diff --git a/dev/articles/extending-ggplot2.html b/dev/articles/extending-ggplot2.html index a9e97e2d6e..18ff5ba448 100644 --- a/dev/articles/extending-ggplot2.html +++ b/dev/articles/extending-ggplot2.html @@ -153,13 +153,11 @@

ggproto } ) A$x -#> [1] 1 -
-A$inc()
+#> [1] 1
+A$inc()
 A$x
-#> [1] 2
-
-A$inc()
+#> [1] 2
+A$inc()
 A$inc()
 A$x
 #> [1] 4
@@ -179,7 +177,7 @@

The simplest stat
+
 StatChull <- ggproto("StatChull", Stat,
   compute_group = function(data, scales) {
     data[chull(data$x, data$y), , drop = FALSE]
@@ -204,7 +202,7 @@ 
The simplest statlayer() takes
 care of teasing the different parameters apart and making sure they’re
 stored in the right place:

-
+
 stat_chull <- function(mapping = NULL, data = NULL, geom = "polygon",
                        position = "identity", na.rm = FALSE, show.legend = NA, 
                        inherit.aes = TRUE, ...) {
@@ -218,7 +216,7 @@ 
The simplest statggplot2::layer() explicitly, or import the
 layer() function into your package namespace.)

 
Once we have a layer function we can try our new stat:

-
+
 ggplot(mpg, aes(displ, hwy)) + 
   geom_point() + 
   stat_chull(fill = NA, colour = "black")

@@ -230,7 +228,7 @@ 
The simplest stat
+
 ggplot(mpg, aes(displ, hwy, colour = drv)) + 
   geom_point() + 
   stat_chull(fill = NA)

@@ -240,7 +238,7 @@ 
The simplest stat

 
We can also override the default geom to display the convex hull in a
 different way:

-
+
 ggplot(mpg, aes(displ, hwy)) + 
   stat_chull(geom = "point", size = 4, colour = "red") +
   geom_point()

@@ -255,7 +253,7 @@ 
Stat parametersgeom_smooth() that adds a line of best
 fit to a plot. We create a StatLm that inherits from
 Stat and a layer function, stat_lm():

-
+
 StatLm <- ggproto("StatLm", Stat, 
   required_aes = c("x", "y"),
   
@@ -290,7 +288,7 @@ 
Stat parameterscompute_group() method and our wrapper function:

-
+
 StatLm <- ggproto("StatLm", Stat, 
   required_aes = c("x", "y"),
   
@@ -331,7 +329,7 @@ 
Stat parameters@inheritParams ggplot2::stat_identity: that will
 automatically inherit documentation for all the parameters also defined
 for stat_identity().

-
+
 #' @export
 #' @inheritParams ggplot2::stat_identity
 #' @param formula The modelling formula passed to \code{lm}. Should only 
@@ -365,7 +363,7 @@ 
Picking defaults
 
To do this we override the setup_params() method. It’s
 passed the data and a list of params, and returns an updated list.

-
+
 StatDensityCommon <- ggproto("StatDensityCommon", Stat, 
   required_aes = "x",
   
@@ -405,7 +403,7 @@ 
Picking defaultsA line plot showing three kernel density estimates of engine displacement,
  coloured for three types of drive trains. The lines are a little bit
  wobbly.

-
+
 
 ggplot(mpg, aes(displ, colour = drv)) + 
   stat_density_common(bandwidth = 0.5)

@@ -426,7 +424,7 @@ 
Variable names and default aesthe
 default_aes to automatically map density to
 y, which allows the user to override it to use with
 different geoms:

-
+
 StatDensityCommon <- ggproto("StatDensity2", Stat, 
   required_aes = "x",
   default_aes = aes(y = after_stat(density)),
@@ -445,7 +443,7 @@ 
Variable names and default aesthe
  the kernel density estimate of the displacement." width="672" style="display: block; margin: auto;">

 
However, using this stat with the area geom doesn’t work quite right.
 The areas don’t stack on top of each other:

-
+
 ggplot(mpg, aes(displ, fill = drv)) + 
   stat_density_common(bandwidth = 1, geom = "area", position = "stack")

 
An area plot showing the kernel density estimates of
@@ -455,7 +453,7 @@ <h3 id=Variable names and default aesthe
 
This is because each density is computed independently, and the
 estimated xs don’t line up. We can resolve that issue by
 computing the range of the data once in setup_params().

