Including Number of Observations in Each Quartile of Boxplot using ggplot2 in R

In this article, we will explore how to add the number of observations in each quartile to a box-plot created with ggplot2 in R.

Introduction

Box-plots are a graphical representation that displays the distribution of data based on quartiles. A quartile is a value that divides the dataset into four equal parts. The first quartile (Q1) represents the lower 25% of the data, the second quartile (Q2 or median) represents the middle 50%, and the third quartile (Q3) represents the upper 25%. Understanding the distribution of your data using box-plots is essential in various fields such as statistics, engineering, economics, and many others.

R provides an extensive collection of packages that support the creation of box-plots. The most widely used package for creating plots with R is ggplot2, which offers a more efficient way to create static visualizations compared to base graphics.

Problem Statement

A user wants to plot a box-plot to visualize the distribution of the variable Sepal.Length from the iris dataset. In addition to displaying the values of quartiles, the user also wants to include the number of observations in each quartile on the right-hand side of the plot.

Solution Approach

To add the number of observations in each quartile to a box-plot created with ggplot2, we need to follow these steps:

1. Create Dataframe for Counting Quartiles: We will create a new dataframe that stores the count of observations in each quartile.
2. Calculate Quantiles and Counts: Using the quantile function, we calculate the first three quantiles (Q1, Q2, and Q3) for our dataset.
3. Create Box-plot with Quartile Values: We use ggplot to create a box-plot with the values of quartiles on the left-hand side of the plot.
4. Add Count of Observations in Each Quartile: We use the geom_text function from ggplot2 to add the count of observations in each quartile at the right-hand side of the plot.

Calculating Quantiles and Counts

We will first create a new dataframe, df_quantile_counts, that stores the count of observations in each quartile. The code for calculating quantiles and counts is provided below:

quantile_counts <- function(x) {
  df = data.frame(label = table(cut(x, quantile(x))),
                  label_pos = diff(quantile(x)) / 2 + quantile(x)[1:4])
  return(df)
}

df_quantile_counts = quantile_counts(df$Sepal.Length)

This code creates a new dataframe df_quantile_counts with two columns:

• label: This column stores the label for each quartile, which is calculated using the table(cut(x, quantile(x))) function.
• label_pos: This column stores the position of each quartile, which is calculated using the formula diff(quantile(x)) / 2 + quantile(x)[1:4].

Creating Box-plot with Quartile Values

We use ggplot to create a box-plot with the values of quartiles on the left-hand side of the plot. The code for creating the box-plot is provided below:

boxplot <- ggplot(df, aes(x = "", y = Sepal.Length)) +
  geom_boxplot(width = 0.1, position = "dodge", fill = "red") +
  stat_boxplot(geom = "errorbar", width = 0.1) +
  stat_summary(geom = "label_repel", fun.y = quantile, aes(label = ..y..),
               position = position_nudge(x = -0.1), size = 3) +
  geom_text(data=df_quantile_counts, aes(x="", y=label_pos, label = label.Freq),
            position = position_nudge(x = +0.1), size = 3) +
  ggtitle("") +
  xlab("") +
  ylab('Sepal.Length')

This code creates a box-plot with the following features:

• geom_boxplot: This function is used to create the box-plot itself.
• position = "dodge": This argument is used to offset each box by half of its width, creating space between boxes for better visibility.
• fill = "red": This argument is used to specify the fill color for each box in the plot.
• stat_boxplot: This function is used to add error bars to the plot, representing the interquartile range (IQR).
• fun.y = quantile: This argument is used to calculate the first three quantiles (Q1, Q2, and Q3) for our dataset.
• stat_summary: This function is used to create a label for each box in the plot.
• geom_text: This function is used to add text labels at specific positions on the plot.

Conclusion

In this article, we explored how to include the number of observations in each quartile to a box-plot created with ggplot2 in R. We created a new dataframe that stores the count of observations in each quartile and calculated the first three quantiles for our dataset using the quantile function.

We then used ggplot to create a box-plot with the values of quartiles on the left-hand side of the plot and added the count of observations in each quartile at the right-hand side of the plot. The resulting plot provides a clear visual representation of the distribution of our dataset, making it easier to analyze and understand.

Additional Tips

To further enhance your visualization skills:

• Experiment with different colors and fill patterns for boxes and error bars.
• Use labels and annotations to provide more context about your data points.
• Consider using interactive plots or simulations to explore the behavior of your dataset.

By following these steps and experimenting with different options, you can create informative and visually appealing box-plots that effectively communicate insights about your dataset.

Last modified on 2024-02-08