How can I handle R CMD check "no visible binding for global variable" notes when my ggplot2 syntax is sensible?

How to Handle "No Visible Binding for Global Variable" Notes in R CMD check when Using Sensible ggplot2 Syntax?

✨Are you an R package developer who loves using ggplot2 for creating awesome visualizations? 📊 But every time you run the R CMD check command, you get pesky "no visible binding for global variable" notes? 😱 Don't worry, you are not alone! In this blog post, we will discuss the common issues related to this problem, provide easy solutions, and give you a compelling call-to-action to engage with us. Let's dive in! 💪

The short version 👀

R CMD check throws the note "no visible binding for global variable [variable name]" every time you use sensible plot-creation syntax in ggplot2. This can be frustrating, as it seems to penalize a perfectly valid syntax. You might be wondering how to get your package to pass R CMD check and be admitted to CRAN. 🤔

Background 🌍

A similar issue has been discussed by Sascha Epskamp with the use of the subset() function. The crucial difference is that subset()'s manpage explicitly mentions that it is designed for interactive use. However, in this case, the issue arises from the use of ggplot2's core feature - the data argument.

An example of code that generates these notes 📝

Take a look at the JitteredResponsesByContrast sub-function in the granovaGG package here. The use of ggplot2's data argument triggers the following notes:

granovagg.contr : JitteredResponsesByContrast: no visible binding for global variable 'x.values'
granovagg.contr : JitteredResponsesByContrast: no visible binding for global variable 'y.values'

Why R CMD check is right ❗️

Technically, R CMD check is correct in showing these notes. The variables x.values and y.values are not defined locally within the JitteredResponsesByContrast() function, nor are they pre-defined globally or in the caller function. Instead, they are variables within a dataframe that gets defined earlier and passed into the function.

Why ggplot2 makes it difficult to appease R CMD check 🤷‍♀️

ggplot2 encourages the use of the data argument, which allows you to specify the dataset for your plot. This allows the following code to work:

library(ggplot2)
p <- ggplot(aes(x = hwy, y = cty), data = mpg)
p + geom_point()

However, if you were to run this code, you would encounter an "object-not-found" error:

library(ggplot2)
hwy # a variable in the mpg dataset

Two workarounds, and why I'm happy with neither 🙅‍♂️

The NULLing out strategy 📌

One workaround suggested by Matthew Dowle is to set the problematic variables to NULL in the function. For example:

JitteredResponsesByContrast <- function (data) {
  x.values <- y.values <- NULL
  return(
    geom_point(
      aes(
        x = x.values, 
        y = y.values
      ),
      data     = data,
      position = position_jitter(height = 0, width = GetDegreeOfJitter(jj))
    )
  )
}

While this solution appeases R CMD check, it has some drawbacks:

1. It serves no purpose beyond passing the check.

2. It obscures the real purpose of the code and misleads the expectation of the aes() call.

3. The need to include the NULLing statement for every plot element function becomes confusing and repetitive.

The with() strategy 📌

Another workaround is to use with() to explicitly signal that the variables can be found inside a larger environment. Here's an example:

JitteredResponsesByContrast <- function (data) {
  with(data, {
    geom_point(
      aes(
        x = x.values, 
        y = y.values
      ),
      data     = data,
      position = position_jitter(height = 0, width = GetDegreeOfJitter(jj))
    )
  })
}

Although this solution works, it has its own drawbacks:

1. You still need to wrap every plot element function with a with() call.

2. The with() call can be misleading as you still need to provide the data argument.

Conclusion and Call-to-Action 🎯

Considering the available options, you might be left feeling unsatisfied. Here are the three options you can choose from:

1. Lobby CRAN to ignore the notes, arguing that they are "spurious" as per CRAN policy. However, this will require constant lobbying every time you submit a package.

2. Implement one of the undesirable strategies (NULLing or with() blocks) to fix the code. However, these strategies are not ideal and can be confusing.

3. Engage with us! We would love to hear your thoughts and suggestions on how to handle these "no visible binding for global variable" notes. Together, we can find better solutions and make the R CMD check process easier for all ggplot2 enthusiasts. Let's make R package development in ggplot2 even more enjoyable! 😊

We hope this blog post has shed some light on this common issue faced by package developers using ggplot2. Remember, it's not just about passing R CMD check, but also about writing clean, maintainable, and robust code. Happy coding! 🚀✨