Style code used in examples (#4098)

Fixes #4092

Author: rjake

NEWS.md

@@ -2,6 +2,8 @@
 
 * Fixed a bug in `labeller()` so that `.default` is passed to `as_labeller()`
   when labellers are specified by naming faceting variables. (@waltersom, #4031)
+  
+* Updated style for example code (@rjake, #4092)
 
 # ggplot2 3.3.2
 This is a small release focusing on fixing regressions introduced in 3.3.1.

R/aes-colour-fill-alpha.r

@@ -92,8 +92,11 @@
 #' p <- ggplot(economics, aes(x = date, y = unemploy)) + geom_line()
 #' p
 #' yrng <- range(economics$unemploy)
-#' p <- p + geom_rect(aes(NULL, NULL, xmin = start, xmax = end, fill = party),
-#' ymin = yrng[1], ymax = yrng[2], data = presidential)
+#' p <- p +
+#'   geom_rect(
+#'     aes(NULL, NULL, xmin = start, xmax = end, fill = party),
+#'     ymin = yrng[1], ymax = yrng[2], data = presidential
+#'   )
 #' p
 #' p + scale_fill_manual(values = alpha(c("blue", "red"), .3))
 #' }

R/axis-secondary.R

@@ -59,27 +59,39 @@
 #'
 #' # Secondary axes work for date and datetime scales too:
 #' df <- data.frame(
-#'   dx = seq(as.POSIXct("2012-02-29 12:00:00",
-#'                        tz = "UTC",
-#'                        format = "%Y-%m-%d %H:%M:%S"
-#'   ),
-#'   length.out = 10, by = "4 hour"
+#'   dx = seq(
+#'     as.POSIXct("2012-02-29 12:00:00", tz = "UTC"),
+#'     length.out = 10,
+#'     by = "4 hour"
 #'   ),
 #'   price = seq(20, 200000, length.out = 10)
 #'  )
 #'
 #' # This may useful for labelling different time scales in the same plot
-#' ggplot(df, aes(x = dx, y = price)) + geom_line() +
-#'   scale_x_datetime("Date", date_labels = "%b %d",
-#'   date_breaks = "6 hour",
-#'   sec.axis = dup_axis(name = "Time of Day",
-#'   labels = scales::time_format("%I %p")))
+#' ggplot(df, aes(x = dx, y = price)) +
+#'   geom_line() +
+#'   scale_x_datetime(
+#'     "Date",
+#'     date_labels = "%b %d",
+#'     date_breaks = "6 hour",
+#'     sec.axis = dup_axis(
+#'       name = "Time of Day",
+#'       labels = scales::time_format("%I %p")
+#'     )
+#'   )
 #'
 #' # or to transform axes for different timezones
-#' ggplot(df, aes(x = dx, y = price)) + geom_line() +
-#'   scale_x_datetime("GMT", date_labels = "%b %d %I %p",
-#'   sec.axis = sec_axis(~ . + 8 * 3600, name = "GMT+8",
-#'   labels = scales::time_format("%b %d %I %p")))
+#' ggplot(df, aes(x = dx, y = price)) +
+#'   geom_line() +
+#'   scale_x_datetime("
+#'     GMT",
+#'     date_labels = "%b %d %I %p",
+#'     sec.axis = sec_axis(
+#'       ~ . + 8 * 3600,
+#'       name = "GMT+8",
+#'       labels = scales::time_format("%b %d %I %p")
+#'     )
+#'   )
 #'
 #' @export
 sec_axis <- function(trans = NULL, name = waiver(), breaks = waiver(), labels = waiver(),

