R is a programming language that many researchers use to analyse their data. There are many reasons why you should consider learning R (with RStudio) in addition to SPSS, whether you’re working on a third-year project, preparing to submit your next journal article, or somewhere in between.
1. It’s free and open source
Statistics software can be expensive, and they typically use secret algorithms hidden behind menus and buttons. But R is an open source programming language, which means that anyone can download it for free and even take a look at the source code to find out exactly how it works.
2. The functionality of R can be extended using packages
Due to it being an open source project, many contributors have written new functions for R and made them available to the community in a downloadable package. Importantly, the code for many R packages has been peer-reviewed in published journal articles. For example, many researchers use the lme4 package for running mixed-effects models, which was published in the Journal of Statistical Software. This makes R more reliable and trustworthy than the black box algorithms of commercial software.
3. You will have access to the latest statistics techniques
When relying on GUI software, you are largely restricted by the tools the developers decided would be useful. This can become a major obstacle when there is paradigm shift in your field, leaving you without access to the latest methods and wondering whether they will be added in the next major update. However, many of these innovations in statistics are developed using R, with the authors often providing packages to run their new techniques alongside their publications. But if this isn’t the case, then R users can write their own code to implement these new methods.
4. R offers an easy introduction to programming
R is a very forgiving programming language, and RStudio provides many useful tools for identifying errors and finding help with your code. After gaining some experience in R, learning other languages like Python is much easier as they often share similar features.
5. …but it is also powerful enough for complex analyses and simulations
As well as making life easier for novice programmers, R is extremely versatile and can support computationally intensive procedures like simulations, neural networks, and big data analytics.
6. Using scripts allows for easier replication and reproducibility
There is a replication crisis in psychology, with reports that repeating an experiment doesn’t guarantee repeating the results (Open Science Collaboration, 2015). To address this, researchers are taking steps to ensure that their work is reproducible, including their data analysis. Using R makes this extremely easy, since the code used to generate the statistics is saved as a script that can be shared with other researchers.
7. It can save you a lot of time
R makes it easy to automate repetitive tasks that could otherwise take hours to do, and you can easily reuse code for common tasks like counterbalancing, simple calculations (e.g. BMI, visual angles), and reorganising data files.
8. R is famous for its amazing data visualisation tools
Whereas SPSS is infamous for its basic and boring plots, one of the main strengths of R is the powerful ways you can communicate your data through graphics. The ggplot2 package allows you to precisely design effective plots that leave an impression (here are some examples).
9. R can help you learn a lot more about the statistics you rely on in your research
One of the biggest advantages for learning R is that encourages you to learn more about the statistics you are using in your research. Rather than pressing buttons in the order prescribed in your stats bible of choice, the process of tailoring the code for each project will help you reach a deeper understanding of the tools being used.
10. You can use it to create reports and presentations
R markdown allows you to export your results in a wide range of formats, including MS Word documents, PDFs, and interactive slideshow presentations. It is even possible to create complete research reports with figures, statistics, and references, and have R markdown export it as an APA formatted document ready to be submitted. This allows your work to be fully reproducible and avoids the hassle and risks of moving your stats with copy-and-paste.
11. R is a highly sought-after skill that will improve your career opportunities
The ability to work with data effectively is an invaluable tool to any researcher, which is why such a large portion of psychology undergraduate programmes are devoted to it. But outside of academia, R is used by dozens of companies including Google, Microsoft, and Facebook, all of whom have servers overflowing with data and are on the lookout for skilled data scientists to explore it. Having knowledge of both SPSS and R will put you at an advantage.
But there is a downside to R…
Admittedly, there is a massive learning curve when you first start with R, which can be very off-putting to new users with no programming experience. It will take many frustrating hours of staring at the screen wondering why your code doesn’t work, often to find there was a typo on line 2. But it’s worth it! There are tons of free books, cheatsheets, and online resources to help you learn R, as well as a giant community of users ready to offer support when it’s needed.
So, for your next project, try using R!
A contribution by Andrew Jessop, PhD Candidate at the University of Liverpool.