Using R to enhance numeracy in geography: some pros and cons


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A short presentation for the 2011 Geography, Earth and Environmental Sciences conference (Teaching and Learning for GEES Students, Birmingham) exploring how R might help improve the statistical numeracy of undergraduate students.

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Using R to enhance numeracy in geography: some pros and cons

  1. 1. Using R to enhance numeracyin geography: some pros and cons <br />Rich<br />Using R to enhance numeracy in geography: some pros and cons by Richard Harris / University of Bristol is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.<br />
  2. 2. From my office door<br />A society in which our lives and choices are enriched by an understanding of statistics<br /><br />A little idealistic but…<br />
  3. 3. The Crisis of Numeracy<br />To unravel the complexities of society requires a highly skilled research base, equipped with suitable tools and techniques, most notably advanced quantitative methods […] Yet there are persistent concerns that the UK lacks the critical mass to satisfy such demand. <br />ESRC document (2011)<br />
  4. 4. How can R help?<br />Broadly intuitive<br />Strong focus on graphics<br />It has in build good practice<br />It’s free and cross-platform<br /><br />Extendable and customisable<br />Libraries for mapping, spatial statistics, spatial regression, geostatistics, etc.<br />Large user community<br />
  5. 5. It’s not esoteric!<br />“R is used at the world’s largest technology companies (including Google, Microsoft and Facebook), the largest pharmaceutical companies (including Johnson & Johnson, Merck, and Pfizer), and at hundreds of other companies. It’s used in statistics classes at universities around the world and be statistical researchers to try new techniques and algorithms”<br />Adler (2010)<br />
  6. 6. Example graphic made in R:Guardian league table rankings by University<br />
  7. 7. Reading data into R<br />
  8. 8. Exploring the data<br />
  9. 9. Pseudo-code &Simple descriptive statistics<br />
  10. 10. Manipulating the data<br />
  11. 11. Inferential statistics<br />
  12. 12. Relational statistics<br />
  13. 13. Mapping(this is where it gets interesting!)<br />
  14. 14. Some pros<br />Command line<br />Faster, pedagogically superior (learning by doing, no dumb button pushing!)<br />Keeps a clear log of what’s been tried<br />Which could be re-run as a script<br />Graphics are of publishable quality and easy to customise<br />Interactive<br />Extensive help documentation<br />Realistic exposure to research level computing environment<br />
  15. 15. Some cons<br />Risk of automated copying by scripts<br />I always create individualised data for assessed project work<br />Pigeon English is hard for overseas students<br />There is native language support<br />Will often allow you to make errors!<br />Isn’t a software package that will mean much to most employers<br />Though the skill of statistical computing may be more credible<br />
  16. 16. In general<br />“R is very good at plotting graphics, analyzing data, and fitting statistical models using data that fits in the computer’s memory. It’s not good at storing data in complicated structures, efficiently querying data, or working with data that doesn’t fit in the computer’s memory.”<br />Adler (2010)<br />
  17. 17. Integration into the curriculum at Bristol School of Geographical Sciences<br />
  18. 18. Resources<br />Books & Manuals for R<br /><br />Using R for Introductory Statistics (Verzani, 2004)<br />Statistics: An Introduction Using R (Crawley, 2005)<br />R in a Nutshell (Adler, 2009)<br />The UseR series<br /><br />Integration of R with Excel<br /><br /><br />Special Interest Group (SIG) on teaching statistics with R. One particular focus of the SIG is to provide helpful support to instructors new to R who are teaching introductory statistics courses populated with students with little experience in statistics, statistical software, and command line interfaces. <br />
  19. 19. And of course…<br />Workshop tomorrow<br />13.30 – 15.00 (Horton B)<br />Teaching material<br /><br />From late 2011 onwards<br />