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These functions allow you to specify your own set of mappings from levels in the data to aesthetic values.


  aesthetics = "colour",
  breaks = waiver(),
  na.value = "grey50"

  aesthetics = "fill",
  breaks = waiver(),
  na.value = "grey50"

scale_size_manual(..., values, breaks = waiver(), na.value = NA)

scale_shape_manual(..., values, breaks = waiver(), na.value = NA)

scale_linetype_manual(..., values, breaks = waiver(), na.value = "blank")

scale_linewidth_manual(..., values, breaks = waiver(), na.value = NA)

scale_alpha_manual(..., values, breaks = waiver(), na.value = NA)

scale_discrete_manual(aesthetics, ..., values, breaks = waiver())



Arguments passed on to discrete_scale


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.


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.


One of:

  • NULL for no minor breaks

  • waiver() for the default breaks (none for discrete, one minor break between each major break for continuous)

  • A numeric vector of positions

  • A function that given the limits returns a vector of minor breaks. Also accepts rlang lambda function notation. When the function has two arguments, it will be given the limits and major break positions.


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


a set of aesthetic values to map data values to. The values will be matched in order (usually alphabetical) with the limits of the scale, or with breaks if provided. If this is a named vector, then the values will be matched based on the names instead. Data values that don't match will be given na.value.


Character string or vector of character strings listing the name(s) of the aesthetic(s) that this scale works with. This can be useful, for example, to apply colour settings to the colour and fill aesthetics at the same time, via aesthetics = c("colour", "fill").


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


The aesthetic value to use for missing (NA) values


The functions scale_colour_manual(), scale_fill_manual(), scale_size_manual(), etc. work on the aesthetics specified in the scale name: colour, fill, size, etc. However, the functions scale_colour_manual() and scale_fill_manual() also have an optional aesthetics argument that can be used to define both colour and fill aesthetic mappings via a single function call (see examples). The function scale_discrete_manual() is a generic scale that can work with any aesthetic or set of aesthetics provided via the aesthetics argument.

Color Blindness

Many color palettes derived from RGB combinations (like the "rainbow" color palette) are not suitable to support all viewers, especially those with color vision deficiencies. Using viridis type, which is perceptually uniform in both colour and black-and-white display is an easy option to ensure good perceptive properties of your visualizations. The colorspace package offers functionalities

  • to generate color palettes with good perceptive properties,

  • to analyse a given color palette, like emulating color blindness,

  • and to modify a given color palette for better perceptivity.

For more information on color vision deficiencies and suitable color choices see the paper on the colorspace package and references therein.


p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point(aes(colour = factor(cyl)))
p + scale_colour_manual(values = c("red", "blue", "green"))

# It's recommended to use a named vector
cols <- c("8" = "red", "4" = "blue", "6" = "darkgreen", "10" = "orange")
p + scale_colour_manual(values = cols)

# You can set color and fill aesthetics at the same time
  aes(mpg, wt, colour = factor(cyl), fill = factor(cyl))
) +
  geom_point(shape = 21, alpha = 0.5, size = 2) +
    values = cols,
    aesthetics = c("colour", "fill")

# As with other scales you can use breaks to control the appearance
# of the legend.
p + scale_colour_manual(values = cols)

p + scale_colour_manual(
  values = cols,
  breaks = c("4", "6", "8"),
  labels = c("four", "six", "eight")

# And limits to control the possible values of the scale
p + scale_colour_manual(values = cols, limits = c("4", "8"))

p + scale_colour_manual(values = cols, limits = c("4", "6", "8", "10"))