Which Visualization Should I Use?

September 22, 2024

Line Charts

Line charts are used to show trends over time
You especially want to use a line chart when you have multiple cases or categories that you want to compare over time

Huntington’s Three Waves

Line Chart Example

Here is the code…

# 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
  )

Use geom_line() to specify a line chart…

# 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
  )

Add third dimension to the aes() call for line color…

# 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
  )

Modify the legend title…

# 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
  )

Your Turn!

See table three of this article
Select three countries to visualize
Adjust setup code to filter data on those countries
Visualize with geom_line()

10:00

Scatter Plots

Scatter plots are used to show the relationship between two variables
Frequently the outcome variable is on the y-axis and the predictor variable is on the x-axis
In addition to the points, you can use color, size, and shape to add more information to the plot

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

Code

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy, color = region, size = women_rep)) + 
  geom_point() + # use geom_point() for scatter plots
  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",
    size = "Women Reps"
    )

Scatter Plot

Use geom_point()…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy, color = region, size = women_rep)) +
  geom_point() + # use geom_point() for scatter plots
  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",
    size = "Women Reps"
    )

Scatter Plot

Four dimensions…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy, color = region, size = women_rep)) + 
  geom_point() + # use geom_point() for scatter plots
  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",
    size = "Women Reps"
    )

Scatter Plot

Stretch axis on log scale and use scales package to adjust labels…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy, color = region, size = women_rep)) + 
  geom_point() + # use geom_point() for scatter plots
  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",
    size = "Women Reps"
    )

Scatter Plot

Change legend titles…

ggplot(dem_summary_ctry, aes(x = gdp_pc, y = polyarchy, color = region, size = women_rep)) + 
  geom_point() + # use geom_point() for scatter plots
  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",
    size = "Women Reps"
    )

Your Turn!

There are four variables in dem_summary_ctry
Pick one related to women’s empowerment
Visualize it on the y-axis with gdp_pc or polyarchy on the x-axis
Change labels and legend titles to match your visualization
Interpret your plot

10:00

Add a Trend Line

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"
    )

Add a Trend Line

Taking out size and adding color to geom_point() call…

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"
    )

Add a Trend Line

Changing legend titles…

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"
    )

Your Turn!

Add a trendline to your plot
Change the labels accordingly
Try using method = "loess" instead of a “lm”

10:00

Which Plot Should You Use?

Trends in stock process over time
Distribution of income in a country
Comparison of FLFP across MENA countries
Relationship between poverty and inequality (cross-nationally)

Which Geom Would You Use?

Column chart
Histogram
Line chart
Scatter plot

Other Plots and Geometries

Box Plot
geom_boxplot()
Violin Plot
geom_violin()
Density Plot
geom_density()
Bar Plot (Categorical)
geom_bar()
Heatmap
geom_tile()

Area Plot
geom_area()
Dot Plot
geom_dotplot()
Pie Chart
(usually a bar plot with coord_polar())
Ridgeline Plot
ggridges::geom_density_ridges()
Map Plot (Choropleth)
geom_polygon()