• Introduction
• ggplot2 Components
1. Data
2. Aesthetic Mappings
3. Geoms, Stats, Scales, coordinate system, and positions
4. Faceting
5. Themes
• Publication Level Graphics

1. Experience with R
2. Idea about different Graphical representations
3. Basic Statistics Knowledege
4. Some knowledge about Cricket

(Bring your laptop with R installed.)

1. Produces very elegant graphics
2. Very flexible and Plot specification at a high level of abstraction
3. Very friendly email list (google group).
4. Worth investing time in.
5. Syntax takes a while to get used to.
6. Relatively slow.

1. Base Graphics
2. Grid Graphics
3. Lattice Graphics
4. ggplot2

library("ggplot2")


s10 <- read.csv(file="http://www.iimb.ernet.in/~vinuct10/ggplot/sachin.csv") 


tail(s10,3)


## Subsetting the dataset based on few dismissal 

s10a <- subset(s10,Dismissal %in% c("bowled", "caught", "lbw","not out"))


s10a$Inns <- factor(s10a$Inns)

Mapping aesthetics (position, size, color etc.)

Ball faced Vs Strike Rate by countries

s10c <- subset(s10,Opposition %in% c("v Australia","v South Africa","v New Zealand")  )

ggplot(data=s10c)+geom_point(aes(x=BF,y=SR,colour=Opposition))
 ggplot(data=s10c)+geom_point(aes(x=BF,y=SR,shape=Opposition))
 ggplot(data=s10c)+geom_point(aes(x=BF,y=SR,size=Opposition))

ggplot(data=s10c)+geom_point(aes(x=BF,y=SR,colour=Opposition,shape=result))
ggplot(data=s10c)+geom_point(aes(x=BF,y=SR,colour=Opposition))  
+ 
   geom_smooth(aes(x=BF,y=SR,colour=Opposition )) +
 xlab("Ball Faced") + ylab("Runs Scored")

## Examples of geoms: line,point,box,bar,... 

s10b <- subset(s10,result %in% c("won", "lost") & Opposition %in% c("v Australia","v South Africa","v New Zealand"))


s10b$result <- factor(s10b$result)

s10b$opp <- factor(s10b$Opposition)


ggplot(s10b,aes(opp,Runs))+geom_violin(aes(fill=opp))
require(chron)
qplot(x=factor(years(StartDate)), y=Runs, geom="boxplot", fill=I("blue"),  data=s10b)+ xlab("Year") 



## Examples of stats: smooth, boxplot,...
qplot(Runs, data=s10b,fill=result, geom=c("histogram"))
qplot(Runs, data=s10b,colour=result,geom=c("density"))
ggplot(data=s10b)+geom_point(aes(x=BF,y=Runs ))+ geom_line(data=s10,aes(BF,BF))+geom_smooth(aes(x=BF,y=Runs )) +
  xlab("Ball Faced") + ylab("Runs Scored")

## Scales 

ggplot(data=s10b)+geom_point(aes(x=BF,y=Runs,colour=result))+ scale_colour_manual(values=c("lost"="red","won"="green"))

ggplot(data=s10b)+geom_point(aes(x=BF,y=Runs,size=result,shape=result))+ scale_size_manual(values=c("lost"=5,"won"=3))+
 scale_shape_manual(values=c("lost"=15,"won"=25))   
    
ggplot(data=s10b)+geom_point(aes(x=BF,y=Runs,colour=result))+ scale_colour_discrete(name="Parinaam",labels=c("lost"="HAAR","won"="JEET"))

## Facets 

 s10c= subset(s10,(Opposition %in% c("v Australia","v England","v South Africa","v Sri Lanka")) & (result %in% c("lost", "won"))  )


s10c$yr= cut(as.numeric(format(as.Date(s10c$StartDate), format="%Y")),4) 

qplot(data=s10c, factor(Inns),fill=result,geom="bar",position="fill",facets=  Opposition~yr)

## Themes 

s10a <- subset(s10,Dismissal %in% c("bowled", "caught", "lbw","not out")) 

s10a$Dismissal <- factor(s10a$Dismissal)

s10a$Inns  <- factor(s10a$Inns)

qplot( data=s10a, Dismissal, geom="bar",fill=Inns)

qplot( data=s10a, Dismissal, geom="bar",fill=Inns)+theme_bw() 

qplot( data=s10a, Dismissal, geom="bar",fill=Inns)+theme(panel.background = element_rect(fill="lightblue") )

qplot( data=s10a, Dismissal, geom="bar",fill=Inns)+ theme(plot.background= element_rect(fill="yellow"),panel.background= element_rect(fill='purple'))

• Raster: png(), jpeg(), bitmap()
• Vector: pdf(), postscript(),svg(),cairo_ps()
• On-screen devices: X11, X11cairo, windows, JavaGD, quartz (OS X)
• Packages: rgl (OpenGL), GDD (bitmap formats), RSvgDevice, canvas (HTML canvas), RSVGTipsDevice, tikzDevice (Latex).

Width, height, font, and resolution

• ggplot2 google group
• gglot2 cookbook
• Hadley Wickham's website
• Stack Overflow
• ggplot 0.9.0 Documentation

Slides themes are taken from Vadim Makeev's Shower templeate