Skip to contents

Creating reproducible examples

Source: vignettes/articles/guide-3.Rmd
guide-3.Rmd

Introduction

In this guide, we will explore how to create reproducible examples using the reprex package (Bryan et al. 2024) in R. Reproducible examples are essential for effective communication and collaboration among data scientists and statisticians.

Outcomes

  • ...

What is a Reproducible Example?

Reproducible examples are crucial for effectively communicating problems, solutions, and ideas in the world of data science. In this post, we will discuss the importance of reproducible examples and demonstrate how to create them using the reprex package in R.

A reproducible example, often referred to as a "reprex," is a minimal, self-contained piece of code that demonstrates a specific issue or concept. It should include:

  • A brief description of the problem or question
  • The necessary data to reproduce the issue
  • The R code used to generate the output
  • The actual output, including any error messages or warnings

Why Use the reprex Package?

The reprex package in R streamlines the process of creating reproducible examples by:

  • Automatically capturing code, input data, and output
  • Formatting the example for easy sharing on various platforms (e.g., GitHub, Stack Overflow)
  • Encouraging best practices for creating clear and concise examples

Installing and Loading the reprex Package

To get started with the reprex package, first install it from CRAN and load it into your R session:

Creating a Reproducible Example with reprex

In this section, we will demonstrate how to create a reproducible example using the reprex package.

Basic Usage

To create a simple reprex, write your R code and then call the reprex() function:

library(reprex)

code <- '
x <- 1:10
mean(x)
'

reprex(input = code)

This will generate a formatted output that includes the code, input data, and results.

Customizing Output Format

You can customize the output format of your reprex by specifying the venue argument. For example, to create a reprex suitable for GitHub, use:

reprex(input = code, venue = "gh")

Including Data

When your example requires specific data, you can include it using the dput() function:

data <- data.frame(x = 1:10, y = 11:20)
data_dput <- dput(data)

code_with_data <- '
data <- {{ data_dput }}
plot(data$x, data$y)
'

reprex(input = code_with_data)

This will incorporate the data into your reprex, allowing others to reproduce your example easily.

Sharing Your Reproducible Example

Once you have created your reprex, you can share it on various platforms such as GitHub, Stack Overflow, or via email. The formatted output generated by the reprex package ensures that your example is easy to read and understand.

Conclusion

In this blog post, we have discussed the importance of reproducible examples and demonstrated how to create them using the reprex package in R. By creating clear and concise reprexes, you can effectively communicate problems, solutions, and ideas with your peers and collaborators. Give the reprex package a try and see how it can improve your workflow!

References

Datapasta is a package that allows you to copy and paste data frames from RStudio into a reprex. This is a very useful tool for creating reproducible examples. Here is an example of how to use datapasta to create a reprex.

Bryan, Jennifer, Jim Hester, David Robinson, Hadley Wickham, and Christophe Dervieux. 2024. Reprex: Prepare Reproducible Example Code via the Clipboard. https://reprex.tidyverse.org.