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.com/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 lines of R code 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).