How to plot means inside boxplot using ggplot2 in R?

In this article, we are going to see how to plot means inside boxplot using ggplot in R programming language. 

A box plot in base R is used to summarise the distribution of a continuous variable. It can also be used to display the mean of each group. Means or medians can also be computed using a boxplot by labeling points. 

Method 1: Using stat_summary method

The ggplot method in R is used to do graph visualizations using the specified data frame. It is used to instantiate a ggplot object. Aesthetic mappings can be created to the plot object to determine the relationship between the x and y-axis respectively. Additional components can be added to the created ggplot object.

Syntax: ggplot(data = NULL, mapping = aes(), fill = )

Arguments :

• data – Default dataset to use for plot.
• mapping – List of aesthetic mappings to use for plot.

Geoms can be added to the plot using various methods. The geom_boxplot() method in R can be used to add box plots in the plots made. It is added as a component to the existing plot. Aesthetic mappings can also contain color attributes which is assigned differently based on different data frames.

geom_boxplot(alpha = )

The method stat_summary() can be used to add mean points to a box plot. It is used to add components to the made plot. This method saves the calculation of mean before plotting the data. 

sSyntax: tat_summary(fun=mean, geom=)

Arguments : 

• geom – The geometric object to use display the data
• position – The position adjustment to use for overlapping points on this layer

Example:

R

# Library
library(ggplot2)

# defining the columns of the data frame
data_frame <- data.frame(col1=c(rep("A", 10) ,
                                rep("B", 12) ,
                                rep("C", 18)),
                         col2=c( sample(2:5, 10 ,
                                        replace=T) ,
                                sample(4:10, 12 ,
                                       replace=T),
                                sample(1:7, 18 ,
                                       replace=T))
                         )

# plotting the data frame
graph <- ggplot(data_frame,
                aes(x=col1, y=col2, fill=col1)) +
  geom_boxplot(alpha=0.7) +
  stat_summary(fun=mean, geom="point",
               shape=20, color="blue",
               fill="blue")

# constructing the graph
print(graph)

Output

Method 2: Using the aggregate method

Aggregate() method in base R is used to split the data into subsets. It can also be used to compute summary statistics for each of the computed subsets and then return the result in a group by form. 

Syntax: aggregate(x, by, FUN)

Arguments : 

• x – A list or data frame
• by – The list of the column of the data frame to group by
• FUN – The function to apply to x

The boxplot method in R is used to produce box-and-whisker plot(s) of the specified grouped set of values. The boxplot method in R has the following syntax : 

Syntax: boxplot( formula)

Arguments : 

• formula –  formula, such as y ~ grp, where y is a numeric vector of data values

The boxplot can be customised further to add points and text on the plot. 

Syntax: points (x , y , col, pch)

Arguments : 

• x ,y – The coordinates of the points to mark
• col – The colour to plot the points with

R

# defining the columns of the data frame
data_frame <- data.frame(col1=c(rep("A", 10) ,
                                rep("B", 12) ,
                                rep("C", 18)),
                         col2=c( sample(2:5, 10 ,
                                        replace=T) ,
                                sample(4:10, 12 ,
                                       replace=T),
                                sample(1:7, 18 ,
                                       replace=T))

df_col1 <- list(data_frame$col1)

# computing the mean data frame
data_mod <- aggregate(data_frame$col2,
                        df_col1,
                        mean)
# plotting the boxplot
boxplot(data_frame$col2 ~ data_frame$col1)

# calculating rows of data_mod
row <- nrow(data_mod)

# marking the points of the box plot
points(x = 1:row,
       y = data_mod$x,
       col = "red",
       pch = 14
       )

# adding text to the plot
text(x = 1:row,
     y = data_mod$x - 0.15,
     labels = paste("Mean - ", round(data_mod$x,2)),
     col = "dark green")

Output: