Skip to content

scale_shape() maps discrete variables to six easily discernible shapes. If you have more than six levels, you will get a warning message, and the seventh and subsequent levels will not appear on the plot. Use scale_shape_manual() to supply your own values. You can not map a continuous variable to shape unless scale_shape_binned() is used. Still, as shape has no inherent order, this use is not advised.


scale_shape(name = waiver(), ..., solid = TRUE)

scale_shape_binned(name = waiver(), ..., solid = TRUE)



The name of the scale. Used as the axis or legend title. If waiver(), the default, the name of the scale is taken from the first mapping used for that aesthetic. If NULL, the legend title will be omitted.


Arguments passed on to discrete_scale


A palette function that when called with a single integer argument (the number of levels in the scale) returns the values that they should take (e.g., scales::pal_hue()).


One of:

  • NULL for no breaks

  • waiver() for the default breaks (the scale limits)

  • A character vector of breaks

  • A function that takes the limits as input and returns breaks as output. Also accepts rlang lambda function notation.


One of:

  • NULL to use the default scale values

  • A character vector that defines possible values of the scale and their order

  • A function that accepts the existing (automatic) values and returns new ones. Also accepts rlang lambda function notation.


Should unused factor levels be omitted from the scale? The default, TRUE, uses the levels that appear in the data; FALSE includes the levels in the factor. Please note that to display every level in a legend, the layer should use show.legend = TRUE.


Unlike continuous scales, discrete scales can easily show missing values, and do so by default. If you want to remove missing values from a discrete scale, specify na.translate = FALSE.


If na.translate = TRUE, what aesthetic value should the missing values be displayed as? Does not apply to position scales where NA is always placed at the far right.


The names of the aesthetics that this scale works with.


One of:

  • NULL for no labels

  • waiver() for the default labels computed by the transformation object

  • A character vector giving labels (must be same length as breaks)

  • An expression vector (must be the same length as breaks). See ?plotmath for details.

  • A function that takes the breaks as input and returns labels as output. Also accepts rlang lambda function notation.


A function used to create a guide or its name. See guides() for more information.


The call used to construct the scale for reporting messages.


The super class to use for the constructed scale


Should the shapes be solid, TRUE, or hollow, FALSE?

See also

The documentation for differentiation related aesthetics.

Other shape scales: scale_shape_manual(), scale_shape_identity().

The shape section of the online ggplot2 book.


dsmall <- diamonds[sample(nrow(diamonds), 100), ]

(d <- ggplot(dsmall, aes(carat, price)) + geom_point(aes(shape = cut)))
#> Warning: Using shapes for an ordinal variable is not advised

d + scale_shape(solid = TRUE) # the default

d + scale_shape(solid = FALSE)

d + scale_shape(name = "Cut of diamond")

# To change order of levels, change order of
# underlying factor
levels(dsmall$cut) <- c("Fair", "Good", "Very Good", "Premium", "Ideal")

# Need to recreate plot to pick up new data
ggplot(dsmall, aes(price, carat)) + geom_point(aes(shape = cut))
#> Warning: Using shapes for an ordinal variable is not advised

# Show a list of available shapes
df_shapes <- data.frame(shape = 0:24)
ggplot(df_shapes, aes(0, 0, shape = shape)) +
  geom_point(aes(shape = shape), size = 5, fill = 'red') +
  scale_shape_identity() +
  facet_wrap(~shape) +