Quantitative Data Analysis

Data 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

histogram

histogram(~Countries$Population|Countries$Region)

histogram

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

Pie chart

data<-read.csv("data\\piedata.csv",header=T)pie(data$amounts,labels=as.character(data$names))

Boxplot/Barplot

source("boxplot.R")

barplot

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

scatterplots

source(“scatterplot.R")

Bubble plot

source("bubble.R")

Time series

source(“ts.R”)

coplot

source("ozone.R")

Interaction plot

source("interaction.R")

Quantitative Data Analysis

Data preprocessing

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 between variablesFill in the unknown values by exploring the similarity between cases

Transformations of the response and explanatory variables

Linearize the relationship between the response and the explanatory variableslogy 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.

Quantitative Data 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