Introduction

Plotting multiple box-plots using columns of a dataframe in R is a common task, especially when dealing with categorical data and multiple variables. In this article, we will explore how to achieve this task efficiently and effectively.

We’ll start by explaining the basics of box-plots, followed by an overview of the provided solution. We’ll then break down the code and explain each step in detail, providing additional context and examples where necessary.

Understanding Box-Plots

A box-plot is a graphical representation of data that displays the five-number summary: minimum value, first quartile (Q1), median (second quartile, Q2), third quartile (Q3), and maximum value. The box-plots are used to compare the distribution of different groups or variables.

The plot consists of the following components:

• Median: The middle value in the data set.
• Quartiles: The first quartile (Q1) is the median of the lower half, while the third quartile (Q3) is the median of the upper half. These values are used to calculate the interquartile range (IQR).
• Box: The box represents the IQR, with a line inside indicating the median.
• Whiskers: The whiskers extend from the ends of the box to show the range of the data.

The Provided Solution

The provided solution uses the sapply function over a vector of column numbers and subsets mydata to the column of interest within the function. This approach allows for easy access to the correct column name to be added to the plot later.

To adjust the y-axis scale to accommodate the range of the variable, we can use the outline=FALSE argument when specifying the boxplot call.

Breaking Down the Code

Let’s break down the provided code into smaller sections and explain each step in detail:

Section 1: Initialization

par(mfrow=c(3,3), mar=c(3, 3, 0.5, 0.5), mgp = c(1.5, 0.3, 0), tck = -0.01,
oma=c(0, 0, 1, 0))

This code initializes the plot with a specified layout (3x3 grid) and margins. The mgp argument is used to adjust the margins between panels, while the tck argument sets the tick length for the x-axis.

Section 2: Looping Over Columns

sapply(seq_along(mydata)[-1], function(i) {
  y <- mydata[, i]
  # ...
})

This code uses sapply to iterate over a vector of column numbers (excluding the first column). The loop assigns each column to the variable y.

Section 3: Plotting Box-Plot

boxplot(y ~ mydata$categ, outline=FALSE, ylab="VarLevel", tck = 1.0,
         names=c("categ1","categ2"), las=1)

This code plots the box-plot using the boxplot function. The outline=FALSE argument removes outliers from the plot, while the ylab argument sets the y-axis label.

Section 4: Adding Points and Calculating p-values

points(y ~ jitter(mydata$categ, 0.5),
       col=ifelse(mydata$categ==1, 'firebrick', 'slateblue'))
test <- wilcox.test(y ~ mydata$categ)
pvalue <- test$p.value
pvalueformatted <- format(pvalue, digits=3, nsmall=2)
mtext(paste(colnames(mydata)[i], " p = ", pvalueformatted), side=3,
      line=0.5, at=0.9, cex = 0.6)

This code adds points to the plot using points, which creates a jittered version of the data. The ifelse statement is used to color the points based on the categorical value.

The loop also calculates the p-value for each column using the wilcox.test function and formats it with three digits.

Section 5: Finalizing the Plot

})

This code closes the sapply loop, ending the plotting process.

Conclusion

In this article, we explored how to plot multiple box-plots using columns of a dataframe in R. We broke down the provided solution into smaller sections and explained each step in detail.

The key takeaways from this article are:

• Use sapply to iterate over a vector of column numbers and subset the data frame.
• Set outline=FALSE when calling boxplot to remove outliers and adjust the y-axis limits accordingly.
• Calculate p-values using the wilcox.test function and format them with three digits.

By following these steps, you can create an efficient and effective plot of multiple box-plots for your R project.

Last modified on 2023-06-04