Merge branch 'main' into label_dictionary

Author: teunbrand

NEWS.md

@@ -2,6 +2,8 @@
 
 * New argument `labs(dictionary)` to label based on variable name rather than 
   based on aesthetic (@teunbrand, #5178)
+* The `summary()` method for ggplots is now more terse about facets 
+  (@teunbrand, #5989).
 * `guide_bins()`, `guide_colourbar()` and `guide_coloursteps()` gain an `angle`
   argument to overrule theme settings, similar to `guide_axis(angle)` 
   (@teunbrand, #4594).

R/geom-ribbon.R

@@ -98,7 +98,7 @@ geom_ribbon <- function(mapping = NULL, data = NULL,
 GeomRibbon <- ggproto("GeomRibbon", Geom,
   default_aes = aes(
     colour = NA,
-    fill = from_theme(col_mix(ink, paper, 0.799)),
+    fill = from_theme(col_mix(ink, paper, 0.2)),
     linewidth = from_theme(borderwidth),
     linetype = from_theme(bordertype),
     alpha = NA),

R/geom-text.R

@@ -31,8 +31,8 @@
 #'
 #' @section Alignment:
 #' You can modify text alignment with the `vjust` and `hjust`
-#' aesthetics. These can either be a number between 0 (right/bottom) and
-#' 1 (top/left) or a character (`"left"`, `"middle"`, `"right"`, `"bottom"`,
+#' aesthetics. These can either be a number between 0 (left/bottom) and
+#' 1 (right/top) or a character (`"left"`, `"middle"`, `"right"`, `"bottom"`,
 #' `"center"`, `"top"`). There are two special alignments: `"inward"` and
 #' `"outward"`. Inward always aligns text towards the center, and outward
 #' aligns it away from the center.

R/summary.R

@@ -29,8 +29,9 @@ summary.ggplot <- function(object, ...) {
     cat("scales:  ", paste(object$scales$input(), collapse = ", "), "\n")
   }
 
-  cat("faceting: ")
-  print(object$facet)
+  vars <- object$facet$vars()
+  vars <- if (length(vars) > 0) paste0("~", vars) else "<empty>"
+  cat("faceting: ", paste0(vars, collapse = ", "), "\n")
 
   if (length(object$layers) > 0)
     cat("-----------------------------------\n")

README.Rmd

@@ -47,7 +47,7 @@ pak::pak("tidyverse/ggplot2")
 It's hard to succinctly describe how ggplot2 works because it embodies a deep philosophy of visualisation. However, in most cases you start with `ggplot()`, supply a dataset and aesthetic mapping (with `aes()`). You then add on layers (like `geom_point()` or `geom_histogram()`), scales (like `scale_colour_brewer()`), faceting specifications (like `facet_wrap()`) and coordinate systems (like `coord_flip()`).
 
 ```{r example}
-#| fig.alt = "Scatterplot of engine displacement versus highway miles per
+#| fig.alt: "Scatterplot of engine displacement versus highway miles per
 #|  gallon, for 234 cars coloured by 7 'types' of car. The displacement and miles
 #|  per gallon are inversely correlated."
 library(ggplot2)

man/geom_text.Rd

@@ -54,3 +54,19 @@
 
     Can't recycle `..1` (size 4) to match `..2` (size 0).
 
+# summary method gives a nice summary
+
+    Code
+      summary(p)
+    Output
+      data: manufacturer, model, displ, year, cyl, trans, drv, cty, hwy, fl,
+        class [234x11]
+      mapping:  x = ~displ, y = ~hwy, colour = ~drv
+      scales:   x, xmin, xmax, xend, xintercept, xmin_final, xmax_final, xlower, xmiddle, xupper, x0, colour 
+      faceting:  ~year, ~cyl 
+      -----------------------------------
+      geom_point: na.rm = FALSE
+      stat_identity: na.rm = FALSE
+      position_identity 
+      
+

tests/testthat/_snaps/utilities.md

@@ -196,3 +196,16 @@ test_that("expose/ignore_data() can round-trip a data.frame", {
   expect_equal(test, df[, c("a", "c", "b", "d")])
 
 })
+
+test_that("summary method gives a nice summary", {
+  # This test isn't important enough to break anything on CRAN
+  skip_on_cran()
+
+  p <- ggplot(mpg, aes(displ, hwy, colour = drv)) +
+    geom_point() +
+    scale_x_continuous() +
+    scale_colour_brewer() +
+    facet_grid(year ~ cyl)
+
+  expect_snapshot(summary(p))
+})

tests/testthat/test-utilities.R

@@ -39,7 +39,7 @@ You should use `annotate(geom = "text")` instead of `geom_text()` for annotation
 In the following visualisation we have annotated a histogram with a red line and red text to mark the mean. Note that both the line and the text appears pixellated/fuzzy.
 
