If you’re analysing data (for example, data collected with Kobo Toolbox) and working in Excel is getting horrendous, you need to check out R and R Studio.
You’ll need to learn to program (a bit – I’m halfway through Python for Everybody and it was just about enough), but when you do you’ll be able to write code that processes thousands of lines of data in the blink of an eye.
What is R?
R Studio is a free program that makes it much easier to use R.
What else is it helpful to know before I get started?
- There’s a set of packages* called the tidyverse that make a few tweaks to R, adding extra functionality and updating some R ways of doing things that have become a bit outdated (I couldn’t tell you what those are).
- There’s a great open-source guide to R for Data Science that’s completely free online.
- I’ve used resources (mostly accessed via Google) at Quick-R by Datacamp, Stat545, and Rbloggers.
- It’s also worth checking out The Data Carpentry at The Carpentries (“We teach foundational coding and data science skills to researchers worldwide.”)
- There’s a mythical page that was vaguely ecology related but had a great tone and was very helpful – let me know if you find it.
What are you waiting for? Get data wrangling.
[You may also want to check out Python for Data Analysis]
*Effectively a pre-made program that you can use: “An R package is a collection of functions, data, and documentation that extends the capabilities of base R. Using packages is key to the successful use of R.“