Teaching R as a GIS: problems, solutions and lessons learned Robin Lovelace and Rachel Oldroyd University of Leeds

● Statistical programming language and data environment ● Based on the statistical language S ● Can rival proprietary GIS's due to the emergence of advanced spatial packages Background

Advantages ● Reproducibility ● Batch Processing ● Extensible environment – packages / libraries ● Fast ● Advanced Graphics

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Slow uptake amongst social scientists – command line interface (CLI) – the popularity of traditional GIS (QGIS, ArcMap, MapInfo) – Pre-conceptions, steep learning curve – Failure to see usefulness – Absence on UG /PG curriculum Barriers to Learning

Aims and Objectives ● Raise awareness of R's spatial capabilities ● Teach new methods to social scientists – TALISMAN project (2010-2015) – Consumer Data Research Centre (2015 - ) ● Promote the use of Open Source Software

The Teaching Activity ● 1 day practical workshop ● Variety of audiences ● Over an 18 month period ● Formal and informal Likert feedback collected ● 'Experiential learning'

Difficulties ● Variety of participant prior knowledge – 3 'types of participant' ● Material Covered – Originally 2 tutorials – 'I was acting more like a typewriter' – 'I was tying code without understanding what I was doing'

Difficulties ● Technical Difficulties – Permissions ● Finishing early

Solutions I ● Quality not quantity of materials – Focus on 1 tutorial ● Extension tasks – 'The course strikes a good balance...for an R novice and interesting for more experienced R users' – 'The pace was just enough and there were enough extra bits of information to keep me interested'

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Solutions II ● Supportive learning environment – High number of demonstrators – Interactive ● Use of IDE - RStudio – Autocompletion – Help – Plots – Data Environment

Solutions II ● Use of traditional GIS – QGIS – For those not familiar with geographical datatypes – Visualisation of attribute and geometry data

Results

Lessons Learned ● Start at the beginning ● Evolving, dynamic materials ● Communication = vital

Lessons Learned ● Dissemination – CRAN, Twitter, Conferences ● Clearly define target audience – Not suitable for some participants – Clearly identify prerequisites beforehand

Reference Lovelace, Robin, and James Cheshire. "Introduction to visualising spatial data in R." (2014) http://robinlovelace.net/r/2014/01/30/spatial- data-with-R-tutorial.html