Geometric Bar Plots in ggplot2: Scaling Counts by a Variable

Introduction

In data visualization, geometric bar plots are a popular choice for displaying categorical data. When dealing with counts or proportions, it’s often desirable to scale these values by another variable, such as the population of each state in our example. In this article, we’ll explore how to achieve this using ggplot2 and the dplyr library.

Background

ggplot2 is a powerful data visualization library for R that offers an elegant syntax for creating beautiful plots. One of its key features is the use of geometric layers, such as geom_col() or geom_bar(), which allow us to create complex, layered plots with ease. However, when working with multiple variables, it can be challenging to scale counts by a specific variable.

In our example, we have two data frames: loans and state_pops. The loans data frame contains the borrower states and some additional information, while the state_pops data frame provides the population of each state. We want to create a bar plot that displays the counts of loans originated in each state, scaled by the inverse of the population.

The Problem

The original code snippet attempts to achieve this using a simple geom_bar() function, but it doesn’t quite work as expected:

ggplot(aes(x = BorrowerState), data = loans) + geom_bar()

This code will create a bar plot with the states on the x-axis and counts on the y-axis, but it won’t scale these counts by the inverse of the population.

The Solution

To solve this problem, we need to modify our approach. One way to do this is by using the geom_col() function instead of geom_bar(), along with the aes() function’s ability to manipulate variables:

ggplot(loans) +
  geom_col(aes(x=BorrowerState, y=1/Population)) +
  ylab("Loan Fraction")

In this code snippet, we’re using the inverse of the population (1/Population) as the y-variable. By doing so, we effectively scale the counts by the inverse of the population.

Understanding the Code

Let’s break down the code:

• ggplot(loans) specifies that we want to create a ggplot object from the loans data frame.
• geom_col() specifies that we want to use a geometric column layer, which is ideal for displaying counts or proportions.
• aes(x=BorrowerState, y=1/Population) maps the x and y variables to the columns of our data frame. By using y=1/Population, we’re scaling the counts by the inverse of the population.

Additional Considerations

When working with scales in ggplot2, it’s essential to consider the following:

• Scales are applied at the layer level: Each geometric layer, such as geom_col() or geom_bar(), has its own set of scales.
• Scales can be manipulated using aes(): By using aes() with a specific expression, we can manipulate variables and create custom scales.

Example Use Case

Suppose we have another data frame called sales that contains the sales amounts for different products. We want to create a bar plot that displays the sales amounts scaled by the price of each product:

library(ggplot2)
library(dplyr)

# Sample data
products <- data.frame("Product" = c("A", "B", "C"), "Sales" = c(100, 200, 300), "Price" = c(10, 20, 30))

# Create a ggplot object
ggplot(products) +
  geom_col(aes(x=Product, y=Sales/Price)) +
  ylab("Sales per Price Unit")

In this example, we’re using the Sales column as the x-variable and scaling it by the Price column using the expression Sales/Price. This results in a bar plot with sales amounts scaled by the price of each product.

Conclusion

In conclusion, geometric bar plots are a powerful tool for displaying categorical data. By understanding how to scale counts by a variable, such as the inverse of the population, we can create complex, layered plots that provide valuable insights into our data. With ggplot2 and the dplyr library, creating these plots is relatively straightforward.

Last modified on 2024-12-02