-
+
 StatDensityCommon <- ggproto("StatDensityCommon", Stat, 
   required_aes = "x",
   default_aes = aes(y = after_stat(density)),
@@ -484,7 +482,7 @@ 
Variable names and default aesthe
  Three areas are shown that indicate the estimates for three types of drive
  trains separately. The areas are stacked on top of one another and show
  no overlap." width="672" style="display: block; margin: auto;">

-
+
 ggplot(mpg, aes(displ, drv, fill = after_stat(density))) + 
   stat_density_common(bandwidth = 1, geom = "raster")

 
A heatmap showing the density of engine displacement for three types of
@@ -534,7 +532,7 @@ <h3 id=A simple geomgeom_point():

-
+
 GeomSimplePoint <- ggproto("GeomSimplePoint", Geom,
   required_aes = c("x", "y"),
   default_aes = aes(shape = 19, colour = "black"),
@@ -609,7 +607,7 @@ 
Collective geomsdraw_group().

 
The following code makes a simplified version of
 GeomPolygon:

-
+
 GeomSimplePolygon <- ggproto("GeomPolygon", Geom,
   required_aes = c("x", "y"),
   
@@ -687,7 +685,7 @@ 
Inheriting from an existing GeomGeomPolygon to work better with
 StatChull:

-
+
 GeomPolygonHollow <- ggproto("GeomPolygonHollow", GeomPolygon,
   default_aes = aes(colour = "black", fill = NA, linewidth = 0.5, linetype = 1,
     alpha = NA)
@@ -744,7 +742,7 @@ 
Omnidirectional statsThe actual guessing of orientation will happen in
 setup_params() using the has_flipped_aes()
 helper:

-
+
 StatBoxplot$setup_params
 #> <ggproto method>
 #>   <Wrapper function>
@@ -775,7 +773,7 @@ 
Omnidirectional statssetup_data():

-
+
 StatBoxplot$setup_data
 #> <ggproto method>
 #>   <Wrapper function>
@@ -811,7 +809,7 @@ 
Omnidirecitonal geomsstat_identity()):

-
+
 GeomBoxplot$setup_data
 #> <ggproto method>
 #>   <Wrapper function>
@@ -865,7 +863,7 @@ 
Dealing with required aestheticsStat and Geom classes understands the
 | (or) operator. Looking at GeomBoxplot we can
 see how it is used:

-
+
 GeomBoxplot$required_aes
 #> [1] "x|y"            "lower|xlower"   "upper|xupper"   "middle|xmiddle"
 #> [5] "ymin|xmin"      "ymax|xmax"

@@ -885,7 +883,7 @@ 
Ambiguous layersambiguous = TRUE to cancel any guessing based on data
 format. As an example we can see the setup_params() method
 of GeomLine:

-
+
 GeomLine$setup_params
 #> <ggproto method>
 #>   <Wrapper function>
@@ -917,11 +915,10 @@ 
Overriding elements
+
 theme_grey()$legend.key
-#> NULL

-
-
+#> NULL
+
 new_theme <- theme_grey() + theme(legend.key = element_rect(colour = "red"))
 new_theme$legend.key
 #> List of 5
@@ -933,7 +930,7 @@ 
Overriding elements#>  - attr(*, "class")= chr [1:2] "element_rect" "element"

 
To override it completely, use %+replace% instead of
 +:

-
+
 new_theme <- theme_grey() %+replace% theme(legend.key = element_rect(colour = "red"))
 new_theme$legend.key
 #> List of 5
@@ -986,7 +983,7 @@ 
Global elementsThese set default properties that are inherited by more specific
 settings. These are most useful for setting an overall “background”
 colour and overall font settings (e.g. family and size).

