This project is a book(down) introducing geographic data science using R, which I designed as a companion for the module GY7702 R for Data Science of the MSc in Geographic Information Science at the School of Geography, Geology, and the Environment of the University of Leicester. As such, it is very much a work in progress.
The materials included in this book have been designed for a module focusing on the programming language R as an effective tool for data science for geographers. R is one of the most widely used programming languages. It provides access to a vast repository of programming libraries, covering all aspects of data science, from data wrangling to statistical analysis, from machine learning to data visualisation. That includes various libraries for processing spatial data, performing geographic information analysis, and creating maps. As such, R is a highly versatile, free and open-source tool in geographic information science, which combines the capabilities of traditional GIS software with the advantages of a scripting language and an interface to a vast array of algorithms.
The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.
The book and lecture slides use #FFF0E2 as the background colour to avoid using of a pure white background, which can make reading more difficult and slower for people with dyslexia. All colours have also been checked for readability using Colour Contrast Analyser.
This work is licensed under the GNU General Public License v3.0 except where specified. The text is licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0). See the src/images folder for information regarding the images used in the materials. Contains public sector information licensed under the Open Government Licence v3.0 and information derived from data by sources such as the Office for National Statistics, Ministry of Housing, Communities & Local Government, Ofcom, and other institutions of the UK Government under the Open Government License v3 (see the data folder for further information).
This repository includes teaching materials created by me (Dr Stefano De Sabbata) for the module GY7702 R for Data Science while working at the School of Geography, Geology, and the Environment of the University of Leicester. I would also like to acknowledge the contributions made to parts of these materials by Prof Chris Brunsdon and Prof Lex Comber (see also An Introduction to R for Spatial Analysis and Mapping, Sage, 2015), Dr Marc Padilla, and Dr Nick Tate, who convened previous versions of the module at the University of Leicester.
Last but not least, I would like to acknowledge the myriad of small contributions by users of many platforms, including the Stack Exchange Network (e.g., Stack Overflow, provided under Creative Commons Attribution-Share Alike 2.5 Generic License), who have both asked and answered many of the questions I had while coding this book. It would be impossible to trace back all their contributors through the pages, scripts and years, but they are there. The learning materials were created using R, RStudio, RMarkdown and Bookdown (with many thanks to Yihui Xie for those fantastic tools and related documentation), and GitHub.
You can now reproduce R for Geographic Data Science using Docker. First, install Docker on your system, install Git if not already installed, and clone this repository from GitHub. You can then either build the sdesabbata/r-for-geographic-data-science image running the src/Docker_Build.sh script you can find in the repository or pull the latest sdesabbata/r-for-geographic-data-science image from the Docker Hub.
You should now have all the code and the computational environment to reproduce these materials, which can be done by running the script src/Docker_Make.sh (src/Docker_Make_WinPowerShell.sh on Windows using PowerShell). The script will instantiate a Docker container for the sdesabbata/r-for-geographic-data-science image, bind mount the repository folder to the container and execute src/Make.R on the container, clearing and re-making all the materials. The data used in the materials can be re-created from the original open data using the scripts in src/utils, as described in data/README.md.
For instance, in a Unix-based system like Linux or Mac OS, you can reproduce R for Geographic Data Science using the following four commands:
docker pull sdesabbata/r-for-geographic-data-science:latest
git clone https://github.com/sdesabbata/r-for-geographic-data-science.git
cd r-for-geographic-data-science
# follow the instructions in data/README.md before continuing
./src/Docker_Make.sh
This approach should allow not simply to use the materials but to easily edit and create your version in the same computational environment. To develop your materials, modify the code in the repository and run the src/Docker_Make.sh from the repository folder again to obtain the updated materials.
The RMarkdown code used to create the materials for this book and the lecture slides can be found in the src/book and src/slides folders, respectively. The files are used to generate the Bookdown book and IOSlides slides. The src/utils folder contains the IOSlides templates and some style classes used in the RMarkdown code.
.
├── data
├── docs
└── slides
└── src
├── book
├── images
├── practicals
├── slides
└── utils
├── IOSlides
└── RMarkdown
You can edit the materials in the r-for-geographic-data-science repository folder using RStudio or another editor on your computer and then compile the new materials using Docker. Alternatively, you can follow the learner instructions below to start RStudio Server using Docker and develop your materials in the same environment in which they will be compiled.The first option might be quicker for minor edits, whereas the latter might be preferable for substantial modifications, especially when you need to test your code.
As a learner, you can use Docker to follow the practical sessions instructions and complete the exercises. First, install Docker on your system, install Git if not already installed, and clone this repository from GitHub.
You can then either build the sdesabbata/r-for-geographic-data-science image running the src/Docker_Build.sh script you can find in the repository or pull the latest sdesabbata/r-for-geographic-data-science image from the Docker Hub. You should now have all the code and the computational environment to reproduce these materials, which can be done by running the script src/Docker_RStudio_Start.sh (src/Docker_RStudio_Start_WinPowerShell.sh on Windows using PowerShell) from the repository folder.
For instance, in a Unix-based system like Linux or Mac OS, you can set up and start the r-for-geographic-data-science container using the following four commands:
docker pull sdesabbata/r-for-geographic-data-science:latest
git clone https://github.com/sdesabbata/r-for-geographic-data-science.git
cd r-for-geographic-data-science
# follow the instructions in data/README.md before continuing
./src/Docker_RStudio_Start.sh
The src/Docker_RStudio_Start.sh script will first create a my_r-for-geographic-data-science folder in the parent directory of the root directory of the repository (if it doesn't exist). The script will then instantiate a Docker container for the sdesabbata/r-for-geographic-data-science image, bind mount the my_r-for-geographic-data-science folder and the r-for-geographic-data-science repository folder to the container and start an RStudio Server.
Using your browser, you can access the RStudio Server running from the Docker container by typing 127.0.0.1:28787 in your address bar and using rstudio as username and rstudio as password. As the my_r-for-geographic-data-science folder is bound, everything you will save in the my_r-for-geographic-data-science folder in your home directory on RStudio Server will be saved on your computer. Everything else will be lost when the Docker container is stopped.
To stop the Docker container, running the script src/Docker_RStudio_Stop.sh (same on Windows using PowerShell) from the repository folder.