Using Variables in Formula Syntax

When working with data manipulation and analysis libraries like doBy in R, it’s often necessary to use formula syntax to define the operations to be performed on your data. However, sometimes you might want to use variables that you’ve defined beforehand instead of hardcoding column names directly into the formula.

In this article, we’ll explore how to achieve this using sprintf(), paste(), and glue() functions in R.

Understanding Formula Syntax

Before diving into the solution, let’s quickly review how formula syntax works in R. When you use a function like doBy::summary_by(), it expects a formula that defines the grouping and calculation to be performed on your data.

The formula syntax is typically written using the ~ operator, followed by the variable(s) to be included in the calculation. For example:

mpg ~ gear + am

This formula tells R to calculate the mean of the mpg column for each group defined by the combination of gear and am columns.

Using Variables in Formula Syntax

Now that we understand the basics of formula syntax, let’s discuss how to use variables instead of hardcoded column names. The key insight here is that you can’t directly embed variable names into the formula syntax using standard evaluation (e.g., non-standard evaluation).

However, there are workarounds:

Using sprintf() or paste()

One approach is to use string formatting functions like sprintf() or paste() to construct a formula string at runtime. Here’s an example using sprintf():

library(data.table)
library(doBy)

mtcars = data.table(mtcars)

variable1 = "gear" # which is a column name of mtcars
variable2 = "am" # which is a column name of mtcars
variable3 = "mpg" # which is a column name of mtcars

doBy::summary_by(data = mtcars, 
                 as.formula(sprintf("%s ~ %s + %s", variable3, variable1, variable2)), 
                 FUN = "mean")

In this example, we use sprintf() to create a formula string that includes the values of variable1, variable2, and variable3. This way, R will interpret the resulting formula at runtime.

Another alternative is to use paste() instead:

library(data.table)
library(doBy)

mtcars = data.table(mtcars)

variable1 = "gear" # which is a column name of mtcars
variable2 = "am" # which is a column name of mtcars
variable3 = "mpg" # which is a column name of mtcars

doBy::summary_by(data = mtcars, 
                 as.formula(paste(variable3, "~", variable1, "+", variable2)), 
                 FUN = "mean")

Using glue() (available in R 4.0+)

If you’re using R 4.0 or later, you can also use the glue package to achieve similar results:

library(data.table)
library(doBy)
library(glue)

mtcars = data.table(mtcars)

variable1 = "gear" # which is a column name of mtcars
variable2 = "am" # which is a column name of mtcars
variable3 = "mpg" # which is a column name of mtcars

doBy::summary_by(data = mtcars, 
                 as.formula(glue("{variable3} ~ {variable1} + {variable2}")), 
                 FUN = "mean")

In this example, we use the glue function to create a formula string that includes the values of variable1, variable2, and variable3. The resulting string is then passed to doBy::summary_by().

Conclusion

Using variables in formula syntax can be tricky, but there are several workarounds available. By using functions like sprintf(), paste(), or glue(), you can create formula strings at runtime that include values from your variables. This approach allows for more flexibility and reusability in your R code.

In summary:

• Use sprintf() or paste() to construct a formula string using variable names.
• If you’re using R 4.0 or later, consider using the glue package for a more elegant solution.

By following these tips, you’ll be able to use variables in your formula syntax with ease and take advantage of R’s powerful data manipulation capabilities.

Last modified on 2023-12-03