scale_manual: Create your own discrete scale

Description Usage Arguments Details Value Color Blindness Examples

Description

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

Usage

1
2
3
4
5
6
scale_shadowcolour_manual(
  ...,
  values,
  aesthetics = "shadowcolour",
  breaks = waiver()
)

Arguments

...

Arguments passed on to ggplot2::discrete_scale

scale_name

The name of the scale that should be used for error messages associated with this 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()).

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

limits

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

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 aesthetic value should the missing values be displayed as? Does not apply to position scales where NA is always placed at the far right.

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.

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.

Value

a scale object to add to a plot.

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](https://arxiv.org/abs/1903.06490) and references therein.

Examples

1
2
3
4
library( ggplot2 )
p <- ggplot(mtcars, aes(wt, mpg, shadowcolour=as.factor(gear)))
p <- p + geom_glowpoint() + guides(shadowcolour='none')
p + scale_shadowcolour_manual(values=c('red', 'blue', 'green'))

ggshadow documentation built on Jan. 22, 2021, 5:12 p.m.