R/geom-dotplot.r

@@ -60,8 +60,11 @@
 #' @references Wilkinson, L. (1999) Dot plots. The American Statistician,
 #'    53(3), 276-281.
 #' @examples
-#' ggplot(mtcars, aes(x = mpg)) + geom_dotplot()
-#' ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5)
+#' ggplot(mtcars, aes(x = mpg)) +
+#'   geom_dotplot()
+#'
+#' ggplot(mtcars, aes(x = mpg)) +
+#'   geom_dotplot(binwidth = 1.5)
 #'
 #' # Use fixed-width bins
 #' ggplot(mtcars, aes(x = mpg)) +
@@ -70,6 +73,7 @@
 #' # Some other stacking methods
 #' ggplot(mtcars, aes(x = mpg)) +
 #'   geom_dotplot(binwidth = 1.5, stackdir = "center")
+#'
 #' ggplot(mtcars, aes(x = mpg)) +
 #'   geom_dotplot(binwidth = 1.5, stackdir = "centerwhole")
 #'
@@ -78,13 +82,16 @@
 #'   scale_y_continuous(NULL, breaks = NULL)
 #'
 #' # Overlap dots vertically
-#' ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5, stackratio = .7)
+#' ggplot(mtcars, aes(x = mpg)) +
+#'   geom_dotplot(binwidth = 1.5, stackratio = .7)
 #'
 #' # Expand dot diameter
-#' ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5, dotsize = 1.25)
+#' ggplot(mtcars, aes(x = mpg)) +
+#'   geom_dotplot(binwidth = 1.5, dotsize = 1.25)
 #'
 #' # Change dot fill colour, stroke width
-#' ggplot(mtcars, aes(x = mpg)) + geom_dotplot(binwidth = 1.5, fill = "white", stroke = 2)
+#' ggplot(mtcars, aes(x = mpg)) +
+#'   geom_dotplot(binwidth = 1.5, fill = "white", stroke = 2)
 #'
 #' \donttest{
 #' # Examples with stacking along y axis instead of x

R/geom-errorbarh.r

@@ -17,9 +17,12 @@
 #' # Define the top and bottom of the errorbars
 #'
 #' p <- ggplot(df, aes(resp, trt, colour = group))
-#' p + geom_point() +
+#' p +
+#'   geom_point() +
 #'   geom_errorbarh(aes(xmax = resp + se, xmin = resp - se))
-#' p + geom_point() +
+#'
+#' p +
+#'   geom_point() +
 #'   geom_errorbarh(aes(xmax = resp + se, xmin = resp - se, height = .2))
 geom_errorbarh <- function(mapping = NULL, data = NULL,
                            stat = "identity", position = "identity",

R/geom-function.R

@@ -18,14 +18,20 @@
 #'   geom_function(fun = dnorm, colour = "red")
 #'
 #' # To plot functions without data, specify range of x-axis
-#' base <- ggplot() + xlim(-5, 5)
+#' base <-
+#'   ggplot() +
+#'   xlim(-5, 5)
+#'
 #' base + geom_function(fun = dnorm)
+#'
 #' base + geom_function(fun = dnorm, args = list(mean = 2, sd = .5))
 #'
 #' # The underlying mechanics evaluate the function at discrete points
 #' # and connect the points with lines
 #' base + stat_function(fun = dnorm, geom = "point")
+#'
 #' base + stat_function(fun = dnorm, geom = "point", n = 20)
+#'
 #' base + geom_function(fun = dnorm, n = 20)
 #'
 #' # Two functions on the same plot
@@ -35,11 +41,14 @@
 #'
 #' # Using a custom anonymous function
 #' base + geom_function(fun = function(x) 0.5*exp(-abs(x)))
+#'
 #' base + geom_function(fun = ~ 0.5*exp(-abs(.x)))
 #'
 #' # Using a custom named function
 #' f <- function(x) 0.5*exp(-abs(x))
+#'
 #' base + geom_function(fun = f)
+#'
 #' @export
 geom_function <- function(mapping = NULL, data = NULL, stat = "function",
                           position = "identity", ..., na.rm = FALSE,

