Visualizations

Section 6.5 Visualizations

With purely categorical data, the visualization most commonly recommended would be a bar chart. With a bar chart, a viewer can interpret differences between the variables. There are two main options, a stacked or grouped bar chart. The main difference from a coding perspective is what we enter inside the geom_bar command.

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Adding Flavor to Your Visualizations.

If you’d like to enhance the aesthetic of your plots, consider using the ggthemes package ([D.1.9]). It provides a variety of alternative themes that can quickly change the look and feel of your graphics.

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The examples below use theme_solarized() and theme_classic(), but many additional options are available. Explore and experiment — just be sure your styling choices support clarity and suit your audience.

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# We can use the library ggthemes to add some flavor to our plots
library(ggthemes)

# Creating a stacked bar chart
stacked <- ggplot(infection, aes(x = Treatment, fill = Infection)) +
  geom_bar(position = "fill") +   # "fill" stacks to 100% height
  labs(
    title = "Proportion of Infections by Treatment Type",
    y = "Proportion of Participants",
    x = "Treatment"
  ) +
  theme_solarized()

A stacked bar chart showing proportions of infection outcomes (No/Yes) for each treatment group: Control, Cranberry, and Lactobacillus. Cranberry has the highest proportion of non-infected participants. — Figure 6.5.1. Stacked bar chart showing the proportion of participants who were infected or not infected within each treatment group. Each bar represents a treatment, with segments indicating the relative frequency of infection outcomes.
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# Creating a grouped bar chart
grouped <- ggplot(infection, aes(x = Treatment, fill = Infection)) +
  geom_bar(position = "dodge") +
  geom_text(
    stat = "count",                          # use counts from geom_bar
    aes(label = after_stat(count)),          # label each bar with its count
    position = position_dodge(width = 0.9),  # place correctly over side-by-side bars
    vjust = -0.3,                            # move labels slightly above bars
    size = 4
  ) +
  labs(
    title = "Number of Infections by Treatment Type",
    x = "Treatment",
    y = "Count of Participants",
    fill = "Infection Outcome"
  ) +
  theme_classic()

A grouped bar chart showing counts of infection outcomes (No/Yes) side by side for each treatment group: Control, Cranberry, and Lactobacillus. Count labels appear above each bar. — Figure 6.5.2. Grouped bar chart displaying the raw counts of infected and not infected participants for each treatment type. Bars are shown side by side to facilitate direct comparison of observed frequencies across treatment groups.
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Using the patchwork package ([D.1.17]), we can add two or more plots together using the + symbol.

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# We can put them side by side to compare what they look like
library(patchwork)

grouped + stacked + plot_annotation(title = "Visualizing Infection Outcomes by Treatment (Stacked Vs. Grouped")

Two bar charts placed side by side: on the left, a grouped bar chart showing raw counts, and on the right, a stacked bar chart showing proportions for each treatment group. — Figure 6.5.3. Side-by-side comparison of stacked and grouped bar charts visualizing infection outcomes by treatment type. The grouped chart highlights raw counts while the stacked chart emphasizes proportional differences.
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To make our graphs more interactive with our users, we can utilize the ggplotly() function from the plotly package ([D.1.19]) ([D.1.28]).

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# We can also use the plotly package to make the visual more interactive
library(plotly)

ggplotly(grouped)

When using ggplotly:.

Move your cursor over the bars to gather more insights about your graph/data.

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