Box plot in R using ggplot2

A box plot (or box-and-whisker plot) visually summarizes the distribution, central value and spread of a dataset, helping to quickly identify outliers and variability. It consists of the following:

• Box: The box shows the interquartile range (IQR), containing the middle 50% of the data. The lower edge is the 25th percentile (Q1) and the upper edge is the 75th percentile (Q3). This box shows where most data points are concentrated.
• Median: A line inside the box marks the median (Q2), the middle value of the dataset.
• Whiskers: The lines extending from the box (whiskers) show the range of data within a certain distance from the quartiles (usually 1.5 times the IQR).
• Outliers: Data points outside the whiskers are marked as outliers, indicating values far from the rest of the data (typically beyond 1.5 times the IQR).

Why Use a Box Plot

• Central Tendency: It shows where the center of the data lies (median), helping to understand the typical value.
• Spread and Variability: The box and whiskers give an immediate sense of the range and how spread out the values are.
• Skewness: If the median line is closer to the top or bottom of the box, it indicates skewness in the data.
• Outliers: Box plots are excellent for spotting outliers—values that are much higher or lower than the rest of the data.

Plotting Box Plots in R using ggplot2

We can plot a box plot in R programming language using the ggplot2 library.

Syntax:

geom_boxplot(mapping = NULL, outlier.colour = NULL, outlier.shape = 19, outlier.size = 1.5, notch = FALSE)

Parameters:

• mapping: Used to define aesthetic mappings like x, y, fill, or color using aes().
• outlier.colour: Sets the color of the outlier points (if not specified, default color is used).
• outlier.shape: Specifies the shape of the outlier points (e.g., 19 for solid circle).
• outlier.size: Sets the size of the outlier points.
• notch: If TRUE, adds a notch to the box to show a confidence interval around the median.

1. Creating a Basic Box Plot

To create a regular boxplot, we first have to import all the required libraries and datasets in use. Then put all the attributes to plot in ggplot() function along with geom_boxplot.

You can download the dataset from here: Crop_recommendation

• ggplot: Initializes a ggplot2 plot object with dataset and aesthetic mapping.
• aes: Sets aesthetic mappings for x and y axes.
• geom_boxplot: Adds the box plot layer to the chart.

install.packages("ggplot2")
library(ggplot2)

ds <- read.csv("/content/Crop_recommendation.csv", header = TRUE)

ggplot(data=ds, mapping=aes(x=label, y=temperature))+
        geom_boxplot()

 Output: 

2. Adding Mean Value to the Box Plot

To add the mean value on the box plot, we can make use of the stat_summary() function. It enables we to add summary statistics such as the mean, which will be included directly in the plot.

Syntax: 

stat_summary( fun, geom)

• fill: Fills the interior of each box according to group.
• stat_summary: Adds summary statistics like mean or median.
• fun: Function to apply mean.
• geom (in stat_summary): Defines how the summary is displayed.
• shape: Shape of the mean point (e.g., 8 = star).
• size: Size of the point.
• color: Color of the point.

library(ggplot2)

ds <- read.csv("Crop_recommendation.csv", header = TRUE)

ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot() +
  stat_summary(fun = "mean", geom = "point", shape = 8,
               size = 2, color = "white")

Output:

3. Change Legend Position of Box Plot

The position of the legend on the plot is easy to customize with the use of the theme() function. For instance, we can include the legend on top, at the bottom, or suppress it altogether.

• theme: Customizes non-data parts of the plot like background, title, legend.
• legend.position: Changes the position of the legend.

library(ggplot2)
ds <- read.csv("Crop_recommendation.csv", header = TRUE)

ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot() +
  theme(legend.position = "top")

Output:

Explanation: This will put the legend in the top of the plot. The theme() function offers further customizations of plot titles, axes and background.

4. Creating a Horizontal Box Plot

Box plots can also be placed horizontally using coord_flip() function. This function just switches the x and y-axis.

