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 analysisSometimes I might post something about R. Links will appear here.
for
loops in R