Line Charts & Scatter Plots

January 29, 2025

Line Plots


Mainly use line charts to display trends over time.

Data Setup

library(tidyverse)
library(vdemdata)
myData <- vdem %>% 
  mutate(region = e_regionpol_6C) %>%   ## make a better region variable
  mutate(region = case_match(region,    # new variable called region
                        1 ~ "Eastern Europe", 
                        2 ~ "Latin America",  
                        3 ~ "Middle East",   
                        4 ~ "Africa", 
                        5 ~ "The West", 
                        6 ~ "Asia")) %>%
    select(country_name, year, v2x_egaldem, v2x_polyarchy, v2x_delibdem, region, v2x_regime, e_peaveduc)

Data Setup

lineData <- myData %>% 
  group_by(year) %>% 
  summarize(democracy = mean(v2x_polyarchy, na.rm = TRUE))

Line Chart

ggplot(lineData, aes(x = year, y = democracy))

Line Chart

Use geom_line() to specify line chart

ggplot(lineData, aes(x = year, y = democracy)) +
  geom_line()

Add some labels

Code 
ggplot(lineData, aes(x = year, y = democracy)) +
  geom_line() +
  labs(x = "Year",
       y = "Average Electoral Democracy",
       title = "Average Global Electoral Democracy",
       caption = "Source: V-Dem Data")

Choose a theme you like, and maybe change some colors

Code 
ggplot(lineData, aes(x = year, y = democracy)) +
  geom_line(color = "coral3") +
  labs(x = "Year",
       y = "Average Electoral Democracy",
       title = "Average Global Electoral Democracy",
       caption = "Source: V-Dem Data") +
  theme_classic()

Add another dimension

Plot by world region: We need to recreate the data

lineDataRegion <- myData %>% 
  group_by(year, region) %>% 
  summarize(democracy = mean(v2x_polyarchy, na.rm = TRUE))
glimpse(lineDataRegion)
Rows: 1,404
Columns: 3
Groups: year [234]
$ year      <dbl> 1789, 1789, 1789, 1789, 1789, 1789, 1790, 1790, 1790, 1790, …
$ region    <chr> "Africa", "Asia", "Eastern Europe", "Latin America", "Middle…
$ democracy <dbl> 0.01000000, 0.02587500, 0.09600000, 0.03077778, 0.01942857, …

Add color = region to aes; remove color from geom_line(); update data; update legend

ggplot(lineDataRegion, aes(x = year, y = democracy, color = region)) +
  geom_line() +
  labs(x = "Year",
       y = "Average Electoral Democracy",
       title = "Average Electoral Democracy by Region",
       caption = "Source: V-Dem Data",
       color = "Region") +
  theme_bw()

Code 
ggplot(lineDataRegion, aes(x = year, y = democracy, color = region)) +
  geom_line() +
  labs(x = "Year",
       y = "Average Electoral Democracy",
       title = "Average Electoral Democracy by Region",
       caption = "Source: V-Dem Data",
       color = "Region") +
  theme_bw()

Color Blindness


  • Color Vision Deficiency (CVD) or color blindness affects 8 percent of men and 1 in 200 women (including me!)
  • 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…

Code 
ggplot(lineDataRegion, aes(x = year, y = democracy, color = region)) +
  geom_line() +
  labs(x = "Year",
       y = "Average Electoral Democracy",
       title = "Average Electoral Democracy by Region",
       caption = "Source: V-Dem Data",
       color = "Region") +
  theme_bw() +
  scale_color_viridis_d(option = "inferno", end = .8) # use viridis color palette

Palettes


  • There are a number of viridis palettes
  • See this reference to view different palettes and options
  • You can also use scale_color_viridis_c() to specify a continuous color scale
  • Also check out the paletteer package for easy access to many more palettes

Build your own line plot


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Scatterplots

  • We use scatterplots to illustrate the relationship between two numerical variables

  • We can use color, shapes, and sizing to add dimensions

Scatter Plot Setup


myData <- vdem %>% 
  filter(year == 2020) %>% 
  mutate(region = e_regionpol_6C) %>%   ## make a better region variable
  mutate(region = case_match(region,    # new variable called region
                        1 ~ "Eastern Europe", 
                        2 ~ "Latin America",  
                        3 ~ "Middle East",   
                        4 ~ "Africa", 
                        5 ~ "The West", 
                        6 ~ "Asia")) %>%
    select(country_name, year, v2x_egaldem, v2x_polyarchy, v2x_delibdem, region, v2x_regime, e_peaveduc)

Democracy and Avg Yrs of Education

ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc))

Democracy and Avg Yrs of Education

ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc)) +
  geom_point(color = "steelblue4")

How should we interpret this?

Code 
ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc)) +
  geom_point(color = "steelblue4") +
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education",
       caption = "Source: V-Dem Data")

Trend line can help

Code 
ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc)) +
  geom_point(color = "steelblue4") +
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education",
       caption = "Source: V-Dem Data") +
    geom_smooth(method = "lm", linewidth = .8, color = "coral")

Add another dimension: Region

Code 
ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc, color = region)) +
  geom_point() + # remember to remove color if addition color to aes!
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education",
       caption = "Source: V-Dem Data",
       color = "Region") +
    scale_color_viridis_d(option = "inferno", end = .9)  +
  theme_bw()

Add another dimension: Region

Add trend line for each region

Code 
ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc, color = region)) +
  geom_point() + # remember to remove color if adding color to aes!
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education",
       caption = "Source: V-Dem Data",
       color = "Region") +
    scale_color_viridis_d(option = "inferno", end = .9) +
      geom_smooth(method = "lm", linewidth = .8, se = FALSE) +
  theme_bw()

Facet Wrap can be helpful

ggplot(myData, aes(x = v2x_egaldem, y = e_peaveduc)) +
  geom_point(color = "steelblue") + # remember to remove color if addition color to aes!
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education",
       caption = "Source: V-Dem Data",
       color = "Region") +
    scale_color_viridis_d(option = "inferno", end = .9) +
      geom_smooth(method = "lm", linewidth = .6, color = "coral", se = FALSE) +
  facet_wrap(~region) +
  theme_bw()

Facet Wrap can be helpful

Last one . . . Labeling Points

geom_text()

myData %>% 
filter(region == "Asia") %>% 
  ggplot(., aes(x = v2x_egaldem, y = e_peaveduc)) +
  geom_point(color = "steelblue") + 
  labs(x = "Democracy",
       y = "Avg Years of Education",
       title = "Democracy and Average Years of Education in Asia",
       caption = "Source: V-Dem Data")   +
        geom_smooth(method = "lm", linewidth = .6, color = "coral", se = FALSE) +
      geom_text(aes(label = country_name), size = 2, vjust = 2) +
  theme_bw()

Last one . . . Labeling Points

geom_text()

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