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# Data exploration and graphics with R

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Quantitative Data Analysis -
Part II: Data exploration and graphics -
Master in Global Environmental Change -
IE University

Published in: Technology, Art & Photos
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### Transcript of "Data exploration and graphics with R"

1. 1. Quantitative Data AnalysisData exploration and graphics
2. 2. Is global warming real? http://chartgraphs.wordpress.com
3. 3. Russian most extreme summer http://joewheatley.net/russian-grain/
4. 4. Is the world a fair place? http://www.gapminder.org/world
5. 5. acpclust.R
7. 7. Plots and charts
8. 8. Low level functions
9. 9. graphical parameters: par()
10. 10. Lattice package
11. 11. Curves and functionsx<-seq(-2,2,0.01)y<-x^3-3*xplot(x,y,type="l")Orcurve(x^3-3*x, -2, 2)
12. 12. Histogram
13. 13. histogramhistogram(~Countries\$Population|Countries\$Region)
14. 14. histogram
15. 15. histogramhistogram(~Population|Region,data=Countries, col=2:6,panel=function(x,...,col) { panel.histogram(x,...,col=col[packet.number()])})
17. 17. Boxplot/Barplot source("boxplot.R")
18. 18. barplotBarplot(tapply(Countries\$Population,Countries\$Region,sum) ,main="Population",col=rainbow(nlevels(Countries\$Regi
19. 19. scatterplots source(“scatterplot.R")
20. 20. Bubble plot source("bubble.R")
21. 21. Time series source(“ts.R”)
22. 22. coplot source("ozone.R")
23. 23. Interaction plot source("interaction.R")
24. 24. QuantitativeData AnalysisData preprocessing
25. 25. Missing valuesRemove the cases with unknownsFill in the unknown values by exploring the properties of the variableFill in the unknown values by exploring the correlations betweenvariablesFill in the unknown values by exploring the similarity between cases
26. 26. Transformations of the response and explanatory variablesLinearize the relationship between the response and the explanatoryvariableslogy against x for exponential relationships;logy against logx for power functions;expy against x for logarithmic relationships;1/y against 1/x for asymptotic relationships;logp/1−p againstx for proportion data.Other transformations are useful for variance stabilization: √y to stabilize the variance for count data; arcsin(y) to stabilize the variance of percentage data.
27. 27. QuantitativeData Analysis Data modelling
28. 28. Exponential functions exp.R
29. 29. Power functions power.R
30. 30. Polynomial functions polinomials.R
31. 31. Inverse polynomial invpol.R
32. 32. Gamma function gamma.R
33. 33. Asymptotic functions
34. 34. Asymptotic functions michaelis.R
35. 35. Asymptotic functions logistic.R
36. 36. fitting1.R
37. 37. fitting2.R
38. 38. fitting3.R
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