• coord_flip: Flips the axes.

library(ggplot2)
ds <- read.csv("c://crop//archive//Crop_recommendation.csv", header = TRUE)

# Creating a Horizontal Boxplot using ggplot2 in R
ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot() +
  coord_flip()

Output:

5. Changing Box Plot Line Colors

We can change the outline colors of box plots in different ways depending on how we want to represent the grouping variable.

5.1. Default Line Colors by Groups

We can reverse the outline color of the boxes according to a grouping variable. This can be achieved by mapping the color aesthetic onto a variable.

• color: Changes the outline color of each box by group.

crop2<-ggplot(ds, aes(x=label, y=temperature, color=label)) +
  geom_boxplot()
crop2

Output:

5.2. Custom Line Colors

We will use the scale_color_manual() function to specify certain colors for each group to have greater control over the box outline colors.

• scale_color_manual: defines specific colors for each group manually.

ggplot(ds, aes(x = label, y = temperature, color = label)) + 
  geom_boxplot() +
  scale_color_manual(values = c("#999999", "#E69F00", "#56B4E9", "Red", "Green"))

Output:

5.3. Using Brewer Color Palettes

We can change the outline color of the box plot with brewer color palettes. For doing so we just need to use the scale_color_brewer() function and set the palette argument within this function. 

• scale_color_brewer: Applies a predefined ColorBrewer palette to line colors.
• palette: The name of the palette used (e.g., "Dark2").

ggplot(ds, aes(x = label, y = temperature, color = label)) + 
  geom_boxplot() +
  scale_color_brewer(palette = "Dark2")

Output:

6. Fill the Box Plot with color 

We can fill the interior of box plots using solid colors, grouped fills, or custom palettes to improve visual clarity or aesthetics.

6.1. Default Filling

To fill the boxes with color, we can use the fill attribute inside the geom_boxplot() function.

ggplot(data = ds, aes(x = label, y = temperature)) +
  geom_boxplot(fill = 'green')

Output:

6.2. Fill by Group

If we want to fill the boxes with different colors based on the label variable, we can map the fill aesthetic to this variable.

ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2)

Output:

6.3. Custom Fill Colors

To manually specify colors for the fills, use scale_fill_manual().

• scale_fill_manual: Lets we assign custom fill colors to groups.

ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2) +
  scale_fill_manual(values = c("#999999", "#E69F00", "#56B4E9", "Red", "Green"))

Output:

6.4. Using Brewer Color Palettes for Filling

Similar to the outline color, we can use scale_fill_brewer() to apply a color palette to the fill.

ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot(outlier.colour = "black", outlier.shape = 16, outlier.size = 2) +
  scale_fill_brewer(palette = "Dark1")

Output:

6.5. Using Grayscale

To fill color of box plots with grayscale use scale_fill_grey() with theme_classic().

• scale_fill_grey: Applies a grayscale color scheme.
• theme_classic: Uses a clean white background with no grid.

crop3<-ggplot(ds, aes(x = label, y = temperature, fill = label)) + 
  geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=2)

crop3 + scale_fill_grey() + theme_classic()

Output:

7. Adding Jitters in Box Plots

Jitters assist in minimizing over plotting when data points coincide. We can control the location of jittered points using the position_jitter() function.

• geom_jitter: Adds random noise to the data points to avoid overlap.
• position_jitter: Controls how much noise to add (e.g., 0.2 on x-axis).

ggplot(ds, aes(x = label, y = temperature)) + 
  geom_boxplot() +
  geom_jitter(position = position_jitter(0.2))

Output:

8. Notched Box Plot

A notched box plot gives the added information of emphasizing the confidence interval of the median. To plot a notched box plot, use the notch parameter as TRUE.

ggplot(ds, aes(x = label, y = temperature)) + 
  geom_boxplot(notch = TRUE) +
  geom_jitter(position = position_jitter(0.2))

Output: