Transport Data Science

A module teaching how to use data science to solve transport problems.

1 Prerequisites

1.1 General computing prerequisites

You should have the latest stable release of R (4.3.0 or above) and be comfortable with computing in general, for example creating folders, moving files, and installing software.

We recommend installing this software on a computer with decent resources (e.g. a laptop with 8 GB of RAM).

1.2 Data science experience prerequisites

Prior experience of using R or Python (e.g. having used it for work, in previous degrees or having completed an online course) is essential.

Students can demonstrate this by showing evidence that they have worked with R before, have completed an online course such as the first 4 sessions in the RStudio Primers series or DataCamp’s Free Introduction to R course.

Evidence of substantial programming and data science experience in previous professional or academic work, in languages such as R or Python, also constitutes sufficient pre-requisite knowledge for the course.

1.3 Software

Although you are free to use any software for the course, the emphasis on reproducibility means that popular popular and established data science languages R and Python are highly recommended.

The teaching will be delivered primarily in R, with some Python code snippets and examples. Unless you have a good reason to use Python, we recommend you use R for the course.

1.3.1 R software prerequisites

For this module you therefore need to have up-to-date versions of R and RStudio installed on a computer you have access to:

  • R from cran.r-project.org
  • RStudio from rstudio.com
  • R packages, which can be installed by opening RStudio and typing install.packages("stats19") in the R console, for example.
  • To install all the dependencies for the module, run the following command in the R console:
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}
remotes::install_github("itsleeds/tdstests")

See Section 1.5 of the online guide Reproducible Road Safety Research with R for instructions on how to install key packages we will use in the module.1

1.3.2 Python software prerequisites

We installing Python with a modern package manager such as pixi.

Footnotes

  1. For further guidance on setting-up your computer to run R and RStudio for spatial data, see these links, we recommend Chapter 2 of Geocomputation with R (the Prerequisites section contains links for installing spatial software on Mac, Linux and Windows): https://geocompr.robinlovelace.net/spatial-class.html and Chapter 2 of the online book Efficient R Programming, particularly sections 2.3 and 2.5, for details on R installation and set-up and the project management section.↩︎