Draw rectangles.


geom_raster(mapping = NULL, data = NULL, stat = "identity", position = "identity", hjust = 0.5, vjust = 0.5, interpolate = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
geom_rect(mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)
geom_tile(mapping = NULL, data = NULL, stat = "identity", position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ...)


Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.
A data frame. If specified, overrides the default data frame defined at the top level of the plot.
The statistical transformation to use on the data for this layer, as a string.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
hjust, vjust
horizontal and vertical justification of the grob. Each justification value should be a number between 0 and 1. Defaults to 0.5 for both, centering each pixel over its data location.
If TRUE interpolate linearly, if FALSE (the default) don't interpolate.
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.
logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.
other arguments passed on to layer. There are three types of arguments you can use here:
  • Aesthetics: to set an aesthetic to a fixed value, like color = "red" or size = 3.
  • Other arguments to the layer, for example you override the default stat associated with the layer.
  • Other arguments passed on to the stat.


geom_rect and geom_tile do the same thing, but are parameterised differently. geom_rect uses the locations of the four corners (xmin, xmax, ymin and ymax). geom_tile uses the center of the tile and its size (x, y, width, height). geom_raster is a high performance special case for when all the tiles are the same size.


geom_tile understands the following aesthetics (required aesthetics are in bold):

  • x
  • y
  • alpha
  • colour
  • fill
  • linetype
  • size


# The most common use for rectangles is to draw a surface. You always want # to use geom_raster here because it's so much faster, and produces # smaller output when saving to PDF ggplot(faithfuld, aes(waiting, eruptions)) + geom_raster(aes(fill = density))

# Interpolation smooths the surface & is most helpful when rendering images. ggplot(faithfuld, aes(waiting, eruptions)) + geom_raster(aes(fill = density), interpolate = TRUE)

# If you want to draw arbitrary rectangles, use geom_tile() or geom_rect() df <- data.frame( x = rep(c(2, 5, 7, 9, 12), 2), y = rep(c(1, 2), each = 5), z = factor(rep(1:5, each = 2)), w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2) ) ggplot(df, aes(x, y)) + geom_tile(aes(fill = z))

ggplot(df, aes(x, y)) + geom_tile(aes(fill = z, width = w), colour = "grey50")

ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) + geom_rect(aes(fill = z, width = w), colour = "grey50")

# Justification controls where the cells are anchored df <- expand.grid(x = 0:5, y = 0:5) df$z <- runif(nrow(df)) # default is compatible with geom_tile() ggplot(df, aes(x, y, fill = z)) + geom_raster()

# zero padding ggplot(df, aes(x, y, fill = z)) + geom_raster(hjust = 0, vjust = 0)

# Inspired by the image-density plots of Ken Knoblauch cars <- ggplot(mtcars, aes(mpg, factor(cyl))) cars + geom_point()

cars + stat_bin2d(aes(fill = ..count..), binwidth = c(3,1))

cars + stat_bin2d(aes(fill = ..density..), binwidth = c(3,1))

cars + stat_density(aes(fill = ..density..), geom = "raster", position = "identity")

cars + stat_density(aes(fill = ..count..), geom = "raster", position = "identity")