Chapter 3 Reconciling yourself to doing things you’ve been avoiding

Some 30 odd years ago, as an undergraduate, I was told that I would have to use computers in one of my 2nd year courses. The thought filled me with dread and I remember putting off going into the computer room for as long as I could. It was a steep learning curve, and I struggled. As my only previous experience had been with a typewriter, each time the cursor got to the end of the screen I hit the ‘carriage-return’ button. And each machine had an odd white device attached called a mouse, and I had no idea what that did. All this seems quite preposterous now, but imagine embarking on a PhD in biological sciences without reconciling yourself to using a PC. This is practically where we are now with R (R Core Team (2021); Figure 3.1), and a wealth of other emerging and alternative programming platforms.

The ubiquitous R logo. R (and the invaluable GUI RStudio) has become the go to platform for many statistics and figures in biological sciences

FIGURE 3.1: The ubiquitous R logo. R (and the invaluable GUI RStudio) has become the go to platform for many statistics and figures in biological sciences

If you are not already aware of the prominence that R now has in conducting statistical analyses in biological sciences, then you’ve missed out on a great deal. The real point here is not that you can use R, but that R represents a platform on which analyses can be easily repeated, and by saving and making the code available together with your data, you are making your data analyses reproducible. We will see elsewhere the importance of reproducibility in sciences (Baker, 2016), and the critical role this has in transparency and, therefore, best practice.

What is perhaps most remarkable about learning R is the freedom it gives you away from large amounts of commercial software. As a basic programming language it can be used for statistical analysis, drawing figures, conducting geographic information system (GIS) level work, and even as a word processor on which to write your thesis (and indeed this book Xie, 2016).

If you already know all of the above, then you won’t face any major barriers, and you will fit into the new and more transparent world of science with great ease. If the thought of using R fills you with dread, in the same way that using a computer did for me some 30 years ago, then you should reconcile yourself now before you go any further. Learning R will be a steep learning curve, but it will be made easier by the large amount of excellent video tutorials and online courses that are available.

3.1 And learning lots of other stuff

The same principle that I’ve written about above can be extended to all sorts of other aspects of your PhD project. You will have to learn new skills, and some of these will be things that you have previously resisted having to do. Allow yourself to be open to all aspects of learning, and all skills. The act of learning opens new pathways in your brain, and using this ‘muscle’ will facilitate future learning exercises.


Baker M. 2016. 1,500 scientists lift the lid on reproducibility : Nature News & Comment. Nature 533:452–454. DOI: doi:10.1038/533452a.
R Core Team. 2021. R: A language and environment for statistical computing.
Xie Y. 2016. Bookdown: Authoring books and technical documents with R markdown. CRC Press.