Accessibility and Color Schemes

October 27, 2023

Color Blindness


  • Color Vision Deficiency (CVD) or color blindness affects 8 percent of men and 1 in 200 women
  • There are different types of CVD but most common is red-green color blindness
  • Therefore, don’t include red and green in the same chart!
  • Look for color blind safe palettes

Last Week’s Line Chart

Last Week’s Line Chart


Create last week’s line chart and save as an object…

<br?

dem_waves_ctrs <- read_csv("https://raw.githubusercontent.com/eteitelbaum/dataviz-fall-2023/main/slides/data/dem_waves_ctrs.csv")

dem_waves_chart <- ggplot(dem_waves_ctrs, aes(x = year, y = polyarchy, color = country)) +
  geom_line(linewidth = 1) + # our geom is a line with a width of 1
  labs(
    x = "Year", 
    y = "Polyarchy Score", 
    title = 'Democracy in countries representing three different "waves"', 
    caption = "Source: V-Dem Institute", 
    color = "Country" # make title of legend to upper case
  )
02:00

CVD Plot


CVD Plot


Call `cvdPlot() from the colorBlindness package. CVD stands for “color vision deficiency.” Pass plot object that you saved into cvdPlot().

library(colorBlindness)

cvdPlot(dem_waves_chart)

CVD Plot


Click on little image to expand view…

Your Turn!


  • Take your dem_waves_chart object and run cvdPlot() on it
  • Expand the window and have a good look
  • Which group would have the toughest time reading this graph?
02:00

Create Your Own Color Scheme


Create Your Own Color Scheme


cb_palette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

flfp_region <- ggplot(flfp_gdp_regions, aes(x = reorder(iso3c, -flfp), y = flfp, fill = region)) +
  geom_col() + 
  scale_y_continuous(labels = scales::label_percent(scale = 1)) +
  labs(
    x = "Region", 
    y = "Avg. Female Labor Force Participation", 
    title = "Levels of female labor force participation by region", 
    fill = "Region",
    caption = "Source: World Bank"
    ) 

flfp_region + scale_fill_manual(values = cb_palette)

Create Your Own Color Scheme


cb_palette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

flfp_region <- ggplot(flfp_gdp_regions, aes(x = reorder(iso3c, -flfp), y = flfp, fill = region)) +
  geom_col() + 
  scale_y_continuous(labels = scales::label_percent(scale = 1)) +
  labs(
    x = "Region", 
    y = "Avg. Female Labor Force Participation", 
    title = "Levels of female labor force participation by region", 
    fill = "Region",
    caption = "Source: World Bank"
    ) 

flfp_region + scale_fill_manual(values = cb_palette)

Your Turn!

  • Go to Module 2.2 on the course website
  • Do the setup steps
  • Reproduce the column chart of FLFP with the custom color scheme
  • Try changing one of the hex codes
  • Now run cvdPlot() on the plot object to make sure it is color-blind friendly
  • Now try a Barbie theme or GW colors
10:00

Using a Color Palette


  • There are many color palettes
  • Viridis scales look cool and are accessible
  • ColorBrewer has many scales some of which are color-blind safe
  • The paletteer package aims to be a comprehensive collection of color palettes

Viridis

flfp_region + scale_fill_viridis_d()

Viridis

flfp_region <-flfp_region + scale_fill_viridis_d()
cvdPlot(flfp_region)

ColorBrewer

flfp_region + scale_fill_brewer(palette = "YlGn")

ColorBrewer

flfp_region <- flfp_region + scale_fill_brewer(palette = "YlGn")
cvdPlot(flfp_region)

Paletteer

library(paletteer)

flfp_region + scale_fill_paletteer_d("rcartocolor::Temps")

Paletteer

Not all palettes are color-blind safe…

flfp_region <- flfp_region + scale_fill_paletteer_d("rcartocolor::Temps")
cvdPlot(flfp_region)

Paletteer

And some many not have enough colors for your chart…

library(paletteer)

flfp_region + scale_fill_paletteer_d("beyonce::X1")

Your Turn


  • Try one of the color palettes packages we just discussed
  • Google the reference documents for details on schemes
  • For paletteer, check out this paletteer gallery
  • For ColorBrewer, check out this selector tool
  • A full list of viridis schemes is here
10:00

Scaling for Scatter Plots

Scaling for Scatter Plots

Use scale_color_viridis_d instead of scale_fill_viridis_d.

wealth_flfp <- ggplot(flfp_gdp, aes(x = gdp_pc, y = flfp)) + 
  geom_point(aes(color = region)) + # color points by region
  geom_smooth(method = "loess", linewidth = 1) +  # make the line a loess curve
  scale_x_log10(labels = scales::label_dollar()) + # stretch axis, add '$' format
  scale_y_continuous(labels = scales::label_percent(scale = 1)) + # add % label
  labs(
    x= "GDP per Capita", # x-axis title
    y = "Female Labor Force Participation", # y-axis title
    title = "Wealth and female labor force participation", # plot title
    caption = "Source: World Bank Development Indicators", # caption
    color = "Region" # legend title
    )

wealth_flfp + scale_color_viridis_d(option = "plasma")

Scaling for Scatter Plots

Use the end argument to darken the colors

wealth_flfp + scale_color_viridis_d(option = "plasma", end = .7)

Scaling for Scatter Plots

Use the direction argument to flip the color scale…

wealth_flfp + scale_color_viridis_d(option = "plasma", direction = -1)

Your Turn!


  • Try using one of the color schemes on a scatter plot
  • Use scale_color_ instead of scale_fill_
  • Play around with end and direction arguments in viridis
10:00

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