R Graph Cookbook 代碼怎么寫

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#CHAPTER 5
#Recipe 1. 多個因素變量條形圖Creating Bar charts with more than one factor variable

install.packages("RColorBrewer")  #if not already installed
library(RColorBrewer) 

citysales<-read.csv("citysales.csv")

barplot(as.matrix(citysales[,2:4]), beside=TRUE,
        legend.text=citysales$City,
        args.legend=list(bty="n",horiz=TRUE),
        col=brewer.pal(5,"Set1"),
        border="white",
        ylim=c(0,100),
        ylab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")

box(bty="l")


#Recipe 2.創建堆疊條形圖 Creating stacked bar charts

install.packages("RColorBrewer")
library(RColorBrewer)

citysales<-read.csv("citysales.csv")

barplot(as.matrix(citysales[,2:4]),  
        legend.text=citysales$City,
        args.legend=list(bty="n",horiz=TRUE),
        col=brewer.pal(5,"Set1"),
        border="white",
        ylim=c(0,200),
        ylab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")


citysalesperc<-read.csv("citysalesperc.csv") 

par(mar=c(5,4,4,8),xpd=T)

barplot(as.matrix(citysalesperc[,2:4]), 
        col=brewer.pal(5,"Set1"),
        border="white",
        ylab="Sales Revenue (1,000's of USD)", 
        main="Percentage Sales Figures") 

legend("right",legend=citysalesperc$City,bty="n",inset=c(-0.3,0),fill=brewer.pal(5,"Set1"))



#Recipe 3. 調整條形圖方向（水平和垂直）Adjusting the orientation of bars ?horizontal and vertical

barplot(as.matrix(citysales[,2:4]), beside=TRUE,horiz=TRUE,
        legend.text=citysales$City,
        args.legend=list(bty="n"),
        col=brewer.pal(5,"Set1"),
        border="white",
        xlim=c(0,100),
        xlab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")

par(mar=c(5,4,4,8),xpd=T)
	
barplot(as.matrix(citysalesperc[,2:4]), horiz=TRUE,
        col=brewer.pal(5,"Set1"),
        border="white",
        xlab="Percentage of Sales",
        main="Perecentage Sales Figures")

legend("right",legend=citysalesperc$City,bty="n",
inset=c(-0.3,0),fill=brewer.pal(5,"Set1"))


#Recipe 4.調整桿寬度、間距、顏色和邊界 Adjusting bar widths, spacing, colours and borders

barplot(as.matrix(citysales[,2:4]), beside=TRUE,
     legend.text=citysales$City,
     args.legend=list(bty="n",horiz=T),
     col=c("#E5562A","#491A5B","#8C6CA8","#BD1B8A","#7CB6E4"),
     border=FALSE,
     space=c(0,5),
     ylim=c(0,100),
     ylab="Sales Revenue (1,000's of USD)",
     main="Sales Figures")


barplot(as.matrix(citysales[,2:4]), beside=T,
        legend.text=citysales$City,
        args.legend=list(bty="n",horiz=T),
        ylim=c(0,100),
        ylab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")


#Recipe 5.條線上方或旁邊顯示值 Displaying values on top of or next to the bars

x<-barplot(as.matrix(citysales[,2:4]), beside=TRUE,
        legend.text=citysales$City,
        args.legend=list(bty="n",horiz=TRUE),
        col=brewer.pal(5,"Set1"),
        border="white",
        ylim=c(0,100),
        ylab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")

y<-as.matrix(citysales[,2:4])

text(x,y+2,labels=as.character(y))


#Horizontal bars
y<-barplot(as.matrix(citysales[,2:4]), beside=TRUE,horiz=TRUE,
        legend.text=citysales$City,
        args.legend=list(bty="n"),
        col=brewer.pal(5,"Set1"),
        border="white",
        xlim=c(0,100),
        xlab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")

x<-as.matrix(citysales[,2:4])

text(x+2,y,labels=as.character(x))



#Recipe 6. Placing labels inside bars

rain<-read.csv("cityrain.csv")
	
y<-barplot(as.matrix(rain[1,-1]),horiz=T,col="white",yaxt="n",
main="Monthly Rainfall in Major CitiesJanuary",
xlab="Rainfall (mm)")

x<-0.5*rain[1,-1] 
text(x,y,colnames(rain[-1]))



#Recipe 7.創建帶垂直誤差線的條形圖Creating Bar charts with vertical error bars

sales<-t(as.matrix(citysales[,-1]))
colnames(sales)<-citysales[,1] 

x<-barplot(sales,beside=T,legend.text=rownames(sales),
args.legend=list(bty="n",horiz=T),
col=brewer.pal(3,"Set2"),border="white",ylim=c(0,100),
        ylab="Sales Revenue (1,000's of USD)",
        main="Sales Figures")

arrows(x0=x,
y0=sales*0.95,
x1=x,
y1=sales*1.05,
angle=90,
code=3,
length=0.04,
lwd=0.4)