 ```{r}
-#| fig.alt = "Histogram of highway miles per gallon for 234 cars. A red line is
+#| fig.alt: "Histogram of highway miles per gallon for 234 cars. A red line is
 #|  placed at the position 23.44 and is adorned with the label 'mean 23.44'.
 #|  Both the line and the text appear pixellated due to overplotting."
 mean_hwy <- round(mean(mpg$hwy), 2)
@@ -62,7 +62,7 @@ This is because `geom_text()` draws the geom once per each row of the data frame
 
 
 ```{r}
-#| fig.alt = "Histogram of highway miles per gallon for 234 cars. A red line is
+#| fig.alt: "Histogram of highway miles per gallon for 234 cars. A red line is
 #|  placed at the position 23.44 and is adorned with the label 'mean = 23.44'.
 #|  Both the line and the text appear crisp."
 ggplot(mpg, aes(x = hwy)) +
@@ -91,7 +91,7 @@ Set `vjust = "inward"` and `hjust = "inward"` in `geom_text()`.
 Suppose you have the following data frame and visualization. The labels at the edges of the plot are cut off slightly.
 
 ```{r}
-#| fig.alt = "A plot showing the words 'two', 'three' and 'four' arranged 
+#| fig.alt: "A plot showing the words 'two', 'three' and 'four' arranged 
 #|  diagonally. The 'two' and 'four' labels have been clipped to the panel's 
 #|  edge and are not displayed completely."
 df <- tibble::tribble(
@@ -108,7 +108,7 @@ ggplot(df, aes(x = x, y = y, label = name)) +
 You could manually extend axis limits to avoid this, but a more straightforward approach is to set `vjust = "inward"` and `hjust = "inward"` in `geom_text()`.
 
 ```{r}
-#| fig.alt = "A plot showing the words 'two', 'three' and 'four' arranged 
+#| fig.alt: "A plot showing the words 'two', 'three' and 'four' arranged 
 #|  diagonally. The 'two' and 'four' labels are aligned to the top-right and 
 #|  bottom-left relative to their anchor points, and are displayed in their
 #|  entirety."
@@ -129,7 +129,7 @@ Either calculate the counts ahead of time and place them on bars using `geom_tex
 Suppose you have the following bar plot and you want to add the number of cars that fall into each `drv` level on their respective bars.
 
 ```{r}
-#| fig.alt = "A bar chart showing the number of cars for each of three types
+#| fig.alt: "A bar chart showing the number of cars for each of three types
 #|  of drive train."
 ggplot(mpg, aes(x = drv)) +
   geom_bar()
@@ -139,7 +139,7 @@ One option is to calculate the counts with `dplyr::count()` and then pass them t
 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
+#| 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) %>%
@@ -152,7 +152,7 @@ mpg %>%
 Another option is to let `ggplot()` do the counting for you, and access these counts with `after_stat(count)` that is mapped to the labels to be placed on the plot with `stat_count()`.
 
 ```{r}
-#| fig.alt = "A bar chart showing the number of cars for each of three types
+#| 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."
 ggplot(mpg, aes(x = drv)) +
   geom_bar() + 
@@ -173,7 +173,7 @@ First calculate the counts for each segment (e.g. with `dplyr::count()`) and the
 Suppose you have the following stacked bar plot.
 
 ```{r}
-#| fig.alt = "A stacked bar chart showing the number of cars for each of seven 
+#| fig.alt: "A stacked bar chart showing the number of cars for each of seven 
 #|  types of cars. The fill colour of the bars indicate the type of drive 
 #|  train."
 ggplot(mpg, aes(x = class, fill = drv)) +
@@ -190,7 +190,7 @@ mpg %>%
 You can then pass this result directly to `ggplot()`, draw the segments with appropriate heights with `y = n` in the `aes`thetic mapping and `geom_col()` to draw the bars, and finally place the counts on the plot with `geom_text()`.
 
 ```{r}
-#| fig.alt = "A stacked bar chart showing the number of cars for each of seven 
+#| fig.alt: "A stacked bar chart showing the number of cars for each of seven 
 #|  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."
@@ -214,7 +214,7 @@ Either calculate the proportions ahead of time and place them on bars using `geo
 Suppose you have the following bar plot but you want to display the proportion of cars that fall into each `drv` level, instead of the count.
 
 ```{r}
-#| fig.alt = "A bar chart showing the number of cars for each of three types
+#| fig.alt: "A bar chart showing the number of cars for each of three types
 #|  of drive train."
 ggplot(mpg, aes(x = drv)) +
   geom_bar()
@@ -223,7 +223,7 @@ ggplot(mpg, aes(x = drv)) +
 One option is to calculate the proportions with `dplyr::count()` and then use `geom_col()` to draw the bars
 
 ```{r}
-#| fig.alt = "A bar chart showing the proportion of cars for each of three types
+#| fig.alt: "A bar chart showing the proportion of cars for each of three types
 #|  of drive train."
 mpg %>%
   dplyr::count(drv) %>%
@@ -236,7 +236,7 @@ Another option is to let `ggplot()` do the calculation of proportions for you, a
 Note that we also need to the `group = 1` mapping for this option.
 
 ```{r}
-#| fig.alt = "A bar chart showing the proportion of cars for each of three types
+#| fig.alt: "A bar chart showing the proportion of cars for each of three types
 #|  of drive train."
 ggplot(mpg, aes(x = drv, y = ..prop.., group = 1)) +
   geom_bar()