ggplot2: geom_tile – R documentation

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geom_tile

Rectangles

Description

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), while 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.

Usage

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",
  ...,
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_tile(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  linejoin = "mitre",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()` or `aes_()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`stat`	The statistical transformation to use on the data for this layer, as a string.
`position`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`...`	Other arguments passed on to `layer()`. These are often aesthetics, used to set an aesthetic to a fixed value, like `colour = "red"` or `size = 3`. They may also be parameters to the paired geom/stat.
`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.
`interpolate`	If `TRUE` interpolate linearly, if `FALSE` (the default) don't interpolate.
`na.rm`	If `FALSE`, the default, missing values are removed with a warning. If `TRUE`, missing values are silently removed.
`show.legend`	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. It can also be a named logical vector to finely select the aesthetics to display.
`inherit.aes`	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()`.
`linejoin`	Line join style (round, mitre, bevel).

Aesthetics

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

x
y
alpha
colour
fill
group
height
linetype
size
width

Learn more about setting these aesthetics in vignette("ggplot2-specs").

Examples

# 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), colour = "grey50")
ggplot(df, aes(x, y, width = w)) +
  geom_tile(aes(fill = z), colour = "grey50")
ggplot(df, aes(xmin = x - w / 2, xmax = x + w / 2, ymin = y, ymax = y + 1)) +
  geom_rect(aes(fill = z), 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 = after_stat(count)), binwidth = c(3,1))
cars + stat_bin2d(aes(fill = after_stat(density)), binwidth = c(3,1))

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

ggplot2

Create Elegant Data Visualisations Using the Grammar of Graphics

v3.3.3

MIT + file LICENSE

Authors

Hadley Wickham [aut] (<https://orcid.org/0000-0003-4757-117X>), Winston Chang [aut] (<https://orcid.org/0000-0002-1576-2126>), Lionel Henry [aut], Thomas Lin Pedersen [aut, cre] (<https://orcid.org/0000-0002-5147-4711>), Kohske Takahashi [aut], Claus Wilke [aut] (<https://orcid.org/0000-0002-7470-9261>), Kara Woo [aut] (<https://orcid.org/0000-0002-5125-4188>), Hiroaki Yutani [aut] (<https://orcid.org/0000-0002-3385-7233>), Dewey Dunnington [aut] (<https://orcid.org/0000-0002-9415-4582>), RStudio [cph, fnd]

Initial release