R/geom-histogram.r

@@ -71,12 +71,18 @@
 #'
 #' # If, however, we want to see the number of votes cast in each
 #' # category, we need to weight by the votes variable
-#' m + geom_histogram(aes(weight = votes), binwidth = 0.1) + ylab("votes")
+#' m +
+#'   geom_histogram(aes(weight = votes), binwidth = 0.1) +
+#'   ylab("votes")
 #'
 #' # For transformed scales, binwidth applies to the transformed data.
 #' # The bins have constant width on the transformed scale.
-#' m + geom_histogram() + scale_x_log10()
-#' m + geom_histogram(binwidth = 0.05) + scale_x_log10()
+#' m +
+#'  geom_histogram() +
+#'  scale_x_log10()
+#' m +
+#'   geom_histogram(binwidth = 0.05) +
+#'   scale_x_log10()
 #'
 #' # For transformed coordinate systems, the binwidth applies to the
 #' # raw data. The bins have constant width on the original scale.
@@ -85,14 +91,20 @@
 #' # bar is anchored at zero, and so when transformed becomes negative
 #' # infinity. This is not a problem when transforming the scales, because
 #' # no observations have 0 ratings.
-#' m + geom_histogram(boundary = 0) + coord_trans(x = "log10")
+#' m +
+#'   geom_histogram(boundary = 0) +
+#'   coord_trans(x = "log10")
 #' # Use boundary = 0, to make sure we don't take sqrt of negative values
-#' m + geom_histogram(boundary = 0) + coord_trans(x = "sqrt")
+#' m +
+#'   geom_histogram(boundary = 0) +
+#'   coord_trans(x = "sqrt")
 #'
 #' # You can also transform the y axis.  Remember that the base of the bars
 #' # has value 0, so log transformations are not appropriate
 #' m <- ggplot(movies, aes(x = rating))
-#' m + geom_histogram(binwidth = 0.5) + scale_y_sqrt()
+#' m +
+#'   geom_histogram(binwidth = 0.5) +
+#'   scale_y_sqrt()
 #' }
 #'
 #' # You can specify a function for calculating binwidth, which is

R/geom-jitter.r

@@ -23,12 +23,16 @@
 #' p + geom_jitter(aes(colour = class))
 #'
 #' # Use smaller width/height to emphasise categories
-#' ggplot(mpg, aes(cyl, hwy)) + geom_jitter()
-#' ggplot(mpg, aes(cyl, hwy)) + geom_jitter(width = 0.25)
+#' ggplot(mpg, aes(cyl, hwy)) +
+#'   geom_jitter()
+#' ggplot(mpg, aes(cyl, hwy)) +
+#'   geom_jitter(width = 0.25)
 #'
 #' # Use larger width/height to completely smooth away discreteness
-#' ggplot(mpg, aes(cty, hwy)) + geom_jitter()
-#' ggplot(mpg, aes(cty, hwy)) + geom_jitter(width = 0.5, height = 0.5)
+#' ggplot(mpg, aes(cty, hwy)) +
+#'   geom_jitter()
+#' ggplot(mpg, aes(cty, hwy)) +
+#'   geom_jitter(width = 0.5, height = 0.5)
 geom_jitter <- function(mapping = NULL, data = NULL,
                         stat = "identity", position = "jitter",
                         ...,

R/geom-point.r

@@ -67,17 +67,21 @@
 #' # You can create interesting shapes by layering multiple points of
 #' # different sizes
 #' p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
-#' p + geom_point(aes(colour = factor(cyl)), size = 4) +
+#' p +
+#'   geom_point(aes(colour = factor(cyl)), size = 4) +
 #'   geom_point(colour = "grey90", size = 1.5)
-#' p + geom_point(colour = "black", size = 4.5) +
+#' p +
+#'   geom_point(colour = "black", size = 4.5) +
 #'   geom_point(colour = "pink", size = 4) +
 #'   geom_point(aes(shape = factor(cyl)))
 #'
 #' # geom_point warns when missing values have been dropped from the data set
 #' # and not plotted, you can turn this off by setting na.rm = TRUE
 #' mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))
-#' ggplot(mtcars2, aes(wt, mpg)) + geom_point()
-#' ggplot(mtcars2, aes(wt, mpg)) + geom_point(na.rm = TRUE)
+#' ggplot(mtcars2, aes(wt, mpg)) +
+#'   geom_point()
+#' ggplot(mtcars2, aes(wt, mpg)) +
+#'   geom_point(na.rm = TRUE)
 #' }
 geom_point <- function(mapping = NULL, data = NULL,
                        stat = "identity", position = "identity",

R/geom-quantile.r

@@ -13,7 +13,9 @@
 #' @param geom,stat Use to override the default connection between
 #'   `geom_quantile()` and `stat_quantile()`.
 #' @examples
-#' m <- ggplot(mpg, aes(displ, 1 / hwy)) + geom_point()
+#' m <-
+#'   ggplot(mpg, aes(displ, 1 / hwy)) +
+#'   geom_point()
 #' m + geom_quantile()
 #' m + geom_quantile(quantiles = 0.5)
 #' q10 <- seq(0.05, 0.95, by = 0.05)