Boxplots are an essential tool in data analysis, often used for examining the distribution and variability of data. They provide a visual summary of data, displaying the minimum, first quartile (25th percentile), median (50th percentile), third quartile (75th percentile), and maximum values in a dataset. In R, boxplots can be created using base R commands or using packages such as ggplot2.

An integral part of creating boxplots (or any other plot) is the ability to modify the axis labels. Axis labels are crucial for the readability of your graph and the conveyance of accurate information about the data being displayed. This article will delve into how to change the axis labels of a boxplot in R. We will look at methods using both base R and ggplot2.

## Changing Axis Labels of Boxplots Using Base R

In base R, the `boxplot()`

function is used to generate boxplots. The x and y-axis labels can be specified directly in the `boxplot()`

function using the `xlab`

and `ylab`

arguments.

Let’s consider an example where we create a boxplot of `mpg`

(miles per gallon) for each `cyl`

(number of cylinders) group in the `mtcars`

dataset:

```
# load data
data(mtcars)
# create boxplot
boxplot(mpg ~ cyl, data = mtcars,
main = "Boxplot of MPG grouped by number of cylinders",
xlab = "Number of Cylinders",
ylab = "Miles Per Gallon")
```

In the code above, `xlab`

and `ylab`

are used to label the x and y-axes, respectively.

## Changing Axis Labels of Boxplots Using ggplot2

With ggplot2, boxplots can be created using the `geom_boxplot()`

function. You can change the axis labels by using the `labs()`

function, which offers a more flexible way of customizing the labels for the x and y-axes.

Let’s replicate the boxplot we made earlier with ggplot2:

```
# load library
library(ggplot2)
# create boxplot
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot() +
labs(title = "Boxplot of MPG grouped by number of cylinders",
x = "Number of Cylinders",
y = "Miles Per Gallon")
```

Here, the `labs()`

function is used to set the title of the plot (`title`

), x-axis label (`x`

), and y-axis label (`y`

).

## Changing Axis Labels to Represent Factor Levels

Sometimes, your axis labels might correspond to factor levels. For instance, in the `mtcars`

dataset, let’s say we want to create a boxplot of `mpg`

based on the `am`

variable, which represents the type of transmission (0 = automatic, 1 = manual). Instead of having 0 and 1 on the x-axis, we’d like it to show ‘Automatic’ and ‘Manual’. Here’s how you can do it:

```
# create boxplot
ggplot(mtcars, aes(x = factor(am), y = mpg)) +
geom_boxplot() +
labs(title = "Boxplot of MPG based on transmission type",
x = "Transmission Type",
y = "Miles Per Gallon") +
scale_x_discrete(labels = c("Automatic", "Manual"))
```

The `scale_x_discrete()`

function is used to change the x-axis labels to ‘Automatic’ and ‘Manual’.

## Adding and Formatting Axis Labels

What if we want to add axis labels after the plot has been created, or we want to format the labels (like changing the color, size, or font)? In ggplot2, we can use the `theme()`

function along with `element_text()`

to modify these details.

Let’s create a boxplot of `mpg`

grouped by `cyl`

, and then add and format the axis labels:

```
# create boxplot
p <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot() +
labs(title = "Boxplot of MPG grouped by number of cylinders")
# add and format axis labels
p + theme(axis.title.x = element_text(color = "blue", size = 14, face = "bold"),
axis.title.y = element_text(color = "red", size = 14, face = "bold"))
```

In the code above, `theme(axis.title.x = element_text(...))`

is used to change the properties of the x-axis label, and `theme(axis.title.y = element_text(...))`

is used for the y-axis label.

## Conclusion

Changing axis labels is a critical skill in data visualization as it greatly enhances the readability and interpretation of your plots. Whether you’re using base R or ggplot2, there are straightforward methods to alter these labels.