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How can you create a donut chart in R?

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Donut charts are a type of pie chart that have a hole in the center, creating a ring-shaped visualization. They can be useful for showing the proportions of different categories or groups in a dataset, as well as highlighting the differences between them. In this article, you will learn how to create a donut chart in R using the ggplot2 and ggforce packages, as well as how to customize its appearance and add labels.

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation

1 Install and load packages

The first step is to install and load the packages that you will need to create a donut chart in R. You can use the install.packages() function to install them, and the library() function to load them. The ggplot2 package is a popular and powerful tool for creating various types of plots in R, based on the grammar of graphics. The ggforce package is an extension of ggplot2 that provides some additional geometries and features, including the geom_arc_bar() function that you will use to create the donut chart.

# Install packages
install.packages(c("ggplot2", "ggforce"))
# Load packages
library(ggplot2)
library(ggforce)

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation
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Ensure you have the necessary tools—it's like gathering your art supplies. Install and load the required packages. In this case, you might want ggplot2 and dplyr. install.packages("ggplot2") install.packages("dplyr") library(ggplot2) library(dplyr)

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2 Prepare the data

The next step is to prepare the data that you want to visualize in a donut chart. For this example, you will use a built-in dataset called mtcars, which contains information about 32 cars, such as miles per gallon, number of cylinders, horsepower, and weight. You will create a donut chart that shows the distribution of cars by the number of cylinders, which is a factor variable with four levels: 4, 6, 8. You will also calculate the percentage of each level and store it in a new column called pct. You can use the dplyr package to manipulate the data, or use base R functions.

# Load dplyr package
library(dplyr)
# Group the data by number of cylinders and count the frequency
data <- mtcars %>%
  group_by(cyl) %>%
  summarise(freq = n())
# Calculate the percentage of each level
data$pct <- data$freq / sum(data$freq) * 100

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation
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Organize your data—it's like arranging your ingredients. Prepare a dataset with the necessary information. For example: data <- data.frame(category = c("A", "B", "C", "D"), value = c(20, 30, 15, 35))

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Samo Ola Daoud

Graduating student at LiU| MSc. Computational Social Sciences| Data Analyst
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The above code calculated the frequency and percentage of each level of the "cyl" variable in the dataset using the (dplyr) package. A small modification i suggest in the last line when trying to create a new column to calculate the percentage of each level, i prefer using "mutate" function within a pipeline .. Just to avoid any possible issues related to data types.

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3 Create the donut chart

The third step is to create the donut chart using the ggplot2 and ggforce packages. You can use the ggplot() function to initialize a plot object, and specify the data and the aesthetic mappings. Then, you can use the geom_arc_bar() function from the ggforce package to create the ring-shaped bars that represent the number of cylinders. You need to provide four arguments to this function: alpha, which is the starting angle of the arc; beta, which is the ending angle of the arc; r0, which is the inner radius of the arc; and r, which is the outer radius of the arc. You can use the coord_polar() function to convert the plot to a polar coordinate system, and set the theta argument to "x" to map the x-axis to the angle of the arc. You can also use the theme() function to customize the appearance of the plot, such as removing the axis labels and ticks, and adding a title.

# Create the donut chart
donut_chart <- ggplot(data, aes(x = factor(1), y = pct, fill = factor(cyl))) +
  geom_arc_bar(aes(alpha = -cumsum(pct) + pct / 2 + 90, 
                   beta = -cumsum(pct) + 90, 
                   r0 = 0.5, 
                   r = 1)) +
  coord_polar(theta = "x") +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(hjust = 0.5)) +
  ggtitle("Distribution of cars by number of cylinders")
# Print the donut chart
donut_chart

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Mariyanaa Swetha Diviyanathan

MBA | B.S-Instrumentation & Controls Engineering | Google Data Analytics Certified | Power BI Analyst | Data Visualization Specialist | Driving data-informed decisions for sustainable business growth.
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Quick & easy 4 step process : 1.Install and Load Required Packages: R code install.packages("ggplot2") library(ggplot2) 2.Create Data for the Donut Chart: R code data <- data.frame(category = c("Category 1", "Category 2", "Category 3"), value = c(30, 45, 25)) 3.Calculate Cumulative Percentages: R code data <- transform(data, end = cumsum(value), start = c(0, head(end, n = -1))) 4.Create Donut Chart using ggplot2: R code ggplot(data, aes(x = 2, y = value, fill = category)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar(theta = "y") + xlim(c(1, 3)) + theme_void() Assumption: Data represented in Percentage.

