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Quantitative   Data AnalysisData exploration and graphics
Is global warming real?               http://chartgraphs.wordpress.com
Russian most extreme summer                 http://joewheatley.net/russian-grain/
Is the world a fair place?                 http://www.gapminder.org/world
acpclust.R
More linkshttp://www.statmethods.net/http://addictedtor.free.fr/graphiques/http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/
Plots and charts
Low level functions
graphical parameters: par()
Lattice package
Curves and functionsx<-seq(-2,2,0.01)y<-x^3-3*xplot(x,y,type="l")Orcurve(x^3-3*x, -2, 2)
Histogram
histogramhistogram(~Countries$Population|Countries$Region)
histogram
histogramhistogram(~Population|Region,data=Countries, col=2:6,panel=function(x,...,col) { panel.histogram(x,...,col=col[pa...
Pie chartdata<-read.csv("datapiedata.csv",header=T)pie(data$amounts,labels=as.character(data$names))
Boxplot/Barplot             source("boxplot.R")
barplotBarplot(tapply(Countries$Population,Countries$Region,sum) ,main="Population",col=rainbow(nlevels(Countries$Regi
scatterplots           source(“scatterplot.R")
Bubble plot              source("bubble.R")
Time series              source(“ts.R”)
coplot         source("ozone.R")
Interaction plot             source("interaction.R")
QuantitativeData AnalysisData preprocessing
Missing valuesRemove the cases with unknownsFill in the unknown values by exploring the properties of the variableFill in ...
Transformations of the response   and explanatory variablesLinearize the relationship between the response and the explana...
QuantitativeData Analysis  Data modelling
Exponential functions                        exp.R
Power functions                  power.R
Polynomial functions                   polinomials.R
Inverse polynomial                     invpol.R
Gamma function                 gamma.R
Asymptotic functions
Asymptotic functions                   michaelis.R
Asymptotic functions                       logistic.R
fitting1.R
fitting2.R
fitting3.R
Data exploration and graphics with R
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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

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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
  6. 6. More linkshttp://www.statmethods.net/http://addictedtor.free.fr/graphiques/http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/
  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()])})
  16. 16. Pie chartdata<-read.csv("datapiedata.csv",header=T)pie(data$amounts,labels=as.character(data$names))
  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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