Understanding Geom Histograms in ggplot2: Using Proportions Instead of Counts

===========================================================

In this post, we will explore how to create histograms using proportions instead of counts in ggplot2. We will use the geom_histogram function and manipulate the data frame to achieve this.

Introduction

The geom_histogram function is a powerful tool for visualizing data distributions in ggplot2. It creates a histogram that displays the frequency of data points within a given range. However, when working with proportions, we need to adjust our approach to ensure accurate representation.

In this post, we will walk through the process of creating histograms using proportions instead of counts, and provide examples and explanations along the way.

Prerequisites

To follow this tutorial, you will need:

• R or other programming language that can read CSV files
• ggplot2 library installed in your R environment
• scales library for pretty breaks
• dplyr library for data manipulation
• A computer with internet connection to download the example file

Example Data

We are given a data frame fish_data containing length measurements for two species of fish across multiple years. The output of dput() can be accessed at this link: https://drive.google.com/file/d/0BzArRBVtzxttdUtaZWVoNUwzTFU/view?usp=sharing. Due to issues with accessing data, we have added another link to the CSV file: https://drive.google.com/open?id=0BzArRBVtzxttZ2RlcDNKdUFERk0.

Creating a Histogram with Counts

To create a histogram of length frequency for each species and year, we can use the following code:

library(dplyr)
library(ggplot2)
library(scales)

fish_data = read.csv("fish_data.csv", header = T)


ggplot(fish_data, aes(x = Length)) +
  geom_histogram(breaks = seq(0, 700, by = 50), colour = "black") +
  geom_histogram(data = filter(fish_data, Length >= 50 & Length <= 100), breaks = seq(0, 700, by = 50), fill = "red") +
  scale_x_continuous(breaks = pretty_breaks(n=15)) +
  facet_grid(Year~Species) +
  theme_grey() +
  labs(y = "Frequency caught\n", x = "\nLength (cm)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

This code creates a histogram with two bins: one for fish that are at least 50 cm and less than or equal to 100 cm long. The geom_histogram function is used twice: once for the entire data frame and once for the filtered subset of data.

Creating a Histogram with Proportions

To create a histogram using proportions, we need to adjust our approach. We can calculate the proportion of fish in each bin by dividing the count of fish in that bin by the total number of fish. Then, we can multiply this proportion by 100 to convert it to a percentage.

Here is an example code snippet:

library(dplyr)
library(ggplot2)
library(scales)

fish_data = read.csv("fish_data.csv", header = T)


ggplot(fish_data, aes(x = Length)) +
  geom_histogram(aes(y=..count../sum(..count..) * 100, fill = Length >= 50 & Length <= 100),
                 breaks = seq(0, 700, by = 50), position = "dodge") +
  scale_fill_manual(values = c('grey50', 'red'), guide = FALSE) +
  scale_x_continuous(breaks = pretty_breaks(n=15)) +
  facet_grid(Year~Species) +
  theme_grey() +
  labs(y = "Proportion caught\n", x = "\nLength (cm)") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

In this code snippet, we have added the position argument to geom_histogram and set it to "dodge". This tells ggplot2 to place each histogram bin next to its corresponding group. We have also updated the y-axis label to indicate that it represents proportions.

Tips and Variations

• Use scale_y_continuous(breaks = trans_linear()) to ensure linear breaks on the y-axis.
• Use theme(axis.text.y = element_text(size = 10)) to reduce text size for y-axis labels.
• Use facet_grid(Year~Species, scales = "free_x") to allow x-axis scaling in each facet.

Conclusion

In this tutorial, we have explored how to create histograms using proportions instead of counts in ggplot2. We used the geom_histogram function and manipulated the data frame to achieve accurate representation. With these tips and variations, you can now create informative and engaging histograms that showcase your data distributions.

Last modified on 2023-06-19