replace %>% with |>

Author: teunbrand

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/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/geom_map.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/tidyeval.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))
 })
 

tests/testthat/test-facet-.R

@@ -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()
 ```

tests/testthat/test-stat-bin.R

@@ -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()
 ```

vignettes/articles/faq-annotation.Rmd

@@ -121,8 +121,8 @@ First, calculate these means and save them in a new data frame.
 ```{r}
 library(dplyr)
 
-mpg_summary <- mpg %>%
-  group_by(drv) %>%
+mpg_summary <- mpg |>
+  group_by(drv) |>
   summarise(hwy_mean = mean(hwy))
 
 mpg_summary

vignettes/articles/faq-bars.Rmd

@@ -234,9 +234,9 @@ Now the blue circle is over the yellow triangle since 0.01 comes after 0 and sim
 #|  four x-positions, of which two are very distinct. Every point has a distinct 
 #|  shape and colour. A blue circle is plotted on top of a yellow triangle. A 
 #|  red square is plotted on top of a black asterisk."
-df_arranged <- df %>% dplyr::arrange(x)
+df_arranged <- df |> dplyr::arrange(x)
 
-df_arranged %>%
+df_arranged |>
   ggplot(aes(x = x, y = y, fill = fill, shape = shape)) +
   geom_point(size = 8) +
   scale_shape_identity() +
@@ -257,14 +257,14 @@ If you wanted to make sure that the observation identified with an asterisk is a
 #|  asterisk is plotted on top of a red square."
 
 ggplot(mapping = aes(x = x, y = y, fill = fill, shape = shape)) +
-  geom_point(data = df %>% filter(shape != "asterisk"), size = 8) +
-  geom_point(data = df %>% filter(shape == "asterisk"), size = 8) +
+  geom_point(data = df |> filter(shape != "asterisk"), size = 8) +
+  geom_point(data = df |> filter(shape == "asterisk"), size = 8) +
   scale_shape_identity() +
   scale_fill_identity()
 
 ggplot(mapping = aes(x = x, y = y, fill = fill, shape = shape)) +
-  geom_point(data = df_arranged %>% filter(shape != "asterisk"), size = 8) +
-  geom_point(data = df_arranged %>% filter(shape == "asterisk"), size = 8) +
+  geom_point(data = df_arranged |> filter(shape != "asterisk"), size = 8) +
+  geom_point(data = df_arranged |> filter(shape == "asterisk"), size = 8) +
   scale_shape_identity() +
   scale_fill_identity()
 ```