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Best of UseR! 2011 A personal & biased view with an emphasis on data visualisation Andy Pryke Birmingham.

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Presentation on theme: "Best of UseR! 2011 A personal & biased view with an emphasis on data visualisation Andy Pryke Birmingham."— Presentation transcript:

1 Best of UseR! 2011 A personal & biased view with an emphasis on data visualisation Andy Pryke Birmingham R User Meeting 20 th 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's javascript 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.x77) GeoStates <- gvisGeoChart(states, "", "Illiteracy", options=list(region="US",displayMode="regions", resolution="provinces", width=600, height=400)) plot(GeoStates)

5 Google Visualisation API – More info Use at Lloyds: Video demo:

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: Original Slides: 2011/TalkSlides/Contributed/16Aug_0950_Kaleid_Ib _2-Gesmann.pdf - Includes good list of other interesting packages 2011/TalkSlides/Contributed/16Aug_0950_Kaleid_Ib _2-Gesmann.pdf

13 Nomograms for visualising relationships between 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 Doerer, 2009, The Lost Art of Nomography, The UMAP Journal, 30(4), pp. 457-493. content/uploads/JournalArticle/The Lost Art of Nomography.pd content/uploads/JournalArticle/The Lost Art of Nomography.pdf



20 More information… Jontys Home page with links to slides & code from: Presentation Slides: Package Design also has a nomogram function() – Not in Cran any more but old versions available.Design

21 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)


23 More Information… Links at: Code linked directly from interactive-ggplots-talk/ interactive-ggplots-talk/ See also: package gWidgets - wraps 5 UI toolkits

24 Predicting Personality from Social 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

25 Predicting Personality - Data 615 respondents 100 question open source personality test, "IPIP NEO" Data, 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

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

27 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


29 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) R 2 = 0.46

30 More Information Original Slides: 2011/TalkSlides/Contributed/17Aug_1115_FocusIII_5- DataMining_2-Chapsky.pdf

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