Understanding the Behavior of the table() Function in R

The table() function in R is a fundamental part of statistical analysis and data visualization. It is used to create contingency tables, which display the distribution of categorical variables across different levels of another variable. However, one peculiar behavior of the table() function has been observed by many users: it uses the variable name as the table title.

This behavior can be seen in the example provided in the Stack Overflow question:

# Create a contingency table
table(c("A","A","B"))

A B 
2 1

# Modify the column names and recreate the table
a <- c("A", "A", "B")
table(a)

x
A B 
2 1 

# Use the identity function to remove the variable name from the table title
aux <- function(x) return(table(identity(x)))
aux(a)

In this example, we first create a contingency table using table(c("A","A","B")). We then modify the column names by assigning new values to the vector a, and recreate the table using table(a). As expected, the variable name “x” is used as the title of the new table.

Workaround: Using deparse.level = 0

The author of the Stack Overflow question found a workaround for this issue by using the deparse.level argument in the table() function. The possible values for deparse.level are:

• 1: Deparse to character strings (default)
• 2: Deparse to expression strings
• 0: Do not deparse, return the underlying R object

By setting deparse.level = 0, we can remove the variable name from the table title. Here’s an example:

# Create a contingency table with deparse.level = 0
a <- c("A", "A", "B")
table(a, deparse.level = 0)

A B 
2 1 

In this revised example, we use deparse.level = 0 when calling the table() function. As expected, the variable name is no longer used as the table title.

Understanding the Role of deparse.level

So, what exactly does deparse.level do? In R, variables are objects that can be manipulated and transformed using various functions. When we use the table() function, it performs some level of transformation on the input data to create the contingency table.

The deparse.level argument determines how much of this transformation should occur. By default (deparse.level = 1), the table() function deconstructs the input vector into its constituent elements (i.e., character strings). This is why we see the variable name “A” used as the table title.

By setting deparse.level = 0, we effectively disable this deconstruction process, allowing us to view the underlying R object directly. In this case, that means seeing the contingency table without the variable name in the title.

Additional Applications of deparse.level

The deparse.level argument has applications beyond just modifying table titles. For example:

• Data visualization: When creating plots with libraries like ggplot2 or base graphics, you may want to customize the axis labels. By setting deparse.level = 0, you can remove the default label strings and replace them with custom ones.
• Mathematical calculations: In some mathematical contexts, it’s desirable to preserve the exact expressions involved in computations. By using deparse.level = 0, you can avoid this deconstruction process and work directly with these original expressions.

Example: Removing Axis Labels

Here’s an example of how to remove axis labels when creating a plot using ggplot2:

# Create a sample dataset
set.seed(123)
x <- rnorm(100, mean = 0, sd = 1)
y <- rnorm(100, mean = 1, sd = 1)

# Create the plot
ggplot(data.frame(x = x, y = y), aes(x = x, y = y)) +
  geom_point() +
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

In this example, we use theme(axis.title.x = element_blank()) and theme(axis.title.y = element_blank()) to remove the x-axis title and y-axis title, respectively. This achieves a similar effect as removing table titles using deparse.level.

Conclusion

The table() function in R has a peculiar behavior that uses variable names as table titles. However, by setting deparse.level = 0, we can circumvent this behavior and view the underlying contingency tables directly. This technique also has applications beyond just modifying table titles, such as data visualization and mathematical calculations.

By understanding how to control the deconstruction process using deparse.level, you can tailor your R workflow to suit your specific needs and work with data in a more flexible manner. Whether you’re working with contingency tables or exploring other aspects of statistical analysis, this technique will help you unlock new insights and possibilities in your R work.

Last modified on 2023-12-30