This document provides a brief overview of the main utiilty functions included in the egg
package.
The function expose_layout
can be useful to illustrate the structure of ggplot2 plots, e.g. when ying to customise and/or post-process the gtable layout.
p1 <- qplot(mpg, wt, data = mtcars, colour = cyl)
p2 <- qplot(mpg, data = mtcars) + ggtitle("title")
p3 <- qplot(mpg, data = mtcars, geom = "dotplot")
p4 <-
p1 + facet_wrap( ~ carb, nrow = 1) + theme(legend.position = "none") +
ggtitle("facetted plot")
pl <- lapply(list(p1, p2, p3, p4), expose_layout, FALSE, FALSE)
grid.arrange(
grobs = pl,
widths = c(1.2, 1, 1),
layout_matrix = rbind(c(1, 2, 3),
c(4, 4, 4))
)
In some cases, having ggplot2 expand the plot panel to best fit the available space isn’t ideal: for instance, we may want to produce multiple plots to appear on different slides of a presentation, and the successive pages should have the exact same layout for smooth visual transition. Another use-case is to embed multiple separate graphics in a drawing/page layout software. In this situation the plot alignement will be made manually, but the plots should not be rescaled (otherwise the fonts would be distorted). For such situations, the easiest solution is to set fixed dimensions to the gtable produced by ggplot2.
The function set_panel_size
helps set the panel size (width, height) to absolute measurements in the form of grid units. In the case of a facetted plot, all panels are set to the same value.
p1 <- qplot(mpg, wt, data = mtcars, colour = cyl)
p2 <- p1 + facet_wrap( ~ carb, nrow = 1)
grid.arrange(grobs = lapply(
list(p1, p2),
set_panel_size,
width = unit(2, "cm"),
height = unit(1, "in")
))
Note that the total size is now fixed, therefore when exporting the plot on a device it can be useful to query the size and set the width and height accordingly, to avoid clipping or white margins. This extra step is enabled by default when saving the results to a file.
gridExtra::grid.arrange
provides no way to align the panels of individual plots. While this is achievable with low-level gtable
functions, it often requires substantial effort on a case-by-case basis. The egg
package introduces a general strategy for such layout manipulations, with the following steps:
rbind
/cbind
p1 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point()
p2 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point() + facet_wrap(~ cyl, ncol = 2, scales = "free") +
guides(colour = "none") +
theme()
p3 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point() + facet_grid(. ~ cyl, scales = "free")
g1 <- ggplotGrob(p1)
g2 <- ggplotGrob(p2)
g3 <- ggplotGrob(p3)
fg1 <- gtable_frame(g1, debug = TRUE)
fg2 <- gtable_frame(g2, debug = TRUE)
fg12 <-
gtable_frame(gtable_rbind(fg1, fg2),
width = unit(2, "null"),
height = unit(1, "null"))
fg3 <-
gtable_frame(
g3,
width = unit(1, "null"),
height = unit(1, "null"),
debug = TRUE
)
grid.newpage()
combined <- gtable_cbind(fg12, fg3)
grid.draw(combined)
Using this generic strategy, we can easily align arbitrary plots (facetted or single-panel), with the convenience function ggarrange
,
p1 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point()+ theme_article() + theme(legend.position = 'top')
p2 <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) +
geom_point() + facet_wrap(~ cyl, ncol = 2, scales = "free") +
guides(colour = "none") +
theme_article()
ggarrange(p1, p2, widths = c(1.5,2))
Note that custom widths and heights may be provided for the layout.
For convenience, labels can be added to refer to the subfigures. All parameters of textGrob
can be used for the formatting of the labels, including the positioning (x
, hjust
, etc.).
ggarrange(p1, p2, p3, ncol=2,
labels = c("A", "b)", "iii."),
label.args = list(gp=gpar(font=4), x=unit(1,"line"), hjust=0))
The package provides two functions for labelling facetted plots in a more compact manner, removing panel strips and using in-panel tags instead:
egg
provides theme_article
and theme_presentation
,
The function symmetric_range
helps align the 0 value of adjacent panels in facetted plots with asymmetric range of data in each group.
