R is a free, open source programming language designed for statistical analysis and graphics. Its core user base has long been statisticians and data miners, but is rapidly gaining popularity in increasingly diverse areas. I started actively learning and using the language in early 2015, and it has completely taken over my approach to analysis of data. In fact, I’m such a fan that I intend to devote a small corner of my website to showcasing its usefulness for scientists like me.
ggplot2, the graphics are good quality from the start, logical in the way that they’re built up (once you understand the logic, anyway) and easy to customise to personal preference.
If I had to list any disadvantages, I would say that even though I think R is easy to learn, it is not always intuitive, and the official documentation is often hard to understand for less familiar users – I at least find it far easier to pick apart an example to find out how something works.
I use R extensively for data analysis, but also for visualisation/communication purposes. Examples are included on this website.
I have also created a page for visualising statistics relating to the 2020 coronavirus (COVID-19) outbreak in Sweden, using R to do all the data analysis and visualisation.
arbintools – a package for data importation, analysis and plotting for users of Arbin battery cycling instruments (not pursued any longer)
impedanceR - a package for simulating impedance spectra of equivalent circuits.
Here I hope to share some R tips based on things that I’ve found very useful. I hope that others will find them useful too! One thing I would thoroughly recommend, however – get RStudio, which is a free IDE for R and makes using it much easier!
lapply()for batch data analysis
Sometimes I might post something about R. Links will appear here.
forloops in R