-
+
 df <- data.frame(x = 1:3, y = 1:3)
 base <- ggplot(df, aes(x, y)) + 
   geom_point() + 
@@ -995,7 +992,7 @@ 
Global elementsbase

 
Scatterplot of three observations arranged diagonally. The axis titles 'x'
  and 'y' are coloured in black

-
+
 base + theme(text = element_text(colour = "red"))

 
Scatterplot of three observations arranged diagonally. The axis titles 'x'
  and 'y' are coloured in red

@@ -1076,7 +1073,7 @@ 
Creating a layout specificationfacet_grid() will e.g. also return the
 faceting variables associated with each panel). Let’s make a function
 that defines a duplicate layout:

-
+
 layout <- function(data, params) {
   data.frame(PANEL = c(1L, 2L), SCALE_X = 1L, SCALE_Y = 1L)
 }

@@ -1090,7 +1087,7 @@ 
Mapping data into panelsPANEL column to the layer data identifying which
 panel it belongs to.

-
+
 mapping <- function(data, layout, params) {
   if (is.null(data) || nrow(data) == 0) {
     return(cbind(data, PANEL = integer(0)))
@@ -1111,7 +1108,7 @@ 
Laying out the panelsWhile the two functions above have been deceivingly simple, this last
 one is going to take some more work. Our goal is to draw two panels
 beside (or above) each other with axes etc.

-
+
 render <- function(panels, layout, x_scales, y_scales, ranges, coord, data,
                    theme, params) {
   # Place panels according to settings
@@ -1187,7 +1184,7 @@ 
Assembling the Facet class
-
+
 
Now with everything assembled, lets test it out:

-
+
 p <- ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point()
 p

 
Scatterplot showing horsepower against miles per gallon for 32 cars.

-
+
 p + facet_duplicate()

 
Scatterplot with two panels showing horsepower against miles per gallon for
  32 cars. The left and right panels are identical.

@@ -1220,7 +1217,7 @@ 
Doing more with facetsThe example above was pretty useless and we’ll now try to expand on
 it to add some actual usability. We are going to make a faceting that
 adds panels with y-transformed axes:

-
+
 library(scales)
 
 facet_trans <- function(trans, horizontal = TRUE, shrink = TRUE) {
@@ -1403,7 +1400,7 @@ 
Doing more with facets
 
Enough talk - lets see if our new and powerful faceting extension
 works:

-
+
 ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point() + facet_trans('sqrt')

 
Scatterplot with two panels showing horsepower against miles per
  gallon for 32 cars. Both panels show the same datapoints. The left panel is
@@ -1420,7 +1417,7 @@ <h2 id=Extending existing facet functionfacet_wrap() to make a facet_bootstrap() class
 that splits the input data into a number of panels at random.

-
+
 facet_bootstrap <- function(n = 9, prop = 0.2, nrow = NULL, ncol = NULL, 
   scales = "fixed", shrink = TRUE, strip.position = "top") {
   
@@ -1510,7 +1507,7 @@ 
Creating new guides
+
 p <- ggplot(mpg, aes(displ, hwy, colour = drv)) +
   geom_point() +
   scale_colour_discrete(
@@ -1533,7 +1530,7 @@ 
Overriding scale extraction
+
 GuideKey <- ggproto(
   "Guide", GuideAxis,
   
@@ -1558,7 +1555,7 @@ 
Guide constructors
+
 guide_key <- function(
   aesthetic, value = aesthetic, label = as.character(aesthetic),
   ...,
@@ -1579,7 +1576,7 @@ 
Guide constructors}

 
Our new guide can now be used inside the guides()
 function or as the guide argument in a position scale.

-
+
 ggplot(mpg, aes(displ, hwy)) +
   geom_point() +
   scale_x_continuous(
@@ -1609,7 +1606,7 @@ 
Custom drawingsposition parameter.

-
+
 # Same as before
 GuideKey <- ggproto(
   "Guide", GuideAxis,
@@ -1639,7 +1636,7 @@ 
Custom drawingsguide_key() in the key, adding a colour column
 to the key is straightforward. We can check that are guide looks correct
 in the different positions around the panel.

-
+
 ggplot(mpg, aes(displ, hwy)) +
   geom_point() +
   guides(
diff --git a/dev/articles/faq-faceting.html b/dev/articles/faq-faceting.html
index ddb5b3d5eb..211d1b915a 100644
--- a/dev/articles/faq-faceting.html
+++ b/dev/articles/faq-faceting.html
@@ -220,9 +220,8 @@ 
#>     filter, lag
 #> The following objects are masked from 'package:base':
 #> 
-#>     intersect, setdiff, setequal, union

-
-
+#>     intersect, setdiff, setequal, union
+
 mpg_summary <- mpg %>%
   group_by(drv) %>%
   summarise(hwy_mean = mean(hwy))
@@ -236,7 +235,7 @@ 
#> 3 r         21

 
Then, add a geom_vline() layer to your plot that uses
 the summary data.

-
+
 ggplot(mpg, aes(x = hwy)) +
   geom_histogram(binwidth = 5) +
   facet_wrap(~ drv) +
@@ -263,7 +262,7 @@ 
How can I set individua
 See example
 
Suppose you have the following faceted plot. By default, both x and y
 scales are shared across the facets.

-
+
 ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point() +
   facet_grid(cyl ~ drv)

@@ -277,7 +276,7 @@ 
How can I set individua
 of faceting functions: varying scales across rows
 ("free_x"), columns ("free_y"), or both rows
 and columns ("free"), e.g.

-
+
 ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point() +
   facet_grid(cyl ~ drv, scales = "free") 

@@ -292,7 +291,7 @@ 
How can I set individua
 included in each of the facets, you can set this with
 expand_limits(), e.g. ensure that 10 is included in the
 x-axis and values between 20 to 25 are included in the y-axis:

-
+
 ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point() +
   facet_grid(cyl ~ drv, scales = "free") +
@@ -319,7 +318,7 @@ 
How can I remove the facet l
 See example
 
Setting strip.text to element_blank() will
 remove all facet labels.

-
+
 ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point() +
   facet_grid(cyl ~ drv) +
@@ -331,7 +330,7 @@ 
How can I remove the facet l
 
You can also remove the labels across rows only with
 strip.x.text or across columns only with
 strip.y.text.

-
+
 ggplot(mpg, aes(x = cty, y = hwy)) +
   geom_point() +
   facet_grid(cyl ~ drv) +
@@ -357,7 +356,7 @@ 
+
 df <- data.frame(
   x = rnorm(100),
   group = c(rep("A long group name for the first group", 50),
@@ -376,7 +375,7 @@ 
label_wrap_gen() function, which
 is then passed to the labeller argument of your faceting
 function.

-
+
 ggplot(df, aes(x = x)) +
   geom_histogram(binwidth = 0.5) +
   facet_wrap(~ group, labeller = labeller(group = label_wrap_gen(width = 25)))

@@ -400,7 +399,7 @@ 
How can I set different
 See example
 
Suppose you have data price data on a given item over a few years
 from two countries with very different currency scales.

-
+
 df <- data.frame(
   year = rep(2016:2021, 2),
   price = c(10, 10, 13, 12, 14, 15, 1000, 1010, 1200, 1050, 1105, 1300),
@@ -424,7 +423,7 @@ 
How can I set different
 
You can plot price versus time and facet by
 country, but the resulting plot can be a bit difficult to
 read due to the shared y-axis label.

-
+
 ggplot(df, aes(x = year, y = price)) +
   geom_smooth() +
   facet_wrap(~ country, ncol = 1, scales = "free_y") +
@@ -437,7 +436,7 @@ 
How can I set different
 as_labeller(), turn off the default y-axis label, and then
 place the facet labels where the y-axis label goes
 ("outside" and on the "left").

