Geocomputation and Data Analysis with R
Date:
25/04/2019 - 26/04/2019
Organised by:
Consumer Data Research Centre (University of Leeds)
Presenter:
Dr Robin Lovelace
Level:
Intermediate (some prior knowledge)
Contact:
Kylie Norman, 0113 3430242, k.r.norman@leeds.ac.uk
Map:
View in Google Maps (LS2 9JT)
Venue:
Leeds Institute for Data Analytics (LIDA), Level 11, Worsley Building, University of Leeds.
Description:
The aim of Geocomputation and Data Analysis with R is 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).
Learning Objectives
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.
Course Tutor
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.
Prior reading/ experience
If you are new to R, ensure you have completed a basic introductory course such as DataCamp’s introduction to R free course or equivalent.
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
Cost:
The course is open to students, academic staff and external delegates.
Early Bird rate (until 18th March 2019)
£400 – Students
£600 – Academics, charitable and public sector
£800 – Other
Rates 18th March-18th April 2019
£500 – Students
£700 – Academics, charitable and public sector
£900 – Other
The fee includes learning materials, lunch and refreshments during the course, but not overnight accommodation.
Website and registration:
https://www.cdrc.ac.uk/events/geocomputation-and-data-analysis-with-r/
Region:
Yorkshire and Humberside
Keywords:
ICT and Software, R, Programming, Geocomputation
Related publications and presentations: