Creating Dynamic Box Plots with ggplot: A Guide to Plotting Over Time

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In this article, we will explore how to create dynamic box plots using the ggplot library in R that build upon each other over time. We will start by understanding what a box plot is and its purpose, and then move on to creating our first box plot.

What are Box Plots?

A box plot, also known as a box-and-whisker plot, is a graphical representation of the distribution of data. It consists of five main components:

The box: represents the interquartile range (IQR), which is the difference between the 75th percentile and the 25th percentile.
The whiskers: represent the range of values that are considered outliers, which are typically defined as any value that falls outside of 1.5 times the IQR from the box.
The median: represents the middle value of the dataset, which is also known as the second quartile (Q2).
The mean: represents the average value of the dataset.

Box plots provide a quick and easy way to visualize the distribution of data and can be used to compare the distributions of different groups or datasets.

Creating Our First Box Plot

Let’s start by creating our first box plot using ggplot. We have a dataframe with three columns: Course, Week, and m. The Course column represents the name of the course, the Week column represents the week number, and the m column represents the grade in that week.

# Load necessary libraries
library(ggplot2)
library(tibble)

# Create our first box plot
d <- subset(data_manual, select(Course, Week, m))
a <- ggplot(data=d, aes(x=Week, fill=Course)) + 
  geom_boxplot()+
  scale_y_continuous(limits = c(-2, 100), breaks = seq(0, 100, by = 20))+
  xlab('Week') +
  ylab('Grade SE')
print(a)

This will create a simple box plot with the week number on the x-axis and the grade on the y-axis.

Creating Dynamic Box Plots

However, we want to create dynamic box plots that build upon each other over time. To do this, we can use the lapply function in R to split our data into chunks based on the week number, and then create a separate box plot for each chunk using ggplot.

# Load necessary libraries
library(ggplot2)
library(tibble)

# Create our dataframe with course names repeated 1000 times
dat <- data.frame(Course = rep(c("A", "B", "C"), each=1000), 
                  Week = rep(rep(1:10, each=100), 3), 
                  m = runif(3000, 50, 100))

# Split our data into chunks based on the week number
dats <- lapply(1:max(dat$Week), \(i){
  tmp <- subset(dat, Week <= i)
  tmp$plot_week <- i
}

# Combine all chunks back together
dats <- do.call(rbind, dats)

# Create a box plot for each chunk using ggplot
ggplot(data=dats, aes(x=as.factor(plot_week), fill=Course, y=m)) + 
  geom_boxplot()+
  scale_y_continuous(limits = c(0, 100), breaks = seq(0, 100, by = 20))+
  xlab('Week') +
  ylab('Grade')

This will create a dynamic box plot that builds upon each other over time. Each chunk is represented by a separate box plot, and the color of the box represents the course name.

How it Works

The lapply function in R is used to apply a function to each element of an object. In this case, we use lapply to split our data into chunks based on the week number. The first argument to lapply is the index at which to start splitting the data, and the second argument is the function that is applied to each chunk.

The subset function is used to subset the original dataframe dat to get a new dataframe for each chunk. We also add a new column plot_week to this new dataframe to represent the week number of each chunk.

The do.call(rbind, dats) function is then used to combine all chunks back together into a single dataframe. The rbind function is used to stack the chunks on top of each other.

Finally, we use ggplot to create a box plot for each chunk using the geom_boxplot() function. We also add a scale_y_continuous() function to set the limits and breaks of the y-axis.

Conclusion

In this article, we have learned how to create dynamic box plots that build upon each other over time using ggplot in R. We have seen how to use the lapply function to split our data into chunks based on the week number, and then combine all chunks back together to create a single dataframe.

We have also learned how to use the subset function to subset our original dataframe to get new dataframes for each chunk, and how to add a new column to these dataframes to represent the week number of each chunk.

Finally, we have seen how to use ggplot to create box plots for each chunk using the geom_boxplot() function, and how to add a scale_y_continuous() function to set the limits and breaks of the y-axis.

Last modified on 2023-07-06