At the end of this tutorial, there will be instructions to access the gapmider dataset in R and you will be able to use the code in this post to recreate the graphs. If you have not downloaded R or RStudio, consider doing so. To understand how ggplot2 works, we need to familiarize ourselves with its layering system. The goal of this tutorial is to introduce ggplot2’s main features and to encourage you to learn how to integrate this package into your workflow. But I believe that as our skills improve, the workflow becomes streamlined and easier to reproduce and replicate.

Learning how to wrangle data in R or plotting with ggplot2 requires lots of practice.

This does not mean that this process is at first easy. While we could achieve these standards using any of these programs, I think R provides users many benefits, including access to thousands of free packages, including the mighty ggplot2.Īs somebody who has used Excel or Google Sheets extensively to produce graphs, I have learned – the hard way – that using these applications is more time-consuming than analyzing data or producing data visualizations in R. My goal is to encourage students to think about the “reproducibility” and the “replicability” of their research – to borrow Patrick Schloss’ words. In this post, I am not making a case for a particular program or workflow. Many people also use other spreadsheet applications such as Microsoft Excel, Google’s Sheets, or Apple’s Numbers as well as more specialized statistical software programs (e.g., Stata or SPSS). There are many open-source alternatives to R, such as Python. Should students and researchers interested in data science use R, the free software environment for statistical analysis and plotting? The simple answer is no.

R studio tutorial