The function geom_custom
extends the ggplot2 function annotation_custom
to cases where multiple grobs are to be placed, e.g. on different panels, or at different positions in a plot. This geom is a bit special in that it does not truly respect a grammar of graphics – arbitrary grobs can be plotted, with no explicit mapping to variables. Its typical use would be to place annotations (images, tables, …). The data used to create the annotation is passed as a list-column.
codes <- data.frame(country = c("nz","ca","ar","fr","gb","es"))
codes$y <- runif(nrow(codes))
gl <- lapply(codes$country,
function(.x) png::readPNG(system.file("flags",
paste0(.x,".png"),
package="egg")))
codes$raster <- I(gl)
ggplot(codes, aes(x = country, y = y)) +
geom_point() +
geom_custom(data = codes, aes(data=raster),
grob_fun = rasterGrob,
fun_params = list(height=unit(1,"cm"))) +
scale_y_continuous(breaks=NULL, "") +
theme(panel.grid = element_blank())
The list-column format allows passing grobs directly, in which case the grob_fun
function should be identity,
codes$raster <- I(lapply(codes$raster, function(x) rasterGrob(x, height=unit(1,"cm"))))
ggplot(codes, aes(x = country, y = y)) +
geom_point() +
geom_custom(data = codes, aes(data=raster),
grob_fun = identity)
Note that such grobs need to have x
and y
slots, which will be mapped to the appropriate location. It is therefore often necessary to create a wrapper with such fields, as illustrated below.
Because the grobs are manually “mapped”, independently of the main ggplot, this geom also allows the placing of arbitrary annotations without interference from transformed coordinate systems, etc.
custom_grob <- function(data, x=0.5,y=0.5){
grob(data=data,x=x,y=y, cl="custom")
}
preDrawDetails.custom <- function(x){
pushViewport(viewport(x=x$x,y=x$y))
}
postDrawDetails.custom <- function(x){
upViewport()
}
drawDetails.custom <- function(x, recording=FALSE, ...){
grid.rect(mean(x$data$x), mean(x$data$y),
width=diff(range(x$data$x)),
height=diff(range(x$data$y)))
grid.lines(x$data$x, x$data$y, gp=gpar(col=x$data$col,lwd=2), default.units = "native")
}
d <- data.frame(x=rep(1:3, 4), f=rep(letters[1:4], each=3))
gl <- lapply(1:4, function(ii){
data.frame(x=seq(0.4,0.6,length=10),
y = runif(10,0.45,0.55),
col = hcl(h = seq(0,300,length=nrow(d)))[ii],
stringsAsFactors = FALSE)
})
subplots <- data.frame(f=letters[1:4], data = I(gl))
str(subplots)
## 'data.frame': 4 obs. of 2 variables:
## $ f : Factor w/ 4 levels "a","b","c","d": 1 2 3 4
## $ data:List of 4
## ..$ :'data.frame': 10 obs. of 3 variables:
## .. ..$ x : num 0.4 0.422 0.444 0.467 0.489 ...
## .. ..$ y : num 0.469 0.452 0.491 0.498 0.492 ...
## .. ..$ col: chr "#FFC5D0" "#FFC5D0" "#FFC5D0" "#FFC5D0" ...
## ..$ :'data.frame': 10 obs. of 3 variables:
## .. ..$ x : num 0.4 0.422 0.444 0.467 0.489 ...
## .. ..$ y : num 0.527 0.471 0.481 0.547 0.508 ...
## .. ..$ col: chr "#FACABC" "#FACABC" "#FACABC" "#FACABC" ...
## ..$ :'data.frame': 10 obs. of 3 variables:
## .. ..$ x : num 0.4 0.422 0.444 0.467 0.489 ...
## .. ..$ y : num 0.469 0.478 0.459 0.471 0.548 ...
## .. ..$ col: chr "#EDD0AD" "#EDD0AD" "#EDD0AD" "#EDD0AD" ...
## ..$ :'data.frame': 10 obs. of 3 variables:
## .. ..$ x : num 0.4 0.422 0.444 0.467 0.489 ...
## .. ..$ y : num 0.477 0.46 0.462 0.549 0.549 ...
## .. ..$ col: chr "#DAD6A7" "#DAD6A7" "#DAD6A7" "#DAD6A7" ...
## ..- attr(*, "class")= chr "AsIs"