Plotting a Proportion Bar Chart Using ggplot2

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In this article, we will explore how to create a proportion bar chart using the popular data visualization library, ggplot2. We will delve into the details of what it means to have a proportion bar chart, and provide examples of how to achieve this using ggplot2.

What is a Proportion Bar Chart?

A proportion bar chart is a type of bar chart that displays the relative size or proportion of different categories within a dataset. In a proportion bar chart, each bar represents a category in the dataset, and the height of the bar corresponds to the proportion of that category within the total.

For example, let’s say we want to create a proportion bar chart showing the GDP contribution of countries in different continents. We would have one bar for each continent, and the height of the bar would represent the proportion of the continent’s GDP to the world’s GDP.

Preparing the Data

Before we can create a proportion bar chart using ggplot2, we need to prepare our data. The dataset should contain two main columns: continent (or country) and gdp. The continent column represents the category for which we want to display the proportion, and the gdp column represents the size of each category.

In this example, we are using the gapminder dataset, which contains information about countries around the world. We will filter the data to only include observations from 2007, and then calculate the GDP contribution for each country by multiplying its population by its GDP per capita.

# Load the necessary libraries
library(ggplot2)
library(dplyr)

# Filter the gapminder dataset to only include observations from 2007
gapminder_2007 <- gapminder %>%
  filter(year == 2007)

# Calculate the GDP contribution for each country
gapminder_2007 %>%
  mutate(gdp = pop * gdpPercap)

Creating a Proportion Bar Chart Using ggplot2

To create a proportion bar chart using ggplot2, we can use the geom_bar() function, but with a twist. Instead of using the fill aesthetic to specify the color of each bar, we will use it to specify the data that will be displayed on the y-axis.

# Create a proportion bar chart
gapminder_2007 %>%
  mutate(gdp = pop * gdpPercap) %>% 
  ggplot() + 
    geom_bar(mapping = aes(x = continent, weight = sum(gdp), fill = country)) +
    guides(fill = FALSE) +
    theme_bw()

In this code snippet, we first calculate the GDP contribution for each country using the mutate() function. Then, we create a proportion bar chart using the ggplot() function and the geom_bar() function. The mapping argument specifies that we want to display the continent on the x-axis, the sum of the GDP contributions on the y-axis, and the country on the fill aesthetic.

However, this code snippet will not produce the desired output. This is because the weight argument in the geom_bar() function expects a vector of numeric values, but we are passing it a data frame with two columns (continent and gdp).

Correcting the Code

To correct the code snippet, we need to specify which column of the data frame should be used as the y-values for the bar chart. We can do this by specifying the y argument in the mapping aesthetic.

# Create a proportion bar chart
gapminder_2007 %>%
  mutate(gdp = pop * gdpPercap) %>% 
  ggplot() + 
    geom_bar(mapping = aes(x = continent, y = sum(gdp), fill = country)) +
    guides(fill = FALSE) +
    theme_bw()

In this corrected code snippet, we specify the y argument as sum(gdp), which means that for each bar, we will use the sum of the GDP contributions for all countries in the specified continent.

However, this will still not produce the desired output. This is because the fill aesthetic expects a factor or a character vector, but we are passing it a data frame with two columns (continent and gdp).

Correcting the Data

To correct the data, we need to ensure that the country column in our dataset is a factor or a character vector. We can do this by specifying the country column as the factor() function.

# Convert the country column to a factor
gapminder_2007 %>%
  mutate(country = factor(country))

In this code snippet, we use the factor() function to convert the country column to a factor. This will ensure that the country column is a character vector with no missing values.

Final Code Snippet

With the data corrected, we can now provide the final code snippet for creating a proportion bar chart using ggplot2.

# Create a proportion bar chart
gapminder_2007 %>%
  mutate(gdp = pop * gdpPercap) %>% 
  ggplot() + 
    geom_bar(mapping = aes(x = country, y = sum(gdp), fill = continent)) +
    guides(fill = FALSE) +
    theme_bw()

In this final code snippet, we specify the country column as the x-values for the bar chart, the sum of the GDP contributions as the y-values, and the continent column as the fill aesthetic.

The resulting plot will display a proportion bar chart showing the GDP contribution of each country in different continents. Each bar represents a continent, and the height of the bar corresponds to the proportion of that continent’s GDP to the world’s GDP.

Alternative Solution: Using a Treemap

However, creating a proportion bar chart can be tricky, especially when dealing with large datasets or multiple categories. In this case, an alternative solution is to use a treemap, which can provide a more intuitive and visually appealing representation of proportional data.

To create a treemap using the treemap function from the ggplot2 package, we need to specify the data that will be used for the x-axis, y-axis, and fill aesthetic. We also need to specify the algorithm to use for the treemap, such as “squarified” or “recursive”.

# Load the necessary libraries
library(ggplot2)
library(treemap)

# Create a treemap
gapminder_2007 %>%
  filter(year == 2007) %>% 
  mutate(gdp = pop * gdpPercap) %>% 
  treemap(., c("continent", "country"), "gdp", algorithm = "squarified")

In this code snippet, we specify the continent and country columns as the x-axis and y-axis, respectively, and the gdp column as the fill aesthetic. We also specify the “squarified” algorithm to use for the treemap.

The resulting plot will display a treemap showing the GDP contribution of each country in different continents. Each rectangle represents a continent, and its size corresponds to the proportion of that continent’s GDP to the world’s GDP.

Conclusion

In conclusion, creating a proportion bar chart using ggplot2 can be challenging, especially when dealing with multiple categories or large datasets. However, by specifying the correct data and aesthetics, we can achieve a visually appealing representation of proportional data. Alternatively, using a treemap can provide an intuitive and effective way to display proportional data, especially for complex datasets.

By following this article, you should now have a better understanding of how to create a proportion bar chart using ggplot2 and when to use alternative solutions such as treemaps.

Last modified on 2024-06-16