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

1. Quantitative
Data Analysis
Data exploration and graphics

2. Is global warming real?
http://chartgraphs.wordpress.com

3. Russian most extreme summer
http://joewheatley.net/russian-grain/

4. Is the world a fair place?
http://www.gapminder.org/world

6. acpclust.R

7. More links
http://www.statmethods.net/
http://addictedtor.free.fr/graphiques/
http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/

8. Plots and charts

9. Low level functions

10. graphical parameters: par()

11. Lattice package

12. Curves and functions
x<-seq(-2,2,0.01)
y<-x^3-3*x
plot(x,y,type="l")
Or
curve(x^3-3*x, -2, 2)

13. Histogram

14. histogram
histogram(~Countries$Population|Countries$Region)

15. histogram

16. histogram
histogram(~Population|Region,data=Countries,
col=2:6,panel=function(x,...,col) {
panel.histogram(x,...,col=col[packet.number()])
})

17. Pie chart
data<-read.csv("datapiedata.csv",header=T)
pie(data$amounts,labels=as.character(data$names))

18. Boxplot/Barplot
source("boxplot.R")

19. barplot
Barplot(tapply(Countries$Population,Countries$Region,sum) ,main="Population",col=rainbow(nlevels(Countries$Regi

20. scatterplots
source(“scatterplot.R")

21. Bubble plot
source("bubble.R")

22. Time series
source(“ts.R”)

23. coplot
source("ozone.R")

24. Interaction plot
source("interaction.R")

25. Quantitative
Data Analysis
Data preprocessing

26. Missing values
Remove the cases with unknowns
Fill in the unknown values by exploring the properties of the variable
Fill in the unknown values by exploring the correlations between
variables
Fill in the unknown values by exploring the similarity between cases

27. Transformations of the response
and explanatory variables
Linearize the relationship between the response and the explanatory
variables
logy 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.

28. Quantitative
Data Analysis
Data modelling

29. Exponential functions
exp.R

30. Power functions
power.R

31. Polynomial functions
polinomials.R

32. Inverse polynomial
invpol.R

33. Gamma function
gamma.R

34. Asymptotic functions

35. Asymptotic functions
michaelis.R

36. Asymptotic functions
logistic.R

37. fitting1.R

38. fitting2.R

39. fitting3.R