#Creating a function
errorbars<-function(x,y,upper,lower=upper,length=0.04,lwd=0.4,...) {
arrows(x0=x,
y0=y+upper,
x1=x,
y1=y-lower,
angle=90,
code=3,
length=length,
lwd=lwd)
}

errorbars(x,sales,0.05*sales) 


#Recipe 8. 帶條件變量的點陣圖Modifying dotplots by grouping variables

install.packages("reshape")
library(reshape)

sales<-melt(citysales)

sales$color[sales[,2]=="ProductA"] <- "red"
sales$color[sales[,2]=="ProductB"] <- "blue"
sales$color[sales[,2]=="ProductC"] <- "violet"

dotchart(sales[,3],labels=sales$City,groups=sales[,2],
col=sales$color,pch=19,
main="Sales Figures",
xlab="Sales Revenue (1,000's of USD)")


#Recipe 9. 可讀性更好的餅圖Making better readable pie charts with clockwise-ordered slices

browsers<-read.table("browsers.txt",header=TRUE)
browsers<-browsers[order(browsers[,2]),]

pie(browsers[,2],
labels=browsers[,1],
clockwise=TRUE,
radius=1,
col=brewer.pal(7,"Set1"),
border="white",
main="Percentage Share of Internet Browser usage")



#Recipe 10. 對餅圖增加標簽Labelling a pie chart with percentage values for each slice 

	browsers<-read.table("browsers.txt",header=TRUE)
	browsers<-browsers[order(browsers[,2]),]
	
pielabels <- sprintf("%s = %3.1f%s", browsers[,1], 100*browsers[,2]/sum(browsers[,2]), "%")

pie(browsers[,2],
labels=pielabels,
clockwise=TRUE,
radius=1,
col=brewer.pal(7,"Set1"),
border="white",
cex=0.8,
main="Percentage Share of Internet Browser usage")



#Recipe 11.餅圖增添圖例 Adding a legend to a pie chart

	browsers<-read.table("browsers.txt",header=TRUE)
	browsers<-browsers[order(browsers[,2]),]
	
pielabels <- sprintf("%s = %3.1f%s", browsers[,1], 100*browsers[,2]/sum(browsers[,2]), "%")

pie(browsers[,2],
labels=NA,
clockwise=TRUE,
col=brewer.pal(7,"Set1"),
border="white",
radius=0.7,
cex=0.8,
main="Percentage Share of Internet Browser usage")

legend("bottomright",legend=pielabels,bty="n",
fill=brewer.pal(7,"Set1"))

#Recipe 1.頻率或概率的圖示 Visualising distributions as frequency or probability  

air<-read.csv("airpollution.csv")

hist(air$Nitrogen.Oxides,
     xlab="Nitrogen Oxide Concentrations",
     main="Distribution of Nitrogen Oxide Concentrations")


hist(air$Nitrogen.Oxides,
     freq=FALSE,
     xlab="Nitrogen Oxide Concentrations",
     main="Distribution of Nitrogen Oxide Concentrations")



#Recipe 2.設置直方圖箱寬度和截斷數 Setting bin size and number of breaks

air<-read.csv("airpollution.csv")

hist(air$Nitrogen.Oxides,
     breaks=20,
     xlab="Nitrogen Oxide Concentrations",
     main="Distribution of Nitrogen Oxide Concentrations")

hist(air$Nitrogen.Oxides,
     breaks=c(0,100,200,300,400,500,600),
     xlab="Nitrogen Oxide Concentrations",
     main="Distribution of Nitrogen Oxide Concentrations")

#Recipe 3.調整直方圖風格：顏色、邊界、坐標 Adjusting histogram styles: bar colours, borders and axes

air<-read.csv("airpollution.csv")

hist(air$Respirable.Particles,
     prob=TRUE,
     col="black",
     border="white",
     xlab="Respirable Particle Concentrations",
     main="Distribution of Respirable Particle Concentrations")


par(yaxs="i",las=1)
hist(air$Respirable.Particles,
     prob=TRUE,	
     col="black",
     border="white",
     xlab="Respirable Particle Concentrations",
     main="Distribution of Respirable Particle Concentrations")
box(bty="l")
grid(nx=NA,ny=NULL,lty=1,lwd=1,col="gray")



#Recipe 4.直方圖上增加密度擬合線 Overlaying density line over a histogram

par(yaxs="i",las=1)
hist(air$Respirable.Particles,
     prob=TRUE,
     col="black",
     border="white",
     xlab="Respirable Particle Concentrations",
     main="Distribution of Respirable Particle Concentrations")
box(bty="l")

lines(density(air$Respirable.Particles,na.rm=T),col="red",lwd=4)
grid(nx=NA,ny=NULL,lty=1,lwd=1,col="gray")



#Recipe 5.帶直方圖的矩陣圖 Multiple histograms along the diagonal of a pairs plot

panel.hist <- function(x, ...)
  {
    par(usr = c(par("usr")[1:2], 0, 1.5) )
    hist(x, prob=TRUE,add=TRUE,col="black",border="white")
  }


plot(iris[,1:4],
     main="Relationships between characteristics of iris flowers",
     pch=19,
     col="blue",
     cex=0.9,
     diag.panel=panel.hist)


#Recipe 6. Histograms in the margins of line and scatterplots

air<-read.csv("airpollution.csv")

#Set up the layout first
layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), widths=c(3,1), heights=c(1,3), TRUE)

#Make Scatterplot
par(mar=c(5.1,4.1,0.1,0))
plot(air$Respirable.Particles~air$Nitrogen.Oxides,
     pch=19,col="black",
     xlim=c(0,600),ylim=c(0,80),
     xlab="Nitrogen Oxides Concentrations",
     ylab="Respirable Particle Concentrations")

#Plot histogram of X variable in the top row
par(mar=c(0,4.1,3,0))
hist(air$Nitrogen.Oxides,
     breaks=seq(0,600,100),
     ann=FALSE,axes=FALSE,
     col="black",border="white")

#Plot histogram of Y variable to the right of the scatterplot
yhist <- hist(air$Respirable.Particles,
              breaks=seq(0,80,10),
              plot=FALSE)

par(mar=c(5.1,0,0.1,1))
barplot(yhist$density,
        horiz=TRUE,
        space=0,axes=FALSE,
        col="black",border="white")

#CHATER 7
#Recipe 1. Creating box plots with narrow boxes for small number of variables

air<-read.csv("airpollution.csv")

boxplot(air,las=1)

boxplot(air,boxwex=0.2,las=1)

par(las=1)

boxplot(air,width=c(1,2))

#Recipe 2. Grouping over a variable

metals<-read.csv("metals.csv")

boxplot(Cu~Source,data=metals,
		main="Summary of Copper (Cu) concentrations by Site")

boxplot(Cu~Source*Expt,data=metals,
		main="Summary of Copper (Cu) concentrations by Site")


#Recipe 3. Varying box widths by number of observations

metals<-read.csv("metals.csv")

boxplot(Cu ~ Source, data = metals,
        varwidth=TRUE,
        main="Summary of Copper concentrations by Site")



#Recipe 4. Creating box plots with notches

metals<-read.csv("metals.csv")

boxplot(Cu ~ Source, data = metals,
        varwidth=TRUE,
        notch=TRUE,	
        main="Summary of Copper concentrations by Site")


#Recipe 5. Including or excluding outliers

metals<-read.csv("metals.csv")

boxplot(metals[,-1], 
	outline=FALSE,
	main="Summary of metal concentrations by Site \n (without outliers)")



#Recipe 6. Creating horizontal box plots

metals<-read.csv("metals.csv")

boxplot(metals[,-1], 
	horizontal=TRUE,
	las=1,
	main="Summary of metal concentrations by Site")


#Recipe 7. Changing box styling

metals<-read.csv("metals.csv")

boxplot(metals[,-1],
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,
        whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of metal concentrations by Site")

grid(nx=NA,ny=NULL,col="gray",lty="dashed")


#Recipe 8. Adjusting the extent of plot whiskers outside the box

metals<-read.csv("metals.csv")

boxplot(metals[,-1],
	range=1,
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,
        whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of metal concentrations by Site \n (range=1) ")

boxplot(metals[,-1],
	range=0,
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,
        whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of metal concentrations by Site \n (range=0)")


#Recipe 9. Showing number of observations 

metals<-read.csv("metals.csv")

b<-boxplot(metals[,-1],
	  xaxt="n",
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,
        whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of metal concentrations by Site")

axis(side=1,at=1:length(b$names),labels=paste(b$names,"\n(n=",b$n,")",sep=""),mgp=c(3,2,0))


install.packages("gplots")
library(gplots)

boxplot.n(metals[,-1],
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,
        whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of metal concentrations by Site")


#Recipe 10. Splitting a variable at arbitrary values into subsets

metals<-read.csv("metals.csv")

cuts<-c(0,40,80)
Y<-split(x=metals$Cu, f=findInterval(metals$Cu, cuts))

boxplot(Y,
        xaxt="n",            
        border = "white",
        col = "black",
        boxwex = 0.3,
        medlwd=1,           
	whiskcol="black",
        staplecol="black",
        outcol="red",cex=0.3,outpch=19,
        main="Summary of Copper concentrations",
	xlab="Concentration ranges",
	las=1)

axis(1,at=1:length(clabels),
     labels=c("Below 0","0 to 40","40 to 80","Above 80"),      
     lwd=0,lwd.ticks=1,col="gray")



boxplot.cuts<-function(y,cuts) {

Y<-split(metals$Cu, f=findInterval(y, cuts))

b<-boxplot(Y,
           xaxt="n",            
           border = "white",
           col = "black",
           boxwex = 0.3,
           medlwd=1,           
           whiskcol="black",
           staplecol="black",
           outcol="red",cex=0.3,outpch=19,
           main="Summary of Copper concentrations",
           xlab="Concentration ranges",
           las=1)

clabels<-paste("Below",cuts[1])
     
for(k in 1:(length(cuts)-1))
   {
    clabels<-c(clabels, paste(as.character(cuts[k]), "to",as.character(cuts[k+1])))
   }

clabels<-c(clabels, 
           paste("Above",as.character(cuts[length(cuts)])))

axis(1,at=1:length(clabels),
labels=clabels,lwd=0,lwd.ticks=1,col="gray")

}


boxplot.cuts(metals$Cu,c(0,30,60))

boxplot(Cu~Source,data=metals,subset=Cu>40)


#An alternative definition of boxplot.cuts()


boxplot.cuts<-function(y,cuts) {

 	f=cut(y, c(min(y[!is.na(y)]),cuts,max(y[!is.na(y)])), ordered_results=TRUE);
   Y<-split(y, f=f)
 
	b<-boxplot(Y,
   	        xaxt="n",            
      	     border = "white",
         	  col = "black",
              boxwex = 0.3,
	           medlwd=1,           
	           whiskcol="black",
           	  staplecol="black",
              outcol="red",cex=0.3,outpch=19,
              main="Summary of Copper concentrations",
	     		  xlab="Concentration ranges",
	     		  las=1)


	clabels = as.character(levels(f))
axis(1,at=1:length(clabels),
labels=clabels,lwd=0,lwd.ticks=1,col="gray")

}


boxplot.cuts(metals$Cu,c(0,40,80))

#CHAPTER 8
#Recipe 1. Creating heat maps of single Z 

variable with scale

sales<-read.csv("sales.csv")

install.packages("RColorBrewer")
library(RColorBrewer)

rownames(sales)<-sales[,1]
sales<-sales[,-1]
data_matrix<-data.matrix(sales)
	
pal=brewer.pal(7,"YlOrRd")

breaks<-seq(3000,12000,1500)

#Create layout with 1 row and 2 columns 

(for the heatmap and scale); the heatmap 

column is 8 times as wide as the scale 

column

layout(matrix(data=c(1,2), nrow=1, 

ncol=2), widths=c(8,1), heights=c(1,1))

#Set margins for the heatmap
par(mar = c(5,10,4,2),oma=c

(0.2,0.2,0.2,0.2),mex=0.5)           


image(x=1:nrow(data_matrix),y=1:ncol

(data_matrix), 	
      z=data_matrix,
      axes=FALSE,
      xlab="Month",
      ylab="",
      col=pal[1:(length(breaks)-1)], 
      breaks=breaks,
      main="Sales Heat Map")

axis(1,at=1:nrow

(data_matrix),labels=rownames

(data_matrix), col="white",las=1)
           
axis(2,at=1:ncol

(data_matrix),labels=colnames

(data_matrix), col="white",las=1)

abline(h=c(1:ncol(data_matrix))+0.5, 
       v=c(1:nrow(data_matrix))+0.5, 

col="white",lwd=2,xpd=FALSE)

breaks2<-breaks[-length(breaks)]

# Color Scale
par(mar = c(5,1,4,7)) 

# If you get a figure margins error while 

running the above code, enlarge the plot 

device or adjust the margins so that the 

graph and scale fit within the device.

image(x=1, y=0:length(breaks2),z=t

(matrix(breaks2))*1.001,
      col=pal[1:length(breaks)-1],
      axes=FALSE,
      breaks=breaks,
      xlab="", ylab="",
      xaxt="n")

axis(4,at=0:(length(breaks2)-1), 

labels=breaks2, col="white", las=1)

abline(h=c(1:length

(breaks2)),col="white",lwd=2,xpd=F)


#Recipe 2. Creating correlation heat maps

genes<-read.csv("genes.csv")

rownames(genes)<-genes[,1]
data_matrix<-data.matrix(genes[,-1])

pal=heat.colors(5)

breaks<-seq(0,1,0.2)

layout(matrix(data=c(1,2), nrow=1, 

ncol=2), widths=c(8,1), heights=c(1,1))

par(mar = c(3,7,12,2),oma=c

(0.2,0.2,0.2,0.2),mex=0.5)           

image(x=1:nrow(data_matrix),y=1:ncol

(data_matrix),
	   z=data_matrix,
      xlab="",
      ylab="",
      breaks=breaks,
      col=pal,
      axes=FALSE)


text(x=1:nrow(data_matrix)+0.75, y=par

("usr")[4] + 1.25, 
     srt = 45, adj = 1, labels = 

rownames(data_matrix), 
     xpd = TRUE)

axis(2,at=1:ncol

(data_matrix),labels=colnames

(data_matrix),col="white",las=1)

abline(h=c(1:ncol(data_matrix))+0.5,v=c

(1:nrow(data_matrix))

+0.5,col="white",lwd=2,xpd=F)

title("Correlation between 

genes",line=8,adj=0)

breaks2<-breaks[-length(breaks)]

# Color Scale
par(mar = c(25,1,25,7))
image(x=1, y=0:length(breaks2),z=t

(matrix(breaks2))*1.001
      ,col=pal[1:length(breaks)-1]
       ,axes=FALSE
       ,breaks=breaks
      ,xlab="",ylab=""
      ,xaxt="n")

axis(4,at=0:(length

(breaks2)),labels=breaks,col="white",las=

1)
abline(h=c(1:length

(breaks2)),col="white",lwd=2,xpd=FALSE)



#Recipe 3. Summarising multivariate data 

in a single heat map

nba <- read.csv("nba.csv")

library(RColorBrewer)

rownames(nba)<-nba[,1]

data_matrix<-t(scale(data.matrix(nba[,-

1])))

pal=brewer.pal(6,"Blues")

statnames<-c("Games Played", "Minutes 

Played", "Total Points", "Field Goals 

Made", "Field Goals Attempted", "Field 

Goal Percentage", "Free Throws Made", 

"Free Throws Attempted", "Free Throw 

Percentage", "Three Pointers Made", 

"Three Pointers Attempted", "Three Point 

Percentage", "Offensive Rebounds", 

"Defensive Rebounds", "Total Rebounds", 

"Assists", "Steals", "Blocks", 

"Turnovers", "Fouls")

par(mar = c(3,14,19,2),oma=c

(0.2,0.2,0.2,0.2),mex=0.5)

#Heat map          
image(x=1:nrow(data_matrix),y=1:ncol

(data_matrix),
      z=data_matrix,
      xlab="",
      ylab="",
      col=pal,
      axes=FALSE)

#X axis labels
text(1:nrow(data_matrix), par("usr")[4] + 

1, 
     srt = 45, adj = 0, 
     labels = statnames,
     xpd = TRUE, cex=0.85)

#Y axis labels
axis(side=2,at=1:ncol(data_matrix),
     labels=colnames(data_matrix),
     col="white",las=1, cex.axis=0.85)

#White separating lines
abline(h=c(1:ncol(data_matrix))+0.5,
       v=c(1:nrow(data_matrix))+0.5,
       col="white",lwd=1,xpd=F)

#Graph Title
text(par("usr")[1]+5, par("usr")[4] + 12,
     "NBA per game performance of top 

50corers", 
     xpd=TRUE,font=2,cex=1.5)

nba <- nba[order(nba$PTS),]


#Recipe 4. Creating contour plots

contour(x=10*1:nrow(volcano), 

y=10*1:ncol(volcano), z=volcano,
		  xlab="Metres 

West",ylab="Metres North", 
		  main="Topography of 

Maunga Whau Volcano")


par(las=1)

plot(0,0,xlim=c(0,10*nrow

(volcano)),ylim=c(0,10*ncol

(volcano)),type="n",xlab="Metres 

West",ylab="Metres 

North",main="Topography of Maunga Whau 

Volcano")

u<-par("usr")

rect(u[1],u[3],u[2],u

[4],col="lightgreen")

contour(x=10*1:nrow(volcano),y=10*1:ncol

(volcano),
		  

volcano,col="red",add=TRUE)


#Recipe 5. Creating filled contour plots


filled.contour(x = 10*1:nrow(volcano), 
		y = 10*1:ncol(volcano), 
		z = volcano, 

color.palette = terrain.colors, 
		plot.title = title(main = 

"The Topography of Maunga Whau",
	        xlab = "Meters North", 
		ylab = "Meters West"),
		plot.axes = {axis(1, seq

(100, 800, by = 100))
            	axis(2, seq(100, 600, by 

= 100))},
		key.title = title

(main="Height\n(meters)"),
		key.axes = axis(4, seq

(90, 190, by = 10))) 

#Increased detail and smoothness

filled.contour(x = 10*1:nrow(volcano), 
		y = 10*1:ncol(volcano), 
		z = volcano, 
		color.palette = 

terrain.colors, 
		plot.title = title(main = 

"The Topography of Maunga Whau",
		xlab = "Meters North", 
		ylab = "Meters West"),
		nlevels=100,
		plot.axes = {axis(1, seq

