Ode to R

Posted by Jessica Devitt

No face

R, many a poem I have written

About our tumultuous relationship-swaying from completely smitten, to recoiling, as though I had been bitten,

Your powers of statistical analysis have had me speechless and in awe, but then you misunderstand me and now I am angry and raw,

One day we click, the next I have lost my wick thinking you are a complete…well you get the picture.

The above poem is a more measured example of my current love/hate relationship with the statistical software programme called R.

R began its development in the early 1990’s right here at the University of Auckland.  This programme is incredibly powerful, with the ability to compute huge datasets, administer an astonishing range of analyses and it is free!  The community surrounding R continue to develop new packages that you can easily install for basically any type of data analysis, manipulation and mining that you could imagine.  And this is fantastic!

Desk flip

However, with a heavy head I have to now insert the ominous ‘BUT”.  For a layperson like myself, with mediocre statistical understanding it can be a daunting undertaking to feed in your painstakingly gathered data, ascertain what statistical analysis to do and then make that happen in R.  Further, the all-important, “do you want graphs with that?” is a whole other story.

R is run using programming language, specifically text-based ‘S’ language, thus you need to write exactly what you want it to do in the language that it ‘understands’, and like learning any new language, this is a relatively slow process, especially when some of the language terms seem counter-intuitive.

This is not a story of failure however, this is a story of redemption, as I had never used the program before and now I can.  For someone that was not ‘bright enough’ to sit School Certificate maths (now NCEA Level 1), who cried at my math tutor’s house once a week and vehemently declared that “I will never be good at maths”!  I can now run a range of tests, understand (mostly) the output and have a fair idea of what to do when the dreaded red error writing is returned.  I have achieved this through the help of my peers, excellent sites such as Stackoverflow, Statmethods and a range of journal articles, books and just good old Google.

Happy computer

The moral of the story here is: don’t be scared off by R, it is worth the initial, and at times reoccurring frustration, and if I can do it anyone can.

Helpful Information

Rlogo R Software: https://www.r-project.org/

UNi logoUniversity of Auckland: https://www.stat.auckland.ac.nz/en/about/our-research/software-development/sw-the-r-project.html


Logan, Murray.  Biostatistical design and analysis using R: a practical guide. John Wiley & Sons, 2011

 De Vries, A., & Meys, J. (2012). R for dummies. John Wiley & Sons.

Jessica jessDevitt is a MSc student at the Centre for Biodiversity & Biosecurity, School of Biological Sciences, University of Auckland. She is researching the potential host-range of the hadda beetle in Auckland to assess how it might impact on native ecology. She is supervised by Margaret Stanley

3 thoughts on “Ode to R

  1. I 100% recommend this book by Andrew Beckerman. Our entire lab used it with zero prior experience with R. We now plot and analyse all our data with R. It’s a great beginners resource. http://www.amazon.co.uk/Getting-Started-An-introduction-biologists/dp/0199601623

    Andrew is an ecologist so understands the problems with biological data, and of course an Ecologist’s bad habits!

    I also recommend rseek.org, a search engine that limits results to those about R. Can be better than trawling through stackoverflow.

    Liked by 1 person

  2. Pingback: Celebrating one year of Ecology Ngātahi | Ecology Ngātahi

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