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation
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Craft your masterpiece—it's like shaping your sculpture. Use ggplot to create the donut chart. ggplot(data, aes(x = 2, y = value, fill = category)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar(theta = "y") + theme_void() + theme(legend.position = "none")

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Mustapha Mekhatria, MBA

Project Manager at Highsoft AS
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Feel free to use highcharter library: highchart() %>% hc_add_series(type = "pie", data = df, hcaes(x = A, y = B), startAngle = -90, endAngle = 90) %>% hc_tooltip(pointFormat = '<b>{point.percentage:.1f}%</b>') %>% hc_title(text = "ABC", margin = 20, style = list(color = "#144746", useHTML = TRUE))

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Samo Ola Daoud

Graduating student at LiU| MSc. Computational Social Sciences| Data Analyst
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"ggplot2" package used to create a donut chart. I prefer to use "geom_bar" function to create the donut chart which is a standard function in "ggplot2" with a specific set of parameters instead of "geom_arc_ba".

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4 Add labels

The fourth step is to add labels to the donut chart, to make it easier to read and interpret. You can use the geom_text() function from the ggplot2 package to add text labels to the plot. You need to provide three arguments to this function: x, which is the x-coordinate of the label; y, which is the y-coordinate of the label; and label, which is the text of the label. You can also use the aes() function to map these arguments to variables in the data, and use the paste() function to combine the number of cylinders and the percentage in the label. You can also use the hjust and vjust arguments to adjust the horizontal and vertical alignment of the label, and the size argument to change the font size.

# Add labels to the donut chart
donut_chart <- donut_chart +
  geom_text(aes(x = factor(1), 
                y = pct / 2 + cumsum(pct) - pct / 2, 
                label = paste(cyl, "cylinders\n", round(pct, 1), "%")), 
            hjust = 0.5, 
            vjust = 0.5, 
            size = 3)
# Print the donut chart with labels
donut_chart

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation
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Label your creation—it's like signing your artwork. Add labels to enhance understanding. ggplot(data, aes(x = 2, y = value, fill = category)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar(theta = "y") + theme_void() + theme(legend.position = "none") + geom_text(aes(label = paste(category, ": ", value, "%")), position = position_stack(vjust = 0.5), size = 4)

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5 Change colors

The fifth step is to change the colors of the donut chart, to make it more appealing and distinctive. You can use the scale_fill_manual() function from the ggplot2 package to specify the colors that you want to use for the fill aesthetic, which corresponds to the number of cylinders. You can either use a vector of color names, or a vector of hexadecimal codes, to assign a color to each level of the factor variable. You can also use the name argument to change the label of the legend, and the guide argument to remove the legend altogether, if you prefer.

# Change colors of the donut chart
donut_chart <- donut_chart +
  scale_fill_manual(values = c("#F8766D", "#00BFC4", "#C77CFF"), 
                    name = "Number of cylinders",
                    guide = FALSE)
# Print the donut chart with colors
donut_chart

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Gerald Tesha

Data Officer @ HETA | Data Visualization | Monitoring and Evaluation
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Apply your preferred color palette—it's like selecting the right hues. Customize the colors of your donut chart. ggplot(data, aes(x = 2, y = value, fill = category)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar(theta = "y") + theme_void() + theme(legend.position = "none") + geom_text(aes(label = paste(category, ": ", value, "%")), position = position_stack(vjust = 0.5), size = 4) + scale_fill_manual(values = c("#E41A1C", "#377EB8", "#4DAF4A", "#FF7F00"))

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Soma Bhatnagar

Data Scientist/Statistician
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Pros: - Donut chart can replace a pie chart. It lets viewers focus more on the arc lengths to understand the proportion. - The inner circle of donut chart can be utilized for data labeling. Cons: - If we have too many groups to visualize, then the chart becomes cluttered and non informative.

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How can you create a donut chart in R?

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2

3

4

5

6

1 Install and load packages

2 Prepare the data

3 Create the donut chart

4 Add labels

5 Change colors

6 Here’s what else to consider

Data Visualization

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How can you create a donut chart in R?

1

2

3

4

5

6

1 Install and load packages

2 Prepare the data

3 Create the donut chart

4 Add labels

5 Change colors

6 Here’s what else to consider

Data Visualization

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