# 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.:

- 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

- Spatial data in R

- Context: spatial ecosystem (see section 1.4 of Geocomputation with R - package ecosystem)

- Exercises: Section 6 of the handout

- Further reading: Section 3.2 to 3.2.2 of handouts

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