ggrastr

Convert any ggplot2 layer into a rasterized output

Using the function rasterize(), users can rasterize any ggplot2 layer:

library(ggplot2)
library(ggrastr)

plot <- ggplot(diamonds, aes(carat, price, colour = cut))

plot + rasterise(geom_point(), dpi = 72) + theme(aspect.ratio = 1)

Note that with ggrastr changes in version 0.2.0, when the aspect ratio is distorted, points are still rendered without distortion, i.e. the points are still circles:

# Points remain round across different aspect ratios
plot + rasterise(geom_point(), dpi = 72) + theme(aspect.ratio = 0.2)

By default, plots are rendered with cairo. However, users now have the option to render plots with the ragg device. The motivation for using ragg is that ragg can be faster and has better anti-aliasing. That being said, the default ragg device also has some alpha blending quirks. Because of these quirks, users are recommended to use the ragg_png option to work around the alpha blending.

The differences in devices are best seen at lower resolution:

# The default 'cairo' at dpi=5
plot + rasterise(geom_point(), dpi = 5, dev = "cairo")

# Using 'ragg' gives better anti-aliasing but has unexpected alpha blending
plot + rasterise(geom_point(), dpi = 5, dev = "ragg")

# Using 'ragg_png' solves the alpha blend, but requires writing a temporary file to disk
plot + rasterise(geom_point(), dpi = 5, dev = "ragg_png")

Raserized plots with facet_wrap()

Facets are rendered correctly without users having to adjust the width/height settings.

# Facets won't warp points
set.seed(123)
plot + rasterise(geom_point(), dpi = 300) + facet_wrap(~ sample(1:3, nrow(diamonds), 2))

Points: Rasterize scatterplots with geom_point_rast()

Sometimes you need to publish a figure in a vector format:

library(ggplot2)
library(ggrastr)

points_num <- 10000
df <- data.frame(x=rnorm(points_num), y=rnorm(points_num), c=as.factor(1:points_num %% 2))
gg <- ggplot(df, aes(x=x, y=y, color=c)) + scale_color_discrete(guide=FALSE)

gg_vec <- gg + geom_point(size=0.5)
print(gg_vec)

But in other cases, your figure contains thousands of points, e.g. try points_num <- 500000 in the example above, and you will notice the performance issues—it takes significantly longer to render the plot:

In this case, a reasonable solution would be to rasterize the plot. But the problem is that all text becomes rasterized as well. Raster layers with ggrastr were developed to prevent such a situation, here using geom_point_rast():

gg_rast <- gg + geom_point_rast(size=0.5)
print(gg_rast)

The plots look the same, but the difference in size can be seen when they are exported to pdfs. Unfortunately, there is a longer rendering time to produce such plots:

PrintFileSize <- function(gg, name) {
  invisible(ggsave('tmp.pdf', gg, width=4, height=4))
  cat(name, ': ', file.info('tmp.pdf')$size / 1024, ' Kb.\n', sep = '')
  unlink('tmp.pdf')
}

PrintFileSize(gg_rast, 'Raster')
#> Raster: 312.8545 Kb.
PrintFileSize(gg_vec, 'Vector')
#> Vector: 556.873 Kb.

As expected, the difference becomes larger with growth of number of points:

points_num <- 1000000
df <- data.frame(x=rnorm(points_num), y=rnorm(points_num), c=as.factor(1:points_num %% 2))
gg <- ggplot(df, aes(x=x, y=y, color=c)) + scale_color_discrete(guide=FALSE)

gg_vec <- gg + geom_point(size=0.5)
gg_rast <- gg + geom_point_rast(size=0.5)

PrintFileSize(gg_rast, 'Raster')
#> Raster: 400.4365 Kb.
PrintFileSize(gg_vec, 'Vector')
#> Vector: 54862.48 Kb.