(100, 800, by = 100))
            		    axis(2, seq

(100, 600, by = 100))},
		key.title = title

(main="Height\n(meters)"),
		key.axes = axis(4, seq

(90, 190, by = 10))) 


#Recipe 6. Creating 3-dimensional surface 

plots

install.packages("rgl")
library(rgl)

z <- 2 * volcano
x <- 10 * (1:nrow(z))
y <- 10 * (1:ncol(z))

zlim <- range(z)
zlen <- zlim[2] - zlim[1] + 1

colorlut <- terrain.colors(zlen) 
col <- colorlut[ z-zlim[1]+1 ] 

rgl.open()
rgl.surface(x, y, z, color=col, 

back="lines")


#Recipe 7. Visualizing time Series as 

calendar heat maps

source("calendarHeat.R")

stock.data <- read.csv("google.csv")

install.packages("chron")
library("chron")

calendarHeat(dates=stock.data$Date, 
	     values=stock.data$Adj.Close, 
	     varname="Google Adjusted 

Close")


#Using the openair package

install.packages("openair")
library(openair)

calendarPlot(mydata)

mydata$sales<-rnorm(length

(mydata$nox),mean=1000,sd=1500)

calendarPlot

(mydata,pollutant="sales",main="Daily 

Sales in 2003")

#CHAPTER 9
#Recipe 1. Plotting global data by countries on a world map

install.packages("maps")
library(maps)
install.packages("WDI")
library(WDI)
install.packages("RColorBrewer")
library(RColorBrewer)

colors = brewer.pal(7,"PuRd")
wgdp<-WDIsearch("gdp")
w<-WDI(country="all", indicator=wgdp[4,1], start=2005, end=2005)

w[63,1] <-  "USA"

x<-map(plot=FALSE)


x$measure<-array(NA,dim=length(x$names))

for(i in 1:length(w$country)) {

	for(j in 1:length(x$names)) {
		if(grepl(w$country[i],x$names[j],ignore.case=T))
		  x$measure[j]<-w[i,3]
	}

}

sd = data.frame(col=colours,values=seq(min(x$measure[!is.na(x$measure)]),
max(x$measure[!is.na(x$measure)])*1.0001,length.out=7))

#intervals color scheme
sc<-array("#FFFFFF",dim=length(x$names))

for (i in 1:length(x$measure))
	if(!is.na(x$measure[i]))
	sc[i]=as.character(sd$col[findInterval(x$measure[i],sd$values)])

breaks<-sd$values

layout(matrix(data=c(2,1), nrow=1, ncol=2), widths=c(8,1), heights=c(8,1))

# Color Scale first
par(mar = c(20,1,20,7),oma=c(0.2,0.2,0.2,0.2),mex=0.5)           
image(x=1, y=0:length(breaks),z=t(matrix(breaks))*1.001
      ,col=colours[1:length(breaks)-1]
       ,axes=FALSE
       ,breaks=breaks
      ,xlab="",ylab=""
      ,xaxt="n")

axis(4,at=0:(length(breaks)-1),labels=round(breaks),col="white",las=1)
abline(h=c(1:length(breaks)),col="white",lwd=2,xpd=F)


#Map
z<-map(col=sc,fill=TRUE,lty="blank")
map(add=TRUE,col="gray",fill=FALSE)
title("CO2 emissions (kg per 2000 US$ of GDP)")



#Recipe 2. Creating graphs with regional maps

library(maps)
library(RColorBrewer)


x<-map("state",plot=FALSE)

for(i in 1:length(rownames(USArrests))) {
	for(j in 1:length(x$names)) {
	 if(grepl(rownames(USArrests)[i],x$names[j],ignore.case=T))
		  x$measure[j]<-as.double(USArrests$Murder[i])
	}
}

colours <- brewer.pal(7,"Reds")

sd <- data.frame(col=colours,
					values=seq(min(x$measure[!is.na(x$measure)]),
					max(x$measure[!is.na(x$measure)])*1.0001, 
					length.out=7))

breaks<-sd$values

matchcol<-function(y) {
	as.character(sd$col[findInterval(y,sd$values)])
}


layout(matrix(data=c(2,1), nrow=1, ncol=2), 
		 widths=c(8,1),heights=c(8,1))

# Color Scale first
par(mar = c(20,1,20,7),oma=c(0.2,0.2,0.2,0.2),mex=0.5)           
image(x=1, y=0:length(breaks),z=t(matrix(breaks))*1.001
      ,col=colours[1:length(breaks)-1]
       ,axes=FALSE
       ,breaks=breaks
      ,xlab="", ylab="", xaxt="n")
axis(4,at=0:(length(breaks)-1),labels=round(breaks),col="white",las=1)
abline(h=c(1:length(breaks)),col="white",lwd=2,xpd=F)

