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An introduction to road safety analysis with R: setup notes

If you are not experienced with R, it is strongly advised that you read-up on and more importantly test out R and RStudio before attempting analyse road crash data with R.

To read up on R, we recommend reading Chapter 1 Getting Started with Data in R of the online book Statistical Inference via Data Science, which can be found here: https://moderndive.netlify.app/1-getting-started.html

Reading sections 1.1 to 1.3 of that book and trying a few of the examples are considered essential prerequisites, unless you are already experienced with R.

Optionally, if you want a more interactive learning environment, you can try getting started with online resources, such as those found at education.rstudio.com/learn.

And for more information on how R can be used for transport research, the Transportation chapter of Geocomputation with R is a good place to start: https://r.geocompx.org/transport.html

Your computer should also have the necessary software installed.

To ensure your computer is ready for the course, you should have a recent (3.6.0 or later) version of R or RStudio installed. You should have installed packages stats19, tidyverse and a few others shown below. To check you have the necessary packages installed, try running the following line of code:

source("https://git.io/JeaZH")

That does some basic checks. For more comprehensive checkes, and to get used to typing in R code, you can also test your setup by typing and executing the following lines in the RStudio console (this will install the packages you need if they are not already installed):

install.packages("remotes")
pkgs = c(
  "pct",         # package for getting travel data in the UK
  "sf",          # spatial data package
  "stats19",     # downloads and formats open stats19 crash data
  "stplanr",     # for working with origin-destination and route data
  "tidyverse",   # a package for user friendly data science
  "tmap"         # for making maps
)
remotes::install_cran(pkgs)
# remotes::install_github("ITSLeeds/pct")

To test your computer is ready to work with road crash data in R, try running the following commands from RStudio (which should result in the map below):

library(stats19)
library(tidyverse)
library(tmap) # installed alongside mapview
crashes = get_stats19(year = 2022, type = "ac")
crashes_iow = crashes %>% 
  filter(local_authority_district == "Isle of Wight") %>% 
  format_sf()
  
# basic plot
plot(crashes_iow)

You should see results like those shown in the map here: https://github.com/ropensci/stats19/issues/105

If you cannot create that map by running the code above before the course, get in touch with us, e.g. by writing a comment under that github issue page (Note: You will need a github account).

Time

Perhaps the most important pre-requisite is time. You’ll need to find time to work-through these materials, either in one go (see suggested agenda below) or in chunks of perhaps 1 hour per week over a 2 month period. I think ~8 hours is a good amount of time to spend on this course but it can be done in small pieces, e.g.:

2 day course agenda

For the more structured 2 day course for R beginners, a preliminary agenda is as follows:

Day 1: An introduction to R and RStudio for spatial and temporal data

09:00-09:30 Arrival and set-up

09:30-11:00 Introduction to the course and software

Break

11:15-12:30 Working with temporal data

Lunch

13:30-15:00 Working with spatial data

Break

15:15-15:30 Talk on Road Safety 1

15:30-16:15 Practical - Applying the methods to stats19 data - a taster

16:15-16:30 Talk on Road Safety 2

Day 2 road safety analysis with R

09:30-11:00 Point pattern analysis

11:15-12:30 Road network data

Lunch

13:30-15:00 Analysing crash data on road network

Break

15:15-15:30: Talk on Road Safety 3

15:30-16:30 Applying the methods to your own data

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.