional argument in label function
-#' p + facet_grid(. ~ cyl, labeller = function(x) label_both(x, sep = "="))
+#' p + facet_grid(. ~ cyl, labeller = \(x) label_both(x, sep = "="))
 #' }
 #' @name labellers
 NULL

R/stat-summary-2d.R

@@ -99,7 +99,7 @@ StatSummary2d <- ggproto(
 #' d + stat_summary_2d()
 #'
 #' # Specifying function
-#' d + stat_summary_2d(fun = function(x) sum(x^2))
+#' d + stat_summary_2d(fun = \(x) sum(x^2))
 #' d + stat_summary_2d(fun = ~ sum(.x^2))
 #' d + stat_summary_2d(fun = var)
 #' d + stat_summary_2d(fun = "quantile", fun.args = list(probs = 0.1))

R/utilities-tidy-eval.R

@@ -23,8 +23,8 @@
 #'
 #'   ```
 #'   my_function <- function(data, var, ...) {
-#'     data %>%
-#'       group_by(...) %>%
+#'     data |>
+#'       group_by(...) |>
 #'       summarise(mean = mean({{ var }}))
 #'   }
 #'   ```
@@ -43,8 +43,8 @@
 #'     dots <- enquos(...)
 #'
 #'     # Inject
-#'     data %>%
-#'       group_by(!!!dots) %>%
+#'     data |>
+#'       group_by(!!!dots) |>
 #'       summarise(mean = mean(!!var))
 #'   }
 #'   ```
@@ -60,7 +60,7 @@
 #'
 #'   ```
 #'   my_var <- "disp"
-#'   mtcars %>% summarise(mean = mean(.data[[my_var]]))
+#'   mtcars |> summarise(mean = mean(.data[[my_var]]))
 #'   ```
 #'
 #' * Another tidy eval operator is `:=`. It makes it possible to use
@@ -72,7 +72,7 @@
 #'   my_function <- function(data, var, suffix = "foo") {
 #'     # Use `{{` to tunnel function arguments and the usual glue
 #'     # operator `{` to interpolate plain strings.
-#'     data %>%
+#'     data |>
 #'       summarise("{{ var }}_mean_{suffix}" := mean({{ var }}))
 #'   }
 #'   ```
@@ -87,7 +87,7 @@
 #'   my_function <- function(data, var, suffix = "foo") {
 #'     var <- enquo(var)
 #'     prefix <- as_label(var)
-#'     data %>%
+#'     data |>
 #'       summarise("{prefix}_mean_{suffix}" := mean(!!var))
 #'   }
 #'   ```

man/annotation_logticks.Rd

@@ -230,7 +230,7 @@ test_that("facet variables", {
 test_that("facet gives clear error if ", {
   df <- data_frame(x = 1)
   expect_snapshot_error(print(ggplot(df, aes(x)) + facet_grid(x ~ x)))
-  expect_snapshot_error(print(ggplot(df, aes(x)) %>% facet_grid(. ~ x)))
+  expect_snapshot_error(print(ggplot(df, aes(x)) |> facet_grid(. ~ x)))
   expect_snapshot_error(print(ggplot(df, aes(x)) + facet_grid(list(1, 2, 3))))
   expect_snapshot_error(print(ggplot(df, aes(x)) + facet_grid(vars(x), "free")))
 })

man/geom_function.Rd

@@ -73,7 +73,7 @@ test_that("fuzzy breaks are used when cutting", {
   p <- ggplot(df, aes(x)) +
     geom_histogram(binwidth = 0.1, boundary = 0.1, closed = "left")
 
-  bins <- get_layer_data(p) %>% subset(count > 0) %>% .[1:5]
+  bins <- get_layer_data(p) |> subset(count > 0) |> head(5)
   expect_equal(bins$count, c(1, 1, 1, 1))
 })
 

man/geom_histogram.Rd

@@ -142,8 +142,8 @@ Note that we expanded the y axis limit to get the numbers to fit on the plot.
 ```{r}
 #| fig.alt: "A bar chart showing the number of cars for each of three types
 #|  of drive train. The count values are displayed on top of the bars as text."
-mpg %>%
-  dplyr::count(drv) %>%
+mpg |>
+  dplyr::count(drv) |>
   ggplot(aes(x = drv, y = n)) +
   geom_col() +
   geom_text(aes(label = n), vjust = -0.5) +
@@ -184,7 +184,7 @@ ggplot(mpg, aes(x = class, fill = drv)) +
 You can first calculate the counts for each segment with `dplyr::count()`, which will place these values in a column called `n`.
 
 ```{r}
-mpg %>%
+mpg |>
   count(class, drv) 
 ```
 
@@ -195,8 +195,8 @@ You can then pass this result directly to `ggplot()`, draw the segments with app
 #|  types of cars. The fill colour of the bars indicate the type of drive 
 #|  train. In the middle of each filled part, the count value is displayed as 
 #|  text."
-mpg %>%
-  count(class, drv) %>%
+mpg |>
+  count(class, drv) |>
   ggplot(aes(x = class, fill = drv, y = n)) +
   geom_col() +
   geom_text(aes(label = n), size = 3, position = position_stack(vjust = 0.5))
@@ -226,9 +226,9 @@ One option is to calculate the proportions with `dplyr::count()` and then use `g
 ```{r}
 #| fig.alt: "A bar chart showing the proportion of cars for each of three types
 #|  of drive train."
-mpg %>%
-  dplyr::count(drv) %>%
-  mutate(prop = n / sum(n)) %>%
+mpg |>
+  dplyr::count(drv) |>
+  mutate(prop = n / sum(n)) |>
   ggplot(aes(x = drv, y = prop)) +
   geom_col()
 ```

man/geom_map.Rd

@@ -258,7 +258,7 @@ poll <- tribble(
 You can first pivot the data longer to obtain a data frame with one row per party/opinion combination and a new column, `n`, for the number of responses that fall into that category.
 
 ```{r}
-poll_longer <- poll %>%
+poll_longer <- poll |>
   pivot_longer(
     cols = -party,
     names_to = "opinion",
@@ -318,12 +318,12 @@ Then, pass the resulting longer data frame to `ggplot()` group responses for eac
 #| fig.alt: "A grouped bar chart showing the number of responses to three 
 #|  questions. Within each question, two bars denote an 'Agree' or 'Disagree' 
 #|  response."
-survey %>%
+survey |>
   tidyr::pivot_longer(
     cols = -respondent,
     names_to = "question",
     values_to = "response"
-  ) %>%
+  ) |>
   ggplot(aes(x = question, fill = response)) +
   geom_bar(position = "dodge")
 ```
@@ -344,9 +344,9 @@ Then, you can pass the resulting data frame to `ggplot()` and plot bars using `g
 ```{r}
 #| fig.alt: "A bar chart showing the average highway miles per gallon for 
 #|  three types of drive train."
-mpg %>%
-  group_by(drv) %>%
-  summarise(mean_hwy = mean(hwy)) %>%
+mpg |>
+  group_by(drv) |>
+  summarise(mean_hwy = mean(hwy)) |>
   ggplot(aes(x = drv, y = mean_hwy)) +
   geom_col()
 ```