#Map
map("state", boundary = FALSE, 
		col=matchcol(x$measure), 
		fill=TRUE,lty="blank")

map("state", col="white",add = TRUE)

title("Murder Rates by US State in 1973 \n (arrests per 100,000 residents)", line=2)


map("county", "new york")

map("state", region = c("california", "oregon", "nevada"))	

map('italy', fill = TRUE, col = brewer.pal(7,"Set1"))



install.packages("sp")
library(sp)

load(url("http://gadm.org/data/rda/FRA_adm1.RData"))

gadm$rainfall<-rnorm(length(gadm$NAME_1),mean=50,sd=15)

spplot(gadm,"rainfall", col.regions = rev(terrain.colors(gadm$rainfall)),
		main="Rainfall  (simulated) in French administrative regions")


#Recipe 3. Plotting data on Google maps

install.packages("rgdal")
library(rgdal)

install.packages("RgoogleMaps")
library(RgoogleMaps)

air<-read.csv("londonair.csv")

london<-GetMap(center=c(51.51,-0.116), 
		zoom =10, destfile = "London.png", 
		maptype = "mobile")

PlotOnStaticMap(london,lat = air$lat, lon = air$lon, 
		cex=2,pch=19,col=as.character(air$color))


london<-GetMap(center=c(51.51,-0.116),zoom =10, 
destfile = "London_satellite.png", maptype = "satellite")

PlotOnStaticMap(london,lat = air$lat, lon = air$lon,
		cex=2,pch=19,col=as.character(air$color))


GetMap(center=c(40.714728,-73.99867), zoom =14, 
		 destfile = "Manhattan.png", maptype = "hybrid");


#Using OpenStreetMap
GetMap.OSM(lonR= c(-74.67102, -74.63943), 
			  latR = c(40.33804,40.3556), 
			  scale = 7500, destfile = "PrincetonOSM.png")


#Recipe 4. Creating and reading KML data

install.packages("rgdal")
library(rgdal)
cities <- readOGR(system.file("vectors", 
										package = "rgdal")[1], "cities")

writeOGR(cities, "cities.kml", "cities", driver="KML")

df <- readOGR("cities.kml", "cities")




#Recipe 5. Working with ESRI shapefiles

install.packages("maptools")
library(maptools)

sfdata <- readShapeSpatial(system.file("shapes/sids.shp", package="maptools")[1], 
							  proj4string=CRS("+proj=longlat"))

plot(sfdata, col="orange", border="white", axes=TRUE)

#Output as shapefile
writeSpatialShape(sfdata,"xxpoly")


install.packages("shapefiles")
library(shapefiles)

sf<-system.file("shapes/sids.shp", package="maptools")[1]
sf<-substr(sf,1,nchar(sf)-4)
sfdata <- read.shapefile(sf)

write.shapefile(sfdata, "newsf")

#CHAPTER 10
#Recipe 1. Exporting graphs in high resolution image formats: PNG, JPEG, BMP, TIFF


png("cars.png",res=200,height=600,width=600)

plot(cars$dist~cars$speed,
main="Relationship between car distance and speed",
xlab="Speed (miles per hour)",
ylab="Distance travelled (miles)",
xlim=c(0,30),
ylim=c(0,140),
xaxs="i",
yaxs="i",
col="red",
pch=19)

dev.off()


png("cars.png",res=200,height=600,width=600)

par(mar=c(4,4,3,1),omi=c(0.1,0.1,0.1,0.1),mgp=c(3,0.5,0),
	 las=1,mex=0.5,
	 cex.main=0.6,cex.lab=0.5,cex.axis=0.5)

plot(cars$dist~cars$speed,
main="Relationship between car distance and speed",
xlab="Speed (miles per hour)",
ylab="Distance travelled (miles)",
xlim=c(0,30),
ylim=c(0,140),
xaxs="i",
yaxs="i",
col="red",
pch=19,
cex=0.5)

dev.off()


#Recipe 2. Exporting graphs in vector formats: SVG, PDF, PS

pdf("cars.pdf")

plot(cars$dist~cars$speed,
main="Relationship between car distance and speed",
xlab="Speed (miles per hour)",
ylab="Distance travelled (miles)",
xlim=c(0,30),
ylim=c(0,140),
xaxs="i",
yaxs="i",
col="red",
pch=19,
cex=0.5)

dev.off()


svg("3067_10_03.svg")
#plot command here
dev.off()

postscript("3067_10_03.ps")
#plot command here
dev.off()


