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 the free DataCamp course. Other good resources can be 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://geocompr.robinlovelace.net/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 = 2017, 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.:

  • If you’re totally new to R a 3 hour session with a one hour break to do sections 1 to 4
  • If you’re an intermediant user, you could skim through sections 1:3 and focus on sections 4:6 to gain understanding of the temporal and spatial aspects of the data in a few hourse
  • If you’re an advanced user, feel free to skip ahead and work through the sections you find most interesting.

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

  • Introduction to R + coding video/links
  • R installation questions/debugging
  • How to use RStudio (practical in groups of 2)
  • R classes and working with data frames (CC)

Break

11:15-12:30 Working with temporal data

  • Time classes
  • Filtering by time of crash
  • Aggregating over time
  • Forecasting crashes over time

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

  • How to access data with stats19
  • Key stats19 functions
  • Excercises: analysing road crash data on the Isle of Wight

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

Day 2 road safety analysis with R

09:30-11:00 Point pattern analysis

  • Visualising data with tmap
  • Spatial and temporal subsetting
  • Aggregation

11:15-12:30 Road network data

  • Desire lines: using origin-destination data
  • Downloading road network data from OSM
  • Buffers on road networks

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