Understanding and Working with Histograms in R: Changing X-Axis to “Integers”

In this article, we’ll delve into the world of histograms, focusing on a specific problem where users want to display only integer values on the x-axis. We’ll explore the necessary steps and concepts to achieve this goal.

Introduction

A histogram is a graphical representation that organizes a group of data points into specified ranges, called bins or intervals. The x-axis typically represents the bin values, while the y-axis represents the frequency or density of data points within each bin. When working with histograms in R, it’s essential to understand how to customize and manipulate the x-axis to meet specific requirements.

Problem Overview

In this scenario, we’re given a dataset containing population data for different regions. The task is to create histograms that display only integer values on the x-axis. We’ve tried using scale_y_continuous with the integer_breaks() function, but it didn’t produce the desired result. This problem highlights the importance of understanding how to work with axes in R and customize their appearance.

Solution Overview

To achieve our goal, we’ll explore alternative approaches to creating histograms that display integer values on the x-axis. We’ll discuss the following:

Converting data types
Using pivot_longer to reshape the data
Creating bar charts with facetting

These strategies will help us transform our dataset into a format suitable for displaying integer values on the x-axis.

Step 1: Convert Data Type and Prepare Data

The first step is to convert the PopMale, PopFemale, PopTotal, and PopDensity columns in our dataset to integers. We can do this using R’s built-in functions:

library(tidyverse)

df %>% 
  select(starts_with("Pop")) %>% 
  mutate(value = as.integer(value)) %>% 
  mutate(firstdigit = substr(as.character(value), 1, 1))

Step 2: Pivot the Data Using `pivot_longer`

Next, we’ll use pivot_longer to reshape our data from a wide format to a long format:

dfplot <- df %>% 
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value")

Step 3: Create Bar Charts with Faceting

Now that we have our data in the desired format, let’s create bar charts using geom_bar and facetting with facet_wrap. This will allow us to display integer values on the x-axis:

ggplot(dfplot, aes(firstdigit)) +
  geom_bar() +
  facet_wrap(~ variable)

Example Use Case

Let’s consider a real-world scenario where we’re analyzing population growth data for different regions. We want to visualize the population growth as a histogram with integer values on the x-axis:

# Load necessary libraries
library(tidyverse)

# Create sample dataset
df <- read_csv("https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_TotalPopulation.csv")

# Convert data type and prepare data
df %>% 
  select(starts_with("Pop")) %>% 
  mutate(value = as.integer(value)) %>% 
  mutate(firstdigit = substr(as.character(value), 1, 1))

# Pivot the data using pivot_longer
dfplot <- df %>% 
  pivot_longer(cols = everything(), names_to = "variable", values_to = "value")

# Create bar charts with faceting
ggplot(dfplot, aes(firstdigit)) +
  geom_bar() +
  facet_wrap(~ variable)

This code creates a bar chart that displays integer values on the x-axis, providing a clear and concise visual representation of the population growth data.

Conclusion

In this article, we’ve explored the process of creating histograms with R, focusing on customizing the x-axis to display only integer values. By converting data types, pivoting the data using pivot_longer, and creating bar charts with faceting, we’ve transformed our dataset into a format suitable for displaying integer values on the x-axis. This knowledge will help you effectively work with histograms in R and provide meaningful insights from your data analysis projects.

Last modified on 2025-01-17