Bar Charts and Histograms

October 27, 2023

The Grammar of Graphics

  • Data viz has a language with its own grammar
  • Basic components include:
    • Data we are trying to visualize
    • Aesthetics (dimensions)
    • Geom (e.g. bar, line, scatter plot)
    • Color scales
    • Themes
    • Annotations

Let’s start with the first two, the data and the aesthetic.


library(readr)
library(ggplot2)

dem_summary <- read_csv("data/dem_summary.csv")

ggplot(dem_summary, aes(x = region, y = polyarchy))

Gives us the axes without any visualization.


Now let’s add a geom. In this case we want a bar chart so we add geom_col()


ggplot(dem_summary, aes(x = region, y = polyarchy)) + 
  geom_col()

OK but that gets the idea across but looks a little depressing so…

…let’s change the color of the bars by specifying fill = "steelblue".


ggplot(dem_summary, aes(x = region, y = polyarchy)) + 
  geom_col(fill = "steelblue")

Note how color of original bars is simply overwritten.

Now let’s add some labels with the labs() function:


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

And that gives us…

Next, we reorder the bars with the reorder() function.


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"
    )

This way, we get a nice, visually appealing ordering of the bars according to levels of democracy…

Jumping ahead a little, let’s change the theme to theme_minimal().


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()

Gives us a clean, elegant look.

Note that you can also save your plot as an object to modify later.


dem_bar_chart <- ggplot(dem_summary, aes(x = reorder(region, -polyarchy), y = polyarchy)) +
  geom_col(fill = "steelblue")

Which gives us…

dem_bar_chart

Now let’s add our labels.


dem_bar_chart <- dem_bar_chart +
  labs(
    x = "Region", 
    y = "Avg. Polyarchy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
    )

So now we have…

dem_bar_chart

Now let’s add our theme.


dem_bar_chart <- dem_bar_chart + theme_minimal()

Voila!

dem_bar_chart

Change the theme.

dem_bar_chart + theme_bw()

Your Turn!


Use your own wrangled data or this code to get started:


library(readr)
library(ggplot2)

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

  1. glimpse() the data
  2. Find a new variable to visualize
  3. Make a bar chart with it
  4. Change the color of the bars
  5. Order the bars
  6. Add labels
  7. Add a theme
  8. Try saving your plot as an object
  9. Then change the labels and/or theme
10:00

Now Try a Histogram


Use this code or your own wrangled data to get started


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

  1. Pick a variable that you want to explore the distribution of
  2. Make a histogram
    1. Only specify x = in aes()
    2. Specify geom as geom_histogram
  3. Choose color for bars
  4. Choose appropriate labels
  5. Add a theme
10:00

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