You can use the geometric object geom_boxplot() from ggplot2 library to draw a boxplot() in R. Boxplots() in R helps to **visualize the distribution of the data by quartile and detect the presence of outliers.**

We will use the airquality dataset to introduce boxplot() in R with ggplot. This dataset measures the airquality of New York from May to September 1973. The dataset contains 154 observations. We will use the following variables:

- Ozone: Numerical variable
- Wind: Numerical variable
- Month: May to September. Numerical variable

## Create Box Plot

Before you start to create your first boxplot() in R, you need to manipulate the data as follow:

- Step 1: Import the data
- Step 2: Drop unnecessary variables
- Step 3: Convert Month in factor level
- Step 4: Create a new categorical variable dividing the month with three level: begin, middle and end.
- Step 5: Remove missing observations

All these steps are done with dplyr and the pipeline operator %>%.

library(dplyr) library(ggplot2) # Step 1 data_air <- airquality % > % #Step 2 select(-c(Solar.R, Temp)) % > % #Step 3 mutate(Month = factor(Month, order = TRUE, labels = c(“May”, “June”, “July”, “August”, “September”)), #Step 4 day_cat = factor(ifelse(Day < 10, “Begin”, ifelse(Day < 20, “Middle”, “End”))))

A good practice is to check the structure of the data with the function glimpse().

glimpse(data_air)

**Output:**

## Observations: 153 ## Variables: 5 ## $ Ozone 41, 36, 12, 18, NA, 28, 23, 19, 8, NA, 7, 16, 11, 14, … ## $ Wind 7.4, 8.0, 12.6, 11.5, 14.3, 14.9, 8.6, 13.8, 20.1, 8.6… ## $ Month May, May, May, May, May, May, May, May, May, May, May,… ## $ Day 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,… ## $ day_cat Begin, Begin, Begin, Begin, Begin, Begin, Begin, Begi…

There are NA’s in the dataset. Removing them is wise.

# Step 5 data_air_nona <-data_air %>% na.omit()

## Basic box plot

Let’s plot the basic R boxplot() with the distribution of ozone by month.

# Store the graph box_plot <- ggplot(data_air_nona, aes(x = Month, y = Ozone)) # Add the geometric object box plot box_plot + geom_boxplot()

Code Explanation

- Store the graph for further use
- box_plot: You store the graph into the variable box_plot It is helpful for further use or avoid too complex line of codes

- Add the geometric object of R boxplot()
- You pass the dataset data_air_nona to ggplot boxplot.
- Inside the aes() argument, you add the x-axis and y-axis.
- The + sign means you want R to keep reading the code. It makes the code more readable by breaking it.
- Use geom_boxplot() to create a box plot

**Output:**

### Change side of the graph

You can flip the side of the graph.

box_plot + geom_boxplot()+ coord_flip()

Code Explanation

- box_plot: You use the graph you stored. It avoids rewriting all the codes each time you add new information to the graph.
- geom_boxplot(): Create boxplots() in R
- coord_flip(): Flip the side of the graph

**Output:**

### Change color of outlier

You can change the color, shape and size of the outliers.

box_plot + geom_boxplot(outlier.colour = “red”, outlier.shape = 2, outlier.size = 3) + theme_classic()

Code Explanation

- outlier.colour=“red”: Control the color of the outliers
- outlier.shape=2: Change the shape of the outlier. 2 refers to triangle
- outlier.size=3: Change the size of the triangle. The size is proportional to the number.

**Output:**

### Add a summary statistic

You can add a summary statistic to the R boxplot().

box_plot + geom_boxplot() + stat_summary(fun.y = mean, geom = “point”, size = 3, color = “steelblue”) + theme_classic()

Code Explanation

- stat_summary() allows adding a summary to the horizontal boxplot R
- The argument fun.y controls the statistics returned. You will use mean
- Note: Other statistics are available such as min and max. More than one statistics can be exhibited in the same graph
- geom = “point”: Plot the average with a point
- size=3: Size of the point
- color =“steelblue”: Color of the points

**Output:**

## Box Plot with Dots

In the next horizontal boxplot R, you add the dot plot layers. Each dot represents an observation.

box_plot + geom_boxplot() + geom_dotplot(binaxis = ‘y’, dotsize = 1, stackdir = ‘center’) + theme_classic()

Code Explanation

- geom_dotplot() allows adding dot to the bin width
- binaxis=‘y’: Change the position of the dots along the y-axis. By default, x-axis
- dotsize=1: Size of the dots
- stackdir=‘center’: Way to stack the dots: Four values:
- “up” (default),
- “down”
- “center”
- “centerwhole”

**Output:**

## Control Aesthetic of the Box Plot

### Change the color of the box

You can change the colors of the group.

ggplot(data_air_nona, aes(x = Month, y = Ozone, color = Month)) + geom_boxplot() + theme_classic()

Code Explanation

- The colors of the groups are controlled in the aes() mapping. You can use color= Month to change the color of the box and whisker plot according to the months

**Output:**

### Box plot with multiple groups

It is also possible to add multiple groups. You can visualize the difference in the air quality according to the day of the measure.

ggplot(data_air_nona, aes(Month, Ozone)) + geom_boxplot(aes(fill = day_cat)) + theme_classic()

Code Explanation

- The aes() mapping of the geometric object controls the groups to display (this variable has to be a factor)
- aes(fill= day_cat) allows creating three boxes for each month in the x-axis

**Output:**

## Box Plot with Jittered Dots

Another way to show the dot is with jittered points. It is a convenient way to visualize points with boxplot for categorical data in R variable.

This method avoids the overlapping of the discrete data.

box_plot + geom_boxplot() + geom_jitter(shape = 15, color = “steelblue”, position = position_jitter(width = 0.21)) + theme_classic()

Code Explanation

- geom_jitter() adds a little decay to each point.
- shape=15 changes the shape of the points. 15 represents the squares
- color = “steelblue”: Change the color of the point
- position=position_jitter(width = 0.21): Way to place the overlapping points. position_jitter(width = 0.21) means you move the points by 20 percent from the x-axis. By default, 40 percent.

**Output:**

You can see the difference between the first graph with the jitter method and the second with the point method.

box_plot + geom_boxplot() + geom_point(shape = 5, color = “steelblue”) + theme_classic()

## Notched Box Plot

An interesting feature of geom_boxplot(), is a notched boxplot function in R. The notch plot narrows the box around the median. The main purpose of a notched box plot is to compare the significance of the median between groups. There is strong evidence two groups have different medians when the notches do not overlap. A notch is computed as follow:

with is the interquartile and number of observations.

box_plot + geom_boxplot(notch = TRUE) + theme_classic()

Code Explanation

- geom_boxplot(notch=TRUE): Create a notched horizontal boxplot R

**Output:**

## Summary

We can summarize the different types of horizontal boxplot R in the table below:

Objective | Code |
---|---|

Basic box plot | ggplot(df, aes( x = x1, y =y)) + geom_boxplot() |

flip the side | ggplot(df, aes( x = x1, y =y)) + geom_boxplot() + coord_flip() |

Notched box plot | ggplot(df, aes( x = x1, y =y)) + geom_boxplot(notch=TRUE) |

Box plot with jittered dots | ggplot(df, aes( x = x1, y =y)) + geom_boxplot() + geom_jitter(position = position_jitter(0.21)) |