ggplot2 Pie Chart: Why Custom Function Fails But Standalone Code Works

In this article, we’ll explore why a custom function to create pie charts with ggplot2 works as standalone code but fails when used inside another function. We’ll dive into the intricacies of how ggplot2 handles aesthetics and position.

Introduction to ggplot2 Pie Charts

ggplot2 is a powerful data visualization library in R that provides a consistent grammar for creating high-quality, informative graphics. One of its many features is the ability to create pie charts using various layers. In this article, we’ll focus on how ggplot2 handles aesthetics and position when creating pie charts.

Creating Pie Charts with ggplot2

Creating a simple pie chart with ggplot2 involves several steps:

Data Preparation: The data must be in a suitable format, typically a data frame with one row per observation and columns for the variable being measured and any additional information.
Grouping and Aggregation: The data is grouped by the categorical variable(s) of interest, and summaries are calculated (e.g., counts).
Aesthetic Mapping: Aesthetic mappings define how ggplot2 visualizes each column in the summary table as a layer.
Layering: Layers are combined to create the final plot.

Here’s an example using standalone code:

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

# Create sample data frame
dataframe <- as.data.frame(
    cbind(
        gender = c("male", "female", "female", "female", "male", "female", "male"),
        pet = c("dog", "cat", "dog", "cat", "cat", "cat", "dog")
    )
)

# Group by variables and calculate counts
df <- dplyr::group_by_(dataframe, .dots = c('gender', 'pet')) %>%
  dplyr::summarize(counts = n()) %>%
  dplyr::mutate(perc = (counts / sum(counts)) * 100) %>%
  dplyr::arrange(desc(perc))

# Create pie chart
ggplot2::ggplot(df, aes('', counts)) +
  geom_col(position = 'fill', aes(fill = factor(pet))) +
  facet_wrap(~gender, labeller = "label_both") +
  geom_label(
    aes(label = paste0(round(perc), "%"), fill = factor(pet)),
    position = position_fill(vjust = 0.5),
    color = 'black',
    size = 5,
    show.legend = FALSE
  ) +
  coord_polar(theta = "y")

Creating a Custom Function to Create Pie Charts

To create a custom function that produces the same plot, we can define a new function ggpiecustom:

# Define custom function to create pie chart
ggpiecustom <- function(data, condition, main) {
  # Group by variables and calculate counts
  df <- dplyr::group_by_(data, .dots = c(condition, main)) %>%
    dplyr::summarize(counts = n()) %>%
    dplyr::mutate(perc = (counts / sum(counts)) * 100) %>%
    dplyr::arrange(desc(perc))
  
  # Create pie chart
  ggplot2::ggplot(df, aes('', counts)) +
    geom_col(position = 'fill', aes(fill = factor(get(main)))) +
    facet_wrap(condition, labeller = "label_both") +
    geom_label(
      aes(label = paste0(round(perc), "%"), fill = factor(get(main))),
      position = position_fill(vjust = 0.5),
      color = 'black',
      size = 5,
      show.legend = FALSE
    ) +
    coord_polar(theta = "y")
}

# Use custom function to create pie chart
ggpiecustom(data = dataframe, condition = 'gender', main = 'pet')

Why the Custom Function Fails

The issue with using a custom function like ggpiecustom is that it doesn’t correctly map aesthetics from within the function. Specifically:

The fill aesthetic in the geom_col layer expects an actual value for the variable being measured (e.g., pet). However, when we use factor(get(main)), we’re trying to get the label of the categorical variable (main) and then factor it, which doesn’t produce a meaningful result.
The same issue applies to the fill aesthetic in the geom_label layer.

By using get(main) directly in the aesthetic mappings, we inadvertently introduce an incorrect mapping that can lead to unexpected behavior or even errors.

Conclusion

To summarize, creating pie charts with ggplot2 involves understanding how aesthetics and position are used to create layers. In this article, we explored why a custom function fails when trying to produce the same plot as standalone code. By avoiding incorrect aesthetic mappings within the function, we can ensure that our plots are accurate and informative.

Final Answer

Here is an updated version of the ggpiecustom function:

# Define custom function to create pie chart
ggpiecustom <- function(data, condition, main) {
  # Group by variables and calculate counts
  df <- dplyr::group_by_(data, .dots = c(condition, main)) %>%
    dplyr::summarize(counts = n()) %>%
    dplyr::mutate(perc = (counts / sum(counts)) * 100) %>%
    dplyr::arrange(desc(perc))
  
  # Create pie chart
  ggplot2::ggplot(df, aes(x = "", y = counts, fill = main)) +
    geom_col(position = 'fill') +
    facet_wrap(~ condition, labeller = "label_both") +
    geom_label(
      data = df,
      aes(label = paste0(round(perc), "%")),
      position = position_fill(vjust = 0.5),
      color = 'black',
      size = 5,
      show.legend = FALSE
    ) +
    coord_polar(theta = "y")
}

# Use custom function to create pie chart
ggpiecustom(data = dataframe, condition = 'gender', main = 'pet')

Last modified on 2024-11-25