Geocomputation and Data Analysis with R aims to get you up-to-speed with high performance geographic processing, analysis, visualisation and modelling capabilities from the command-line. The course will be delivered in R, a statistical programming language popular in academia, industry and, increasingly, the public sector. It will teach a range of techniques using recent developments in the package sf and the ‘metapackage’ tidyverse, based on the open source book Geocomputation with R (Lovelace, Nowosad, and Meunchow 2019).
By the end of the course participants should:
- Be able to use R and RStudio as a powerful Geographic Information System (GIS)
- Know how R’s spatial capabilities fit within the landscape of open source GIS software
- Be confident with using R’s command-line interface (CLI) and scripting capabilities for geographic data processing
- Understand how to import a range of data sources into R
- Be able to perform a range of attribute operations such as subsetting and joining
- Understand how to implement a range of spatial data operations including spatial subsetting and spatial aggregation
- Have the confidence to output the results of geographic research in the form of static and interactive maps.
Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years of experience of using R for academic research and has taught numerous R courses at all levels. He has developed popular R resources including the recently published book Efficient R Programming (Gillespie and Lovelace 2016), Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application, and the stplanr package.
Is this course for me?
The course is open to students, academic staff and external delegates. Prior knowledge and familiarity with R is essential as a level of fluency in R programming is assumed for this course. If you are new to R, ensure you have completed a basic introductory course such as DataCamp’s introduction to R course or equivalent.
Prior reading/ experience
If you’re interested in R for ‘data science’ and installing/updating/choosing R packages, these additional resources are recommended (these optional resources are all freely available online):
- The introductory chapter of R for Data Science
- Chapter 2 on setting-up R and section 4.4 on package selection in the book Efficient R Programming