#Exporting to SVG for Windows users
install.packages("Cairo")
library(Cairo)
CairoSVG("3067_10_03.svg")
#plot command here
dev.off()


pdf("multiple.pdf")

for(i in 1:3)
  plot(cars,pch=19,col=i)

dev.off()



pdf("multiple.pdf",colormodel=攃myk?

for(i in 1:3)
  plot(cars,pch=19,col=i)

dev.off()




#Recipe 3. Adding Mathematical and scientific notations (typesetting)

plot(air,las=1,
main=expression(paste("Relationship between ",PM[10]," and ",NO[X])),
xlab=expression(paste(NO[X]," concentrations (",mu*g^-3,")")),
ylab=expression(paste(PM[10]," concentrations (",mu*g^-3,")")))


demo(plotmath)


#Recipe 4. Adding text descriptions to graphs


par(mar=c(12,4,3,2))
plot(rnorm(1000),main="Random Normal Distribution")

desc<-expression(paste("The normal distribution has density ",
f(x) == frac(1,sqrt(2*pi)*sigma)~ plain(e)^frac(-(x-mu)^2,2*sigma^2)))

mtext(desc,side=1,line=4,padj=1,adj=0)

mtext(expression(paste("where ", mu, " is the mean of the distribution and ",sigma," the standard deviation.")),side=1,line=7,padj=1,adj=0)



dailysales<-read.csv("dailysales.csv")

par(mar=c(5,5,12,2))

plot(units~as.Date(date,"%d/%m/%y"),data=dailysales,type="l",las=1,ylab="Units Sold",xlab="Date")

desc<-"The graph below shows sales data for Product A in the month of January 2010. There were a lot of ups and downs in the number of units sold. The average number of units sold was around 5000. The highest sales were recorded on the 27th January, nearly 7000 units sold."

mtext(paste(strwrap(desc,width=80),collapse="\n"),side=3,line=3,padj=0,adj=0)

title("Daily Sales Trends",line=10,adj=0,font=2)



#Recipe 5. Using Graph Templates

themeplot<-function(x,theme,...) {
  i<-which(themes$theme==theme)
  par(bg=as.character(themes[i,]$bg_color),las=1)

  plot(x,type="n",...)
  
  u<-par("usr")
  plotcol=as.character(themes[i,]$plot_color)
  rect(u[1],u[3],u[2],u[4],col=plotcol,border=plotcol)
  
  points(x,col=as.character(themes[i,]$symbol_color),...)
  box()
}

themeplot(rnorm(1000),theme="white",pch=21,main="White")
themeplot(rnorm(1000),theme="lightgray",pch=21,main="Light Gray")
themeplot(rnorm(1000),theme="dark",pch=21,main="Dark")
themeplot(rnorm(1000),theme="pink",pch=21,main="Pink")






#Recipe 6. Choosing font families and styles under Windows, OS X and Linux

par(mar=c(1,1,5,1))
plot(1:200,type="n",main="Fonts under Windows",axes=FALSE,xlab="",ylab="")

text(0,180,"Arial \n(family=\"sans\", font=1)", 
	  family="sans",font=1,adj=0)
text(0,140,"Arial Bold \n(family=\"sans\", font=2)", 
	  family="sans",font=2,adj=0)
text(0,100,"Arial Italic \n(family=\"sans\", font=3)", 
	  family="sans",font=3,adj=0)
text(0,60,"Arial Bold Italic \n(family=\"sans\", font=4)", 
	  family="sans",font=4,adj=0)

text(70,180,"Times \n(family=\"serif\", font=1)", 
	  family="serif",font=1,adj=0)
text(70,140,"Times Bold \n(family=\"serif\", font=2)", 
	  family="serif",font=2,adj=0)
text(70,100,"Times Italic \n(family=\"serif\", font=3)", 
	  family="serif",font=3,adj=0)
text(70,60,"Times Bold Italic \n(family=\"serif\", font=4)", 
	  family="serif",font=4,adj=0)

text(130,180,"Courier New\n(family=\"mono\", font=1)",
	  family="mono",font=1,adj=0)
text(130,140,"Courier New Bold \n(family=\"mono\", font=2)", 
	  family="mono",font=2,adj=0)
text(130,100,"Courier New Italic \n(family=\"mono\", font=3)",
	  family="mono",font=3,adj=0)
text(130,60,"Courier New Bold Italic \n(family=\"mono\", font=4)", 
	  family="mono",font=4,adj=0)



windowsFonts(GE = windowsFont("Georgia"))

text(150,80,"Georgia",family="GE")


#Recipe 7. Choosing fonts for PostScripts and PDFs

pdf("fonts.pdf",family="AvantGarde")
plot(rnorm(100),main="Random Normal Distribution")
dev.off()


postscript("fonts.ps",family="AvantGarde")
plot(rnorm(100),main="Random Normal Distribution")
dev.off()


names(pdfFonts())

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