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The stat_summary() function in ggplot2 is used to summarize data and create a visual representation of the summary. It can be used to calculate summary statistics, such as mean or median, for a group of data points and display them as a point, line, or bar on a plot. This function is useful for quickly understanding the overall trend or distribution of data without having to manually calculate and plot the summary statistics. It can also be customized to display different summary measures and add error bars for uncertainty. Overall, the stat_summary() function is a convenient tool for summarizing and visualizing data in ggplot2.
You can use the stat_summary() function in ggplot2 to create visualizations that display summary metrics of specific variables in a data frame.
The following examples show how to use the stat_summary() function in practice with the following data frame in R:
#create data frame df = data.frame(team=rep(c('A', 'B', 'C'), each=4), points=c(8, 12, 4, 6, 26, 21, 25, 20, 9, 18, 14, 14)) #view data frame df team points 1 A 8 2 A 12 3 A 4 4 A 6 5 B 26 6 B 21 7 B 25 8 B 20 9 C 9 10 C 18 11 C 14 12 C 14
Example 1: Use stat_summary() to Visualize Mean Values with Bar Plot
The following code shows how to use the stat_summary() function to visualize the mean value in the points column of the data frame, grouped by the team column:
library(ggplot2)
library(dplyr)
#create bar plot to visualize mean points by team
df %>%
ggplot(aes(x=team, y=points)) +
stat_summary(fun='mean', geom='bar')
The bars in the bar plot represent the mean points value for each unique team value.
Notice that we used the fun argument within stat_summary() to specify the summary function to use and we used the geom argument to specify the geometric shape to use in the plot.
Example 2: Use stat_summary() to Visualize Mean Values with Scatter Plot
The following code shows how to use the stat_summary() function to visualize the mean value in the points column of the data frame, grouped by the team column, using points as the geometric shape:
library(ggplot2)
library(dplyr)
#create plot with points to visualize mean points by team
df %>%
ggplot(aes(x=team, y=points)) +
stat_summary(fun='mean', geom='points')
Notice that we used the geom argument within the stat_summary() function to specify that we’d like to use points as the geometric shape in the plot.
Example 3: Use stat_summary() to Visualize Minimum Values with Bar Plot
The following code shows how to use the stat_summary() function to visualize the minimum value in the points column of the data frame, grouped by the team column:
library(ggplot2)
library(dplyr)
#create bar plot to visualize minimum points by team
df %>%
ggplot(aes(x=team, y=points)) +
stat_summary(fun='min', geom='bar')
Notice that we used the fun argument within the stat_summary() function to specify that we’d like to use the minimum as the summary function.
Additional Resources
The following tutorials explain how to perform other common tasks in ggplot2: