Plotting Multiple Variables on a Single Plot in ggplot2

Plotting multiple variables on a single plot can be achieved using the ggplot2 package in R. This tutorial will demonstrate how to create a single plot that displays all three variables from your experiment.

Introduction

In this tutorial, we’ll explore how to plot three variables on a single plot using ggplot2. We’ll start by understanding the basics of ggplot2 and then move on to plotting multiple variables.

Installing and Loading ggplot2

Before we begin, make sure you have ggplot2 installed in your R environment. You can install it using the following command:

install.packages("ggplot2")

Load the ggplot2 package into your R environment using the following command:

library(ggplot2)

Understanding the Basics of ggplot2

ggplot2 is a powerful data visualization library in R that provides a grammar-based approach to creating high-quality plots. The core concept of ggplot2 is based on layers, which are used to build complex plots by combining multiple elements such as geometry, aesthetics, and themes.

Creating a Sample Data Frame

For this tutorial, we’ll use the data.frame function to create a sample data frame that represents our experiment’s data. The data frame will have three variables: block, familiarity, prime, and RT.

# Create a sample data frame
AB <- data.frame(block = c("A", "A", "A", "A", "B", "B", "B", "B"),
                   familiarity = c("fam", "fam", "unfam", "unfam",
                                   "fam", "fam", "unfam", "unfam"),
                   prime = c("P", "UP", "P", "UP", "P", "UP", "P", "UP"),
                   RT = c(570.6929, 628.7446, 644.6268, 607.4312,
                          556.3581, 645.4821, 623.5624, 604.4113))

Creating a Single Plot with Multiple Variables

To create a single plot that displays all three variables from our experiment, we can use the ggplot function and specify multiple aesthetics.

# Create a single plot with multiple variables
p_all <- ggplot(AB, aes(x = prime, y = RT, group = interaction(familiarity, block))) +
  geom_line(aes(color = block))

In this code:

• We create a new data frame p_all that represents our experiment’s data.
• We use the ggplot function to create a single plot with multiple variables. The aes function is used to specify the aesthetics, which are:
  • x = prime: specifies the x-axis variable as prime.
  • y = RT: specifies the y-axis variable as RT.
  • group = interaction(familiarity, block): groups the data by the interaction between familiarity and block. This is used to color the lines based on different levels of the third variable.
  • color = block: specifies that the lines should be colored based on the block variable.
• We use the geom_line function to create a line plot.

Customizing the Plot

We can customize the plot by adding additional elements such as titles, labels, and themes.

# Add customizations to the plot
p_all <- ggplot(AB, aes(x = prime, y = RT, group = interaction(familiarity, block))) +
  geom_line(aes(color = block)) +
  labs(title = "Experiment Data", x = "Prime", y = "RT") +
  theme_classic() +
  scale_color_manual(values = c("A" = "blue", "B" = "red"))

In this code:

• We add a title, x-axis label, and y-axis label using the labs function.
• We customize the plot’s theme using the theme_classic function.
• We use the scale_color_manual function to specify custom colors for each level of the third variable.

Conclusion

In this tutorial, we demonstrated how to create a single plot that displays all three variables from our experiment using ggplot2. We explored various ways to customize the plot and added additional elements to enhance its visual appeal.

By following these steps, you can create high-quality plots that effectively communicate complex data insights in R using ggplot2.

Last modified on 2023-11-24