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Building capacity and capability into our teams: Gaining analytical insight with R

When building a multifunctional team to demonstrate the power and potential of data, we think it's important to upskill whenever and wherever possible. In the Data Programme, we're learning all the time, as this post about taking a course in R proves!

Never heard of R? It’s an open source software package that can bring enormous benefits when handling and using data.

R can provide real value to the business and the wider Defra family when dealing with data, opening up boundless opportunities for doing better analysis. It can even help you your personal life, e.g. keeping an eye on how you are doing in your fantasy football league, for instance.

#DefraData’s Tim Ashelford and I recently attended an illuminating and extremely useful Government Statistical Service (GSS) course on R in York. R is a free, open source tool for manipulating and summarising data. The course was a full day excellently run by Martin Ralphs of the GSS Good Practice Team. It was highly interactive with opportunities for questions; small group discussions; hands on coding of R scripts sessions; practical exercises; and their solutions.

You need to have R and RStudio installed. You can then install packages, which provide a lot of additional functionality. Packages act like add-ons, and there are so many in the Comprehensive R Archive Network (CRAN) available to download for all sorts of different types of analysis. One example of a really useful package is shiny, which turns your data into lovely visuals.

For the full list of packages held in CRAN go to

The highlights for me were:

  • finding out about the huge online support network for R, evident when you use your favourite search engine to look for help. You’ll find loads of blogs and articles that describe the best packages for your work. They also provide support when you hit an obstacle.
  • a really fascinating look at data manipulation in R. For example, missing data can be catered for in R – it is coded as NA. You can remove missing values from calculations in many functions. You can therefore analyse data, even if not all the figures are there.

Alternative free courses on R can be found on Data Camp here:

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