Reproducible road safety research with R16 Dec 2020

A practical introduction

With the rapid developments of data and data science in the road safety sector, it is vital that road safety analysts are able to develop the skills and methods needed to take road safety data and do more with it, more easily, more affordably, more efficiently and more readily.

That’s why the RAC Foundation commissioned Dr Robin Lovelace – associate professor of transport data science – to author this manual to learning R for road safety analysis. Here he talks about his work:

The statistical programming language R is a leading way into the world of data science, and this self-help training manual takes the reader from no R experience to being able to do the regular tasks of a road safety analysis entirely using R.  It covers installation, setup, the basics of the language, packages, getting STATS19 data, GIS functions, data visualisation, and handling dates and times, alongside stories of how professionals have learnt R themselves and incorporated it into their own analytical workflows.  It is accessible enough to be useful to the newcomer, but detailed enough for the experienced user to use as a reference.

R allows you to code analysis for reproducible research; reproducible in the sense that others can check and verify it as well as borrow, share and adapt it to their own work. Analysts can also repeat their own work as fresh data becomes available – there’s no need to recreate the wheel for each iteration of same analysis on new data. The openness, efficiency and power of working in R offers the opportunity, if taken, to improve how road safety analysis gets done, and we think this manual is great way to get you started.