R is a free and open-source programming language and software environment for statistical analysis and graphics. It has over 9,000 packages that allow users to perform spatial analysis and visualization. R allows users to integrate multiple analyses in one software, link to different resources, and build various outputs. It is useful for spatial analysis tasks like interpolation of data, producing density maps, and examining residuals without being a "black box." R and R Studio allow reproducible research through R Markdown and sharing of workflows and analysis tools through packages.

1. FOSS4G Cymru
19th October 2016
Dr Richard Fry
Using as a GIS

3. What is R?
S
S-Plus
1976
1988
1993
2015
9,368
The Comprehensive R Archive Network

4. Why we use R?
“linkable to other software, large support community
whenever coming across any problems or errors. Knitr
and Markdown gives a very good control over output”
“ allows you to integrate multiple possibilities via its packages,
link to different resources and build different outputs all in the
same software ”
Reproducible Research
Open Source – no black box

7. R for Spatial Analysis
~250
Spatial
Packages
GEOS
ggplot2

9. Spatial interpolation of historic rainfall data

11. CHALICE Example
• NIHR project looking at change in alcohol outlet density longitudinally
• Basis for the health outcomes was density models aggregated to LSOA
level for 24 time periods for the whole of Wales
• Needed a way visualising the data without having to manually
produce ~45,000 maps at LSOA level or 528 maps at Unitary Authority
level to “eyeball” the model residuals
• R, GGPLOT2 and Faceting enabled this
LSOA_CD Y2005Q1 Y2005Q2 Y2005Q3 Y2005Q4 Y2006Q1 Y2006Q2 Y2006Q3 Y2006Q4 Y2007Q1 Y2007Q2 Y2007Q3 Y2007Q4
W01001435 0 0 0 0 0 0 0 0 4.498163 4.498163 4.498163 4.498163
W01001436 0 0 0 0 0 0 0 0 6.166628 6.166628 6.166628 6.166628
W01001437 0 0 0 0 0 0 0 0 2.688254 2.688254 2.688254 2.688254
W01001438 0 0 0 0 0 0 0 0 0.93109 0.93109 0.93109 0.93109
W01001439 0 0 0 0 0 0 0 0 1.282038 1.237153 1.237153 1.237153
W01001440 0 0 0 0 0 0 0 0 1.051661 1.051661 1.051661 1.051661

13. Publishing outputs and sharing code – R
Markdown and Shiny

14. https://richfry.github.io/Exposure.html

15. Shiny Maps

19. Reasons you might choose R (R Studio) for
Spatial Analysis:
1. To perform tailored ESDA, spatial statistics and regressions and
examine the residuals – no black box!
2. You want a single environment to acquire (download), manipulate,
analyse, visualise and publish geospatial data
3. Standardised environment allowing you develop reproducible code
and reports
4. Easily share workflows and methods with colleagues and peers
5. (Easily) develop your own analysis tools in the form of a package

20. R – Spatial SIG - https://stat.ethz.ch/mailman/listinfo/r-sig-geo
James Cheshire – data viz http://spatial.ly/r/
R Studio - https://www.rstudio.com/ & https://blog.rstudio.org/
Resources
Questions ?