# How to Create a Strip Chart in R

Among the various types of data visualizations, strip charts, also known as one-dimensional scatter plots, can offer simple and straightforward data analysis and representation. Strip charts are often used for displaying and comparing values for a single category of data. They are especially useful when you need to compare the distribution of a numeric variable in different categories.

In this tutorial, we will explore how to create a strip chart in R, using built-in R functions as well as popular libraries such as ggplot2.

### Importing Libraries

We will be using the ggplot2 package to create our strip charts, so we need to make sure we have it installed and loaded into our R environment. If you don’t have ggplot2 installed, you can use the install.packages() function to install it.

# Installing the ggplot2 package
install.packages('ggplot2')

library(ggplot2)

### Importing Data

For this tutorial, we will use the built-in mtcars dataset that comes with R. It is a simple, yet rich, dataset that contains various car attributes such as miles per gallon, number of cylinders, horsepower, and so on.

To load the mtcars dataset, use the following code:

# Load the mtcars dataset
data(mtcars)

## Creating a Basic Strip Chart in R

We will start by creating a basic strip chart of the mpg (miles per gallon) attribute against the cyl (number of cylinders) attribute from our mtcars dataset.

In R, a basic strip chart can be created using the stripchart() function. Here’s how you can do it:

# Creating a strip chart
stripchart(mtcars$mpg ~ mtcars$cyl,
main = "Miles Per Gallon vs Number of Cylinders",
xlab = "Number of Cylinders",
ylab = "Miles Per Gallon",
pch = 19)

In the above code:

• mtcars$mpg ~ mtcars$cyl specifies that we want to create a strip chart of the mpg attribute against the cyl attribute.
• main specifies the title of the chart.
• xlab and ylab specify the labels of the x and y axes, respectively.
• pch specifies the shape of the points on the chart. In this case, 19 represents filled circles.

## Enhancing Strip Charts Using ggplot2

While the basic strip chart is useful, it might not always offer the most informative representation, especially for complex datasets. To get a more detailed view, we can use the ggplot2 package, which offers more control and customization options.

### Creating a Strip Chart with ggplot2

The ggplot2 package uses a layering approach to create plots, where you start with an empty canvas and then add elements such as data, axes, legends, and so on.

To create a strip chart with ggplot2, we use the geom_jitter() function. This function adds a bit of random ‘jitter’ to the data points, reducing overlap and making the distribution of the points clearer.

Here’s how you can create a strip chart using ggplot2:

# Creating a strip chart with ggplot2
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_jitter(width = 0.2) +
labs(title = "Miles Per Gallon vs Number of Cylinders",
x = "Number of Cylinders",
y = "Miles Per Gallon")

In the above code:

• ggplot(mtcars, aes(x = factor(cyl), y = mpg)) sets up the empty canvas and maps the cyl attribute to the x-axis and the mpg attribute to the y-axis.
• geom_jitter(width = 0.2) adds the data points with some jitter.
• labs() sets the title and labels for the axes.

With ggplot2, you can add more layers to your plot to make it more informative. For instance, you can add a boxplot layer to your strip chart to provide a summary of the data’s distribution.Here’s how you can do it:

# Creating a strip chart with a boxplot layer
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_jitter(width = 0.2) +
geom_boxplot(alpha = 0.4) +
labs(title = "Miles Per Gallon vs Number of Cylinders",
x = "Number of Cylinders",
y = "Miles Per Gallon")

In the above code, geom_boxplot(alpha = 0.4) adds the boxplot layer to the strip chart. The alpha parameter sets the transparency of the boxplots.

## Conclusion

In this tutorial, we’ve learned how to create a strip chart in R, starting from a basic strip chart and gradually enhancing it using the ggplot2 package. As seen, strip charts provide a clear and simple way to visualize and compare distributions of numeric variables across different categories.

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