Data Viz Best Practices

Make Your Visualizations Accessible

September 22, 2024

Line Chart from Last Class

Problem

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

Solution: Use a colorblind safe color scheme like viridis…

Use scale_color_viridis_d() in this case to specify the viridis color scheme…

# in this ggplot() call, we add a third dimension for line color
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
  ) +
  scale_color_viridis_d(option = "mako", end = .8) # use viridis color palette

Better!

Your Turn!

See this reference to view different palettes and options
Also check out the paletteer package for easy access to many more palettes
Use scale_color_viridis_d() to specify a viridis color scheme

10:00

Scatter Plot Setup

dem_summary_ctry <-
  read_csv("data/dem_women.csv") |>
  group_by(country, region) |> # group by country, keep region
  summarize(
    polyarchy = mean(polyarchy, na.rm = TRUE),
    gdp_pc = mean(gdp_pc, na.rm = TRUE), 
    flfp = mean(flfp, na.rm = TRUE), 
    women_rep = mean(women_rep, na.rm = TRUE)
  )

Scatter Plot Example

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point(aes(color = region)) + 
  geom_smooth(method = "lm", linewidth = 1) + 
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute", 
    color = "Region"
    ) +
  scale_color_viridis_d(option = "mako", end = .8)

Continuous Color Scales

Use `scale_color_viridis_c()`

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point(aes(color = flfp)) + 
  geom_smooth(method = "lm", linewidth = 1) + 
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute", 
    color = "FLFP"
    ) +
  scale_color_viridis_c(option = "mako", end = .8)

Fill vs. Color

Use color (e.g. color = or scale_color_*) to modify the color of points, lines, or text.
Commonly applied to:
- Scatter plots
- Line charts
- Text elements

Fill vs. Color

Use fill (e.g. fill = or scale_fill_*) to modify the fill color of shapes like bars, boxes, or polygons.
Commonly applied to:
- Bar charts
- Box plots
- Histograms

Our Column Chart from Previous Class

Here we used the fill argument to color the bars in our column chart.

ggplot(dem_summary, aes(x = reorder(region, -polyarchy), y = polyarchy)) +
  geom_col(fill = "steelblue") + 
  labs(
    x = "Region", 
    y = "Avg. Polyarchy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    ) + 
  theme_minimal() +
  scale_fill_viridis_d(option = "mako", end = .8)

Should we do this instead?

ggplot(dem_summary, aes(x = reorder(region, -polyarchy), y = polyarchy, fill = region)) +
  geom_col() + 
  labs(
    x = "Region", 
    y = "Avg. Polyarchy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    ) + 
  theme_minimal() +
  scale_fill_viridis_d(option = "mako", end = .8)

Should we do this instead?

When to Use a Color Scheme

Use a color scheme when you want to differentiate between categories or groups
But only when the color adds value to the visualization
In this case, region is already differentiated by column height

Facet Wrapping

Use facet_wrap() with ~ before variable you want to wrap on…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point() + 
  geom_smooth(method = "lm", linewidth = 1) + 
  facet_wrap(~ region) +
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

Facet Wrapping

What else changes? Back down to two dimensions…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point() + 
  geom_smooth(method = "lm", linewidth = 1) + 
  facet_wrap(~ region) +
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

Facet Wrapping

Don’t forget to take the legend title out of the captions…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point() + 
  geom_smooth(method = "lm", linewidth = 1) + 
  facet_wrap(~ region) +
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

Your Turn!

Facet wrap your scatter plot
Use scales = "free" in facet_wrap call to fix the West
- facet_wrap(~ region, scales = "free")

05:00

Labeling Points

Filter for Asia, add labels with geom_text()…

dem_summary_ctry |> 
  filter(region == "Asia") |>
  ggplot(aes(x = gdp_pc, y = polyarchy)) + 
    geom_point() + 
    geom_text(aes(label = country), size = 2, vjust = 2) +
    geom_smooth(method = "lm", linewidth = 1) +
    scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
      labs(
        x= "GDP Per Capita", 
        y = "Polyarchy Score",
        title = "Wealth and democracy in Asia, 1990 - present", 
        caption = "Source: V-Dem Institute"
        )

Your Turn!

Filter for Asia or another region
Use geom_text() to add labels to your points
Play with size and vjust paramters

05:00

Make it Interactive

Use plotly to make any plot interactive…

library(plotly)

modernization_plot <- ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy)) + 
  geom_point(aes(color = region)) + 
  aes(label = country) +
  geom_smooth(method = "lm", linewidth = 1) + 
  scale_x_log10(labels = scales::label_number(prefix = "$", suffix = "k")) +
  labs(
    x= "GDP per Capita", 
    y = "Polyarchy Score",
    title = "Wealth and democracy, 1990 - present", 
    caption = "Source: V-Dem Institute", 
    color = "Region"
    ) +
  scale_color_viridis_d(option = "inferno", end = .8)

ggplotly(modernization_plot, tooltip = c("country", "gdp_pc", "polyarchy"))

Your Turn!

Make your scatter plot interactive with plotly
Use tooltip argument to show more information on hover
Make sure to include an additional aesthetic in aes() for the label

05:00

Messages, Warnings and Errors

Messages tell you what R is doing
Warnings tell you that something might be wrong
Errors tell you that something is definitely wrong
- Locate the error line number in the console and check your code
- Error line tells you about where the error occurred, not exact
- Errors are normal, don’t freak out!
- In fact, you should practice making errors to learn how to fix them

Data Viz Best Practices

Line Chart from Last Class

Problem

Color Blindness

Better!

Your Turn!

Scatter Plot Setup

Scatter Plot Example

Scatter Plot Example

Continuous Color Scales

Use scale_color_viridis_c()

Fill vs. Color

Fill vs. Color

Our Column Chart from Previous Class

When to Use a Color Scheme

Facet Wrapping

Facet Wrapping

Facet Wrapping

Facet Wrapping

Your Turn!

Labeling Points

Labeling Points

Your Turn!

Make it Interactive

Your Turn!

Messages, Warnings and Errors

Use `scale_color_viridis_c()`