robinlovelace/tds repository overview

⁠TDS (Transport Data Science)

This repo support teaching of the Transport Data Science module, part of the Mathematical Modelling for Transport (MMT⁠) MSc course at the Institute for Transport Studies.

The module catalogue can be found at catalogue.md⁠. The code accompanying the course can be found in the code folders. To run this code you will need R and Python installed plus various packages and libraries. This software has been packaged-up into a docker container to ease teaching.

⁠Software

After you have installed docker⁠, you should be able to run the software by executing the following commands in a terminal such as Windows PowerShell or the default terminal on Linx and MAC operating systems:

docker run -d -p 8787:8787 -v $(pwd):/home/rstudio/data robinlovelace/tds  

This will:

• Pull the docker image from https://hub.docker.com/r/robinlovelace/tds/⁠ if it's not already on your computer
• Launch a locally hosted instance of RStudio Server which can be accessed at http://localhost:8787/⁠
• Mount your current working dirctory to the data folder in the home directly of the docker image

After navigating to http://localhost:8787/⁠ in a browser you should see a login screen. Username and password are rstudio. See https://github.com/rocker-org/rocker/wiki/Using-the-RStudio-image⁠ for details.

Once in the container you can use all the R packages. To access the pre-installed Python packages you will need to enter the following commands:

conda activate
python

to go into the Python shell. Form more on running Python in RStudio see community.rstudio.com⁠. A demonstration showing the tds docker image in action is illustrated below.

⁠Issues and contributing

Any feedback or contributions to this repo are welcome. If you have a question please open an issue here (you'll need a GitHub account): https://github.com/ITSLeeds/TDS/issues⁠

⁠Data

Data for course can be accessed from the repos Releases⁠ page. You can, for example, download and unzip the data folder in a local version of the repo (accessed by downloading and unzipp https://github.com/ITSLeeds/TDS/archive/master.zip⁠ ) with the following R commands:

download.file("https://github.com/ITSLeeds/TDS/releases/download/0.1/data.zip", destfile = "data.zip")
unzip("data.zip")

If you want to be clever you can use the piggyback package:

# install.packages("devtools")
devtools::install_github("cboettig/piggyback")
piggyback::pb_download("data.zip")

# (This package was used to upload the data with:)
# piggyback::pb_upload(file = "data.zip")
# piggyback::pb_upload(file = "codeExamples.zip")

⁠Other projects

• A book on R for Geocomputation: https://github.com/Robinlovelace/geocompr⁠
• A Python package for OSM data analysis: https://github.com/gboeing/osmnx⁠