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Switching Guidelines

Source: vignettes/switching-guidelines.Rmd
switching-guidelines.Rmd

Purpose

Using ideacolors with ggplot2 allows you to add IDEA’s or Camp RIO’s color palettes with familiar ggplot2 syntax. These branding guidelines change occasionally, and your visualizations will need to be adapated to use the updated guidelines. However, if you are trying to reproduce a visualization from an older branding guideline for consistency, then you’ll need to override the default behavior of ideacolors which uses the newest branding guidelines available.

Branding guidelines available

Below are the color palettes available from current and past branding guidelines:

  • IDEA: 2024 (default), 2019
  • Camp RIO: 2022 (default)

How it works

Suppose you have a visualization that uses an accent color, say idea_colors_2019$magenta or idea_colors_2024$vermillion, and you want to keep your visualizations consistent between different reports. To specify the branding guidelines for that visualization, you may need to use the year argument.

Option 1: use the most recent guidelines available

If you do not need to use prior branding guidelines, common functions like scale_color_idea(), scale_fill_idea(), scale_color_camp_rio() and scale_fill_camp_rio() will default to the most recent guidelines available (in this case, 2024).

library(ggplot2)
library(ideacolors)

# uses most recent branding guidelines
cars <- ggplot(mtcars) +
  geom_histogram(aes(x = mpg,
                     fill = as.factor(cyl))) +
  scale_fill_idea()
  
cars
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Option 2: explicitly specify branding guidelines

If you want to specify explicitly which guidelines you will use, then add the year argument to the scale_* functions. For example, using the 2019 guidelines would produce this graph:

cars +
  scale_fill_idea(year = 2019)
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Note that using scale_fill_idea(year = 2024) would produce the same graph using scale_fill_idea(), since those are the most recent guidelines. Similarly, using scale_fill_camp_rio(year = 2022) would be equivalent to scale_fill_camp_rio(). If you specify a year without branding guidelines available, then an error message will appear.

Option 3: use individual colors from each palette

If you needed to use an additional color from a different palette you currently use, you can always call each color independently:

my_palette <- c(
  "4" = idea_colors_2019$magenta,
  "6" = idea_colors_2024$darkblue,
  "8" = camp_rio_colors_2022$forestgreen
)

cars +
  scale_fill_manual(values = my_palette)
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Of course, always choose colors that produce a coherent visualization, since randomly selecting a palette may not create an aesthetically pleasing graph.