5  A Quick Introduction To ggplot2

5.1 Why Use ggplot?¹

A great deal of data analysis and visualization involves the same core set of steps: get some data, clean it up a little, run some descriptive statistics, run some bivariate statistics, create a graph or a visualization. ggplot2 (Wickham, 2016) can be an important part of a replicable, automated, documented workflow for complex projects.

%%{
  init: {
    'theme': 'base',
    'themeVariables': {
      'primaryColor': '#FFC20E',
      'primaryTextColor': '#000000',
      'primaryBorderColor': '#2D2926',
      'lineColor': '#2D2926',
      'secondaryColor': '#2D2926',
      'secondaryTextColor': '#000000',
      'tertiaryColor': '#F2F2F2',
      'tertiaryBorderColor': '#2D2926'
    }
  }
}%%

flowchart LR
  A(have a question) --> B(get data)
  B --> C(process and clean data) 
  C --> D(visualize data)
  D --> E(analyze data)
  E --> F(make conclusions)
  F --> G(share ideas)
  

Given the fact that we often want to apply the same core set of tasks to new questions and new data, there are ways to overcome the steep learning curve and learn a replicable set of commands that can be applied to problem after problem.

The same 5 to 10 lines of ggplot2 code can often be tweaked over and over again for multiple projects.

5.2 The Essential Idea Of ggplot Is Simple

There are 3 essential elements to any ggplot call:

1. A reference to the data you are using.
2. An aesthetic that tells ggplot which variables are being mapped to the x axis, y axis, (and often other attributes of the graph, such as the color, * color fill, or even the shape, size, transparency, or line type*). Intuitively, the aesthetic can be thought of as what you are graphing.
3. A geom or geometry that tells ggplot about the basic structure of the graph. Intuitively, the geom can be thought of as how you are graphing it.

You can also add other options, such as a graph title, axis labels and overall theme for the graph.

5.3 Get Started

5.3.1 Call Libraries

library(ggplot2) # beautiful graphs

library(ggthemes) # nice themes for ggplot2

5.3.2 Get Data

load("./simulate-data/MICSsimulated.RData") # data in R format

5.4 Some Examples²

5.4.1 One Continuous Variable

# anything that starts with a '#' is a comment

ggplot(MICSsimulated, # the data I am using
       aes(x = GII)) + # the variable I am using
  geom_histogram() # how I am graphing it

Figure 5.1: Histogram of Gender Inequality Index

We can add color and a theme.

# anything that starts with a '#' is a comment

ggplot(MICSsimulated, # the data I am using
       aes(x = GII)) + # the variable I am using
  geom_histogram(fill = "#1CABE2") + # how I am graphing it
  theme_minimal()

Figure 5.2: Histogram of Gender Inequality Index With Better Options

5.4.2 One Categorical Variable

Make sure R knows aggression is a categorical variable.

MICSsimulated$aggression <- 
  factor(MICSsimulated$aggression, # original numeric variable
         levels = c(0, 1), 
         labels = c("no aggression", "aggression"), 
         ordered = TRUE) # whether order matters

Now make the graph.

ggplot(MICSsimulated, # the data I am using
       aes(x = aggression)) + # the variable I am using
  geom_bar() # how I am graphing it

Figure 5.3: Bar Graph of Aggression

We can add color and a theme.³

ggplot(MICSsimulated, # the data I am using
       aes(x = aggression, # x is aggression
           fill = aggression)) + # fill is also aggression
  geom_bar() + # how I am graphing it
  theme_minimal()

Figure 5.4: Bar Graph of Aggression With Better Options

5.5 Make a More Complex Graph⁴

Make sure R knows cd1 is a categorical variable.

MICSsimulated$cd1 <- 
  factor(MICSsimulated$cd1, # original numeric variable
         levels = c(0, 1), 
         labels = c("no spank", "spank"), 
         ordered = TRUE) # whether order matters

Now make the graph.

ggplot(MICSsimulated, # the data I am using
       aes(x = cd1, # x is spanking
           fill = aggression)) + # fill is aggression
  geom_bar(position = position_dodge()) + # graph with "dodged" bars
  labs(title = "Spanking and Aggression", 
       x = "spanking", 
       y = "count") +
  scale_fill_manual(values = c("#1CABE2", # UNICEF colors
                               "#D8D1C9")) +
  theme_minimal()  # theme

Tip

An interactive tutorial to create this plot can be found here.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

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1. More information can be found here: https://agrogan1.github.io/R/introduction-to-ggplot2/introduction-to-ggplot2.html

2. Changing variables from factor to numeric (e.g. aes(x = as.numeric(outcome))), and vice versa can sometimes be a simple solution that solves a lot of problems when you are trying to graph your variables.

3. Notice how use of fill governs both the color fill in the graph below, as well as the legend that is produced in the graph.

4. Notice how use of fill governs both the color fill in the graph below, as well as the legend that is produced in the graph.