-
+
 ggplot(df, aes(x = year, y = price)) +
   geom_smooth() +
   facet_wrap(~ country, ncol = 1, scales = "free_y", 
diff --git a/dev/articles/profiling.html b/dev/articles/profiling.html
index e0768c8abb..efe1b80fc2 100644
--- a/dev/articles/profiling.html
+++ b/dev/articles/profiling.html
@@ -130,7 +130,7 @@ 
Thomas Lin
 
 profile

 

-
In general, a minimal plot is used so that profiles are focused on
+
In general, a minimal plot is used so that profiles are focused on
 low-level, general code, rather than implementations of specific geoms.
 This might be expanded at the point where improving performance of
 specific geoms becomes a focus. Further, the profile focuses on the
diff --git a/dev/news/index.html b/dev/news/index.html
index 48831cb155..5d27fe9794 100644
--- a/dev/news/index.html
+++ b/dev/news/index.html
@@ -67,7 +67,8 @@
 
     

 
ggplot2 (development version)

-
  • All position scales now use the same definition of x and y aesthetics. This lets uncommon aesthetics like xintercept expand scales as usual. (#3342, #4966, @teunbrand)
    • 
+
    • geom_rect() can now derive the required corners positions from x/width or y/height parameterisation (@teunbrand, #5861).
      • 
+
    • All position scales now use the same definition of x and y aesthetics. This lets uncommon aesthetics like xintercept expand scales as usual. (#3342, #4966, @teunbrand)
      • 
 
    • Bare numeric values provided to Date or Datetime scales get inversely transformed (cast to Date/POSIXct) with a warning (@teunbrand).
      • 
 
    • stat_bin() now accepts functions for argument breaks (@aijordan, #4561)
      • 
 
    • (internal) The plot’s layout now has a coord parameter that is used to prevent setting up identical panel parameters (#5427)
      • 
diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml
index 64b965a898..91007694eb 100644
--- a/dev/pkgdown.yml
+++ b/dev/pkgdown.yml
@@ -13,7 +13,7 @@ articles:
   ggplot2-specs: ggplot2-specs.html
   ggplot2: ggplot2.html
   profiling: profiling.html
-last_built: 2024-07-08T09:16Z
+last_built: 2024-07-10T08:01Z
 urls:
   reference: https://ggplot2.tidyverse.org/reference
   article: https://ggplot2.tidyverse.org/articles
diff --git a/dev/reference/benchplot.html b/dev/reference/benchplot.html
index cd6e3dfba2..379fa9abba 100644
--- a/dev/reference/benchplot.html
+++ b/dev/reference/benchplot.html
@@ -91,29 +91,29 @@ 
      ArgumentsExamples
      
     
      benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point())
 #>        step user.self sys.self elapsed
-#> 1 construct     0.003        0   0.003
-#> 2     build     0.018        0   0.018
-#> 3    render     0.019        0   0.019
-#> 4      draw     0.020        0   0.020
-#> 5     TOTAL     0.060        0   0.060
+#> 1 construct     0.000    0.002   0.003
+#> 2     build     0.017    0.000   0.017
+#> 3    render     0.021    0.000   0.021
+#> 4      draw     0.020    0.000   0.020
+#> 5     TOTAL     0.058    0.002   0.061
 benchplot(ggplot(mtcars, aes(mpg, wt)) + geom_point() + facet_grid(. ~ cyl))
 #>        step user.self sys.self elapsed
-#> 1 construct     0.002    0.001   0.002
-#> 2     build     0.020    0.000   0.019
-#> 3    render     0.044    0.000   0.044
-#> 4      draw     0.035    0.000   0.035
-#> 5     TOTAL     0.101    0.001   0.100
+#> 1 construct     0.003        0   0.003
+#> 2     build     0.020        0   0.020
+#> 3    render     0.044        0   0.044
+#> 4      draw     0.034        0   0.034
+#> 5     TOTAL     0.101        0   0.101
 
 # With tidy eval:
 p <- expr(ggplot(mtcars, aes(mpg, wt)) + geom_point())
 benchplot(!!p)
 
 #>        step user.self sys.self elapsed
-#> 1 construct     0.002        0   0.003
-#> 2     build     0.017        0   0.018
-#> 3    render     0.018        0   0.019
-#> 4      draw     0.021        0   0.020
-#> 5     TOTAL     0.058        0   0.060
+#> 1 construct     0.003        0   0.002
+#> 2     build     0.018        0   0.018
+#> 3    render     0.019        0   0.019
+#> 4      draw     0.020        0   0.020
+#> 5     TOTAL     0.060        0   0.059