Understanding the Issue with Pie Charts in R

The question at hand is related to plotting pie charts in R using the par function. Specifically, it involves the shrinking of pie chart sizes after a certain number of rows are specified. In this response, we will delve into the technical aspects of R’s graphics capabilities and explore possible solutions to prevent or mitigate this issue.

Background: Understanding the par Function

The par function in R is used to control various aspects of plotting, including the layout and appearance of plots. One common use of par is to set up a multi-panel plot by specifying the number and arrangement of rows and columns using the mfrow argument.

# Create a 3x2 grid for multiple pie charts
par(mfrow = c(3,2))

This allows for efficient plotting of multiple plots in a single figure, with each plot taking up a portion of the overall area. The c(3,2) specifies that there should be three rows and two columns.

Exploring the Shrinkage Issue

The original question mentions that when switching from a 3x2 grid to a 4x2 grid (i.e., more rows with fewer columns), the pie charts begin to shrink. This is an important observation, as it implies that there might be a fundamental limit to how large a single plot can become before it starts to degrade.

Understanding Aspect Ratios

R’s graphics system works based on aspect ratios. The par function controls these aspect ratios for various types of plots, including pie charts. When plotting with pie, R uses the 3D pie model, which is designed to display data as a circle with slices that represent different proportions.

# Plotting a pie chart using the default settings
pie(A, col = colsA, labels = pielabelsA)

This creates a pie chart where each slice represents a portion of the total. The size and aspect ratio of this pie chart are inherently linked to the proportions represented by A, colsA, pielabelsA. When displaying multiple pie charts in close proximity (like those in a 4x2 grid), R’s aspect ratios begin to become an issue.

Why Does Shrinkage Occur?

Shrinkage occurs due to a combination of factors. First, when more rows are used with fewer columns, the total area available for plotting decreases. This can result in each individual plot taking up less space, leading to smaller pie charts.

Second, R’s graphics system is geared towards displaying plots that have well-defined proportions and shapes. Pie charts, by their nature, do not fit neatly into this mold because their size depends on the proportion of data being represented. When multiple pie charts are displayed in a tight grid (4x2), these irregularities become more pronounced.

Mitigating Shrinkage

There are several strategies for mitigating shrinkage when displaying multiple pie charts:

1. Increase Aspect Ratio: By specifying asp parameter, you can set the aspect ratio to control how much each segment of the pie chart is scaled.

Plotting a pie chart with adjusted aspect ratio

pie(A, col = colsA, labels = pielabelsA, asp = c(1,0.5))

2.  **Use Other Chart Types**: While not necessarily offering an exact solution to shrinkage, switching to bar plots or histograms can provide alternative ways of displaying your data.

3.  **Modify Plot Layout**: Adjusting the layout and positioning of individual plots within a multi-panel plot (e.g., using `par(pty = "s")`) might help prevent extreme proportions from developing.

4.  **Consider Using Specialized Libraries**: Certain libraries, such as ggplot2 or patchwork, may offer more flexible approaches to arranging multiple plots in an R environment.

### Example Implementation

Below is a code example showcasing how you can implement these strategies:

```markdown
# Load necessary packages for plotting
library(ggplot2)
library(patchwork)

# Data preparation
A <- c(255, 26)
percentlabelsA <- round(100*A/sum(A), 2)
pielabelsA <- paste(percentlabelsA, "%", sep="")

B <- c(374,10)
percentlabelsB <- round(100*B/sum(B), 2)
pielabelsB <- paste(percentlabelsB, "%", sep="")
colsB <- c("red", "yellow")

C <- c(231, 25)
percentlabelsC <- round(100*C/sum(C), 2)
pielabelsC<- paste(percentlabelsC, "%", sep="")
colsC <- c("blue", "yellow")

D <- c(504, 37)
percentlabelsD <- round(100*D/sum(D), 2)
pielabelsD<- paste(percentlabelsD, "%", sep="")
colsD <- c("red", "yellow")

E <- c(50, 2)
percentlabelsE &lt ;-round(100*E/sum(E), 2)
pielabelsE <- paste(percentlabelsE, "%", sep="")
colsE <- c("blue", "yellow")

F <- c(506,14)
percentlabelsF <- round(100*F/sum(F), 2)
pielabelsF <- paste(percentlabelsF, "%", sep="")
colsF <- c("red", "yellow")

G <- c(26, 1)
percentlabelsG <- round(100*G/sum(G), 2)
pielabelsG <- paste(percentlabelsG, "%", sep="")
colsG <- c("blue", "yellow")

H <- c(530,15)
percentlabelsH <- round(100*H/sum(H), 2)
pielabelsH <- paste(percentlabelsH, "%", sep="")
colsH <- c("red", "yellow")

# Plotting pie charts with custom aspect ratios
ggplot() + 
  geom_col(aes(x = 1:8, y = A, fill = colsA), position = position_fill()) +
  labs(title="Pie chart with adjusted aspect ratio") +
  theme(legend.position = c(0.5, 0))

# Plotting pie charts within a 4x2 grid
plot_grid(
  pie(A, col = colsA, labels = pielabelsA) + 
    theme( legend.position = "none"),
  
  pie(B, col = colsB, labels = pielabelsB),
  
  pie(C, col = colsC, labels = pieLabelsC),
  
  pie(D, col = colsD, labels = pieLabelsD)
)

# Example usage with specialized libraries
plot_grid(
  ggplot() + 
    geom_col(aes(x = 1:8, y = A, fill = colsA), position = position_fill()) +
    labs(title="Pie chart plot", theme(legend.position = c(0.5, 0))),
  
  ggplot() + 
    geom_col(aes(x = 1:8, y = B, fill = colsB), position = position_fill()) +
    labs(title="", theme(legend.position = "none"))
)

The first code block demonstrates the use of ggplot2, which can effectively create pie charts with custom aspect ratios and better display these plots in a multi-panel layout.

The second example takes advantage of the patchwork library to arrange multiple pie charts within a single figure, showcasing an alternative strategy for managing plot size.

Last modified on 2024-01-28