Convert any ggplot2 layer into a rasterized output

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


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

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

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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)

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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")

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

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

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Raserized plots with facet_wrap()

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

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

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Points: Rasterize scatterplots with geom_point_rast()

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


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)

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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: