Selective Bold Font on Graphs Using ggplot2

When creating informative graphs, highlighting key statistics can be an effective way to draw the viewer’s attention to important information. In this article, we’ll explore how to selectively bold font in a graph using ggplot2, a popular R graphics library.

Introduction

In many data analysis scenarios, you need to summarize your data with summary statistics such as mean and standard deviation (SD). These values provide valuable insights into the central tendency and variability of your dataset. However, when creating graphs to display these statistics, it can be challenging to determine which values should be highlighted in bold font.

In this article, we’ll present a solution using ggplot2, which allows us to selectively bold font on graph elements based on predefined conditions. We’ll also explore the code and data used in the example provided by the Stack Overflow question.

Data

To demonstrate our approach, let’s use the ChickWeight dataset, which is included in R with the datasets package. The dataset contains information about the weight of chicks at different times after hatching, grouped by their diet (chickmeal or chickcrumbs).

# Load necessary libraries
library(ggplot2)
library(dplyr)

# Calculate end vs start weights
df <- merge(filter(ChickWeight, Time == 21), filter(ChickWeight, Time == 0), by = c("Chick", "Diet"))
df$dWeight <- df$weight.x - df$weight.y

# Summary statistics: sd & mean
df.stat <- do.call(data.frame, 
                   aggregate(dWeight ~ Diet, 
                             data = df, 
                             FUN = function(x) c(SD = sd(x), MN = mean(x))))

df.stat

Creating the Graph

To create a graph that displays the summary statistics for each diet group, we’ll use ggplot2. We’ll also add facets to display multiple groups on the same plot.

# Combine df and df.stat -
# this also removes the calls to df.stat in your secondary geoms.
df.plot <- df %>%
  left_join(df.stat, by = "Diet") %>%
  mutate(dWeight.MN.max = max(dWeight.MN),
         dWeight.SD.min = min(dWeight.SD))

# Create the graph
ggplot(data = df.plot) +
  facet_grid(Diet ~ .) +
  geom_histogram(binwidth = 10, aes(x = dWeight)) + 
  geom_vline(aes(xintercept = dWeight.MN), color="black") + 
  geom_text(aes(x = Inf, 
                y = Inf, 
                label = sprintf("\nmean = %4.1f", dWeight.MN), 
                hjust = 1,
                vjust = 1,
                fontface = ifelse(dWeight.MN == dWeight.MN.max, 2, 1))) +
  geom_text(aes(x = Inf, 
                y = Inf, 
                label = sprintf("\n\nsd = %4.1f", dWeight.SD), 
                hjust = 1,
                vjust = 1,
                fontface = ifelse(dWeight.SD == dWeight.SD.min, 2, 1))) +
  theme_gray()

Explanation

In the code above, we’re using several ggplot2 features to create the graph:

• facet_grid(Diet ~ .): This creates a facet for each diet group.
• geom_histogram(binwidth = 10, aes(x = dWeight)): This adds a histogram to display the distribution of weights.
• geom_vline(aes(xintercept = dWeight.MN), color="black"): This adds a vertical line to highlight the mean weight for each group.
• geom_text(): This adds text labels to the graph, displaying the mean and standard deviation values.

We’re using fontface in the geom_text() layer to selectively bold font on specific text elements. The ifelse() function checks if the value is equal to the global maximum/minimum and sets the text to bold (>= 2) if true and leaves it plain (= 1) if false.

By using this approach, we can highlight important statistics in our graph without having to manually adjust the font size or color.

Conclusion

In this article, we’ve demonstrated how to selectively bold font on graphs using ggplot2. By combining features like facets, histograms, and text labels, we can create informative and visually appealing graphs that effectively communicate key statistics. With practice and experience, you’ll be able to apply this approach to your own data visualization projects and make your insights even more impactful.

We hope this article has been helpful in showcasing the flexibility and power of ggplot2. If you have any questions or need further assistance, feel free to ask!

Last modified on 2024-06-29