Create your own discrete scale — scale

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

scale_colour_manual(..., values, aesthetics = "colour", breaks = waiver())

scale_fill_manual(..., values, aesthetics = "fill", breaks = waiver())

scale_size_manual(..., values, breaks = waiver())

scale_shape_manual(..., values, breaks = waiver())

scale_linetype_manual(..., values, breaks = waiver())

scale_alpha_manual(..., values, breaks = waiver())

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

Arguments

...	Arguments passed on to `discrete_scale` `palette` 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::hue_pal()`). `limits` A character vector that defines possible values of the scale and their order. `drop` Should unused factor levels be omitted from the scale? The default, `TRUE`, uses the levels that appear in the data; `FALSE` uses all the levels in the factor. `na.translate` 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`. `na.value` If `na.translate = TRUE`, what value aesthetic value should missing be displayed as? Does not apply to position scales where `NA` is always placed at the far right. `scale_name` The name of the scale that should be used for error messages associated with this scale. `name` 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. `labels` 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`) A function that takes the breaks as input and returns labels as output `guide` A function used to create a guide or its name. See `guides()` for more information. `super` The super class to use for the constructed scale
values	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`.
aesthetics	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")`.
breaks	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

Details

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 visulizations. 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.

Examples

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
ggplot(
  mtcars,
  aes(mpg, wt, colour = factor(cyl), fill = factor(cyl))
) +
  geom_point(shape = 21, alpha = 0.5, size = 2) +
  scale_colour_manual(
    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"))
#> Warning: Removed 7 rows containing missing values (geom_point).
p + scale_colour_manual(values = cols, limits = c("4", "6", "8", "10"))