Jitter: Rasterize jittered scatterplots with geom_jitter_rast()

Just like the example above withgeom_point_rast(), users may also opt to create rasterized scatterplots with jitter. The geom geom_jitter_rast() is similar to ggplot2::geom_jitter(), but it creates a rasterized layer:

library(ggplot2)
library(ggrastr)

points_num <- 5000 
df <- data.frame(x=rnorm(points_num), y=rnorm(points_num), c=as.factor(1:points_num %% 2))
gg <- ggplot(df, aes(x=x, y=y, color=c)) + scale_color_discrete(guide=FALSE)

gg_jitter_rast <- gg + geom_jitter_rast(raster.dpi=600)
print(gg_jitter_rast)

Tiles: Rasterize heatmaps with geom_tile_rast()

Heatmaps also have similar issues with the default vectorized formats:

library(ggplot2)
library(ggrastr)

coords <- expand.grid(1:500, 1:500)
coords$Value <- 1 / apply(as.matrix(coords), 1, function(x) sum((x - c(50, 50))^2)^0.01)
gg_tile_vec <- ggplot(coords) + geom_tile(aes(x=Var1, y=Var2, fill=Value))
gg_tile_rast <- ggplot(coords) + geom_tile_rast(aes(x=Var1, y=Var2, fill=Value))
print(gg_tile_rast)

We can see that the rasterized plots using ggrastr are lighter in size when rendered to pdf:

PrintFileSize(gg_tile_rast, 'Raster')
#> Raster: 46.76367 Kb.
PrintFileSize(gg_tile_vec, 'Vector')
#> Vector: 817.8281 Kb.

Violinplots: Rasterize violin plots with geom_violin_rast()

One can see a similar effect with violin plots:

library(ggplot2)
library(ggrastr)

gg_violin_vec <- ggplot(mtcars, aes(factor(cyl), mpg)) + geom_violin()
gg_violin_rast <- ggplot(mtcars) + geom_violin_rast(aes(factor(cyl), mpg))
print(gg_violin_rast)

## difference in size shown
PrintFileSize(gg_tile_rast, 'Raster')
#> Raster: 46.76367 Kb.
PrintFileSize(gg_tile_vec, 'Vector')
#> Vector: 817.8281 Kb.

Boxplots: Jitter outliers and rasterize boxplots with geom_boxplot_jitter

Another type of plots with a potentially large number of small objects is geom_boxplot:

library(ggplot2)
library(ggrastr)

points_num <- 5000
df <- data.frame(x=as.factor(1:points_num %% 2), y=log(abs(rcauchy(points_num))))
gg <- ggplot(df, aes(x=x, y=y)) + scale_color_discrete(guide=FALSE)

boxplot <- gg + geom_boxplot()
print(boxplot)

With a large number of objects, outlier points become noninformative. For example, here is the rendered plot with points_num <- 1000000:

For such a large number of points, it would be better to jitter them using geom_boxplot_jitter():

library(ggplot2)
library(ggrastr)
points_num <- 500000
df <- data.frame(x=as.factor(1:points_num %% 2), y=log(abs(rcauchy(points_num))))
gg <- ggplot(df, aes(x=x, y=y)) + scale_color_discrete(guide=FALSE)

gg_box_vec <- gg + geom_boxplot_jitter(outlier.size=0.1, outlier.jitter.width=0.3, outlier.alpha=0.5)
print(gg_box_vec)

And this geom can be rasterized as well:

gg_box_rast <- gg + geom_boxplot_jitter(outlier.size=0.1, outlier.jitter.width=0.3, outlier.alpha=0.5, raster.dpi=200)
print(gg_box_rast)

PrintFileSize(gg_box_rast, 'Raster')
#> Raster: 120.4463 Kb.
PrintFileSize(gg_box_vec, 'Vector')
#> Vector: 226.7061 Kb.

Beeswarm-style plots: geom_beeswarm_rast and geom_quasirandom

ggrastr also allows users to create rasterized beeswarm plots. As described in the README for ggbeeswarm,

Beeswarm plots (aka column scatter plots or violin scatter plots) are a way of plotting points that would ordinarily overlap so that they fall next to each other instead. In addition to reducing overplotting, it helps visualize the density of the data at each point (similar to a violin plot), while still showing each data point individually.

The ggrastr geom geom_beeswarm_rast is similar to ggbeeswarm::geom_beeswarm(), but it provides a rasterized layer:

library(ggplot2)
library(ggrastr)

ggplot(mtcars) + geom_beeswarm_rast(aes(x = factor(cyl), y=mpg), raster.dpi=600, cex=1.5)

Analogously, geom_quasirandom_rast is much like ggbeeswarm::geom_quasirandom(), but with a rasterized layer:

library(ggplot2)
library(ggrastr)

ggplot(mtcars) + geom_quasirandom_rast(aes(x = factor(cyl), y=mpg), raster.dpi=600)

We encourage users to visit both https://CRAN.R-project.org/package=ggbeeswarm and the github repo at https://github.com/eclarke/ggbeeswarm for more details.