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Exploring R for Data Visualization: Insights from User 2011 Conference

This summary captures key presentations from the User 2011 conference, emphasizing data visualization techniques with R. Highlights include Andy Pryke's use of Google Visualization API, Markus Gesmann's statistical visualizations for Lloyds, and Jonathan Rougier's innovative nomogram models for predicting donkey weights in Morocco. Attendees learned how to integrate R with web technologies, create interactive graphics using ggplots, and model personality predictions from social network data. The seminar showcased a diverse range of applications and collaborative efforts in data analysis, visualization, and predictive modeling.

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Exploring R for Data Visualization: Insights from User 2011 Conference

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  1. Best of UseR! 2011 • A personal & biased view with an emphasis on data visualisationAndy Pryke • Andy@the-data-mine.co.uk • Birmingham R User Meeting • 20th March 2012

  2. My Bias… I work in commercial data mining, data analysis and data visualisation Background in computing and artificial intelligence Use R to write programs which analyse data

  3. Using Google Visualisation API from R • Speaker: Markus Gesmann, Lloyds • Motivation: Display statistics about publications on a website • 18 different charts are available through Google API • Requires internet access & viewed through web browser • Data is embedded in HTML, with call to google'sjavascript visualisation API • Using RAPACHE you can mix HTML & R (bit like Sweave) • Can update data & look of chart from R by modifying the object returned by the plotting method

  4. Google Visualisation API - Code install.packages("googleVis") library("googleVis") demo("googleVis") demo(package="googleVis") # Example from demo: require(datasets) states <- data.frame(state.name, state.x77) GeoStates <- gvisGeoChart(states, "state.name", "Illiteracy", options=list(region="US",displayMode="regions", resolution="provinces", width=600, height=400)) plot(GeoStates)

  5. Google Visualisation API – More info Use at Lloyds: http://lloyds.com/stats Video demo: http://goo.gl/zfQdG

  6. Google Visualisation API - Examples

  7. Google Visualisation API - Examples

  8. Google Visualisation API - Examples

  9. Google Visualisation API - Examples

  10. Google Visualisation API - Examples

  11. Google Visualisation API - Examples

  12. More Information… • In use on Lloyds website: http://lloyds.com/stats • Original Slides: http://web.warwick.ac.uk/statsdept/user-2011/TalkSlides/Contributed/16Aug_0950_Kaleid_Ib_2-Gesmann.pdf- Includes good list of other interesting packages

  13. Nomograms for visualising relationshipsbetween three variables • Jonathan Rougier • - Dept Mathematics, Univ. Bristol • Kate Milner • - Crossroads Veterinary Centre,Buckinghamshire

  14. How to Use R, in a Morocan Marketplace, to Improve the Life of Donkeys • It's hard to weigh donkeys in North Africa, but useful to know their weight when prescribing drugs. • 1) Measure the weight, height,girth, body condition, age and gender of donkeys. • 2) Use R to create a predictive model of weight • 3) Create a nonographic model which can be used by vets on the ground

  15. How Heavy is that Donkey? • Initial Model – Complex ! • sqrt(Weight) ~ BCSis + Gender + Age + log(HeartGirth) + log(Height) + log(HeartGirth):log(Height) + BCSis:log(HeartGirth) + Gender:log(HeartGirth) + Age:log(HeartGirth) + BCSis:log(Height) + Gender:log(Height) + Age:log(Height)

  16. How Heavy is that Donkey? • Use stepAIC in the MASS package to simplify the model… • Final Model: • sqrt(Weight) ~ BCSis + Age + log(HeartGirth) + log(Height) • Still hard to use in a dust marketplace though…

  17. Solution - Nomograms • “Graphical representation of formula allowing calculations to be made using paper and a ruler” • Published in books & on charts to make complex calculations possible before calculators & computers • Ron Doerfler, 2009, The Lost Art of Nomography, The UMAP Journal, 30(4), pp. 457-493. • http://myreckonings.com/wordpress/wp-content/uploads/JournalArticle/The Lost Art of Nomography.pdf

  18. More information… • Jonty’s Home page with links to slides & code from: http://www.maths.bris.ac.uk/~MAZJCR/#pres • Presentation Slides: http://www.maths.bris.ac.uk/~MAZJCR/jontyUseR.pdf • Package Design also has a nomogram function() – Not in Cran any more but old versions available.

  19. Easy interactive ggplots • Speaker: Richie Cotton • Clever use of packages ggplots and gWidgetstcltk together, allowing clear and simple code for interactive control of charts • Example data: Chromium exposure of welders. Took air concentations & urine samples (pre/post exposure)

  20. More Information… • Links at: http://www.bitly.com/jV1NBn • Code linked directly from http://4dpiecharts.com/2011/08/17/user2011-easy-interactive-ggplots-talk/ • See also: package gWidgets - wraps 5 UI toolkits

  21. Predicting Personality fromSocial Network Data • Speaker: Daniel Chapsky, Hampshire College • This was quite a fast talk, but one of my favourite pieces of work, so apologies if I've mis-interpreted anything! • Big 5 theory of personality is that 5 dimensions can predict attitude, views, behaviour • This work attempts to build a model which predicts someone's "big 5" values from Online Social Network (OSN) data

  22. Predicting Personality - Data • 615 respondents • 100 question open source personality test, "IPIP NEO" • Data last.fm, netflicks, etc – e.g. genres listened to • Distance from home town to current residence - liberallity correlates with amount of moving around • Mean income, Education level • Race inferred from surname • Data was continuous • Missing data was inferred using gibbs sampling

  23. Predicting Personality – Model • Continuous bayesian networks - discrete needs more data • - Often weaker prediction than black box • + Clear semantics • + Works with limited evidence • + Hybrid network

  24. Predicting Personality – Packages • Database connectivity - RMySQL • Web scraping / API connection - RCurl, RJSONIO, XML • Inference through mashups - psych, geosphere • Data Cleaning - plyr, reshape2, bayestree, mice, tm, mvoutlier • Bayesian Network construction - bnlearn, pcalg • Parallelization of optimization - foreach, snow • Graphics - Latticist, bnlearn, ggplot2

  25. Agreeableness = 42.4 • - 1.26(Sex.Missing) • - 2.47(Sex.Male) • - 25.99(Home.Teen.Prop) • - 0.63(Movie.Dystopia-Political) • - 0.49(Movie.Action-thriller) • + 6.51(Wall.Status.Ratio) • + 0.08(Conscientiousness) • - 0.29(Neuroticism) • R2 = 0.46

  26. More Information • Original Slides: • http://web.warwick.ac.uk/statsdept/user-2011/TalkSlides/Contributed/17Aug_1115_FocusIII_5-DataMining_2-Chapsky.pdf

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