Creating a Box Plot in R: A Step-by-Step Guide for Multiple Time Points and Treatments

In this article, we will explore how to create a box plot in R that displays multiple time points with two treatments on the same graph. This type of plot is commonly used in scientific research to visualize the distribution of data across different conditions.

Introduction to Box Plots

A box plot is a graphical representation of the five-number summary: minimum value, first quartile (Q1), median (second quartile, Q2), third quartile (Q3), and maximum value. It provides a quick overview of the central tendency and spread of a dataset.

R Basics: Setting Up for Box Plot Creation

Before we dive into creating our box plot, let’s make sure we have the necessary libraries loaded in R. We’ll be using the ggplot2 package, which is one of the most popular data visualization libraries in R.

# Load the ggplot2 library
library(ggplot2)

Understanding the Problem: Time Points and Treatments

We want to create a box plot that displays three time points (0, 7, and 28) against abundance. The twist is that we have two treatments, which will be nested within each other. This means that for each time point, we’ll have two separate box plots representing the two treatments.

Collecting Data

For this example, let’s assume we have a dataset called data with columns for time points (Time) and abundance (Abundance). We’ll also create a new column Treatment, which will represent our two treatments (e.g., “CO2” and “Temperature”).

# Create a sample dataset
data <- data.frame(
  Time = c(0, 7, 28),
  Abundance = c(10, 20, 30),
  Treatment = c("CO2", "Temperature")
)

Step 1: Merging Data for Nesting

To create our box plot with nested treatments, we need to merge our data into a long format. We’ll use the tidyr package’s pivot_longer() function to achieve this.

# Load the tidyr library
library(tidyr)

# Merge the data for nesting
data <- pivot_longer(data, cols = -Time, names_to = "Treatment", values_to = "Abundance")

Step 2: Creating the Box Plot

Now that our data is in a suitable format, we can create our box plot using ggplot2. We’ll use the geom_boxplot() function to create the individual box plots for each time point and treatment.

# Create the box plot
ggplot(data, aes(x = Time, y = Abundance)) +
  geom_boxplot() +
  labs(title = "Box Plot of Abundance over Time", x = "Time Point", y = "Abundance") +
  theme_classic()

However, since we have two treatments for each time point, this code will only create a single box plot. To fix this, we need to use the facet_wrap() function from ggplot2 to create separate box plots for each treatment.

# Create the facet-wrapped box plot
ggplot(data, aes(x = Time, y = Abundance)) +
  geom_boxplot() +
  labs(title = "Box Plot of Abundance over Time", x = "Time Point", y = "Abundance") +
  theme_classic() +
  facet_wrap(~ Treatment)

This code will create a separate box plot for each treatment at each time point, effectively displaying our desired nested structure.

Tips and Variations

To add labels or annotations to your box plots, you can use the geom_label() function from ggplot2.
If you want to customize the appearance of your box plots (e.g., color scheme, size, etc.), you can adjust the aesthetics using various ggplot2 functions.
To create a more polished look, consider adding a theme or using the theme_classic() function from ggplot2.
If you want to save your plot as an image file (e.g., PNG, PDF), use the ggsave() function.

Conclusion

Creating a box plot in R with multiple time points and treatments is a manageable task once you understand how to work with nested data. By following these steps and tips, you should be able to create high-quality box plots that effectively communicate your data insights.