R Graph Cookbook 代碼怎么寫，相信很多沒有經(jīng)驗的人對此束手無策，為此本文總結(jié)了問題出現(xiàn)的原因和解決方法，通過這篇文章希望你能解決這個問題。
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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$City,
args.legend=list(bty="n",horiz=TRUE),
col=brewer.pal(5,"Set1"),
border="white",
ylim=c(0,100),
ylab="Sales Revenue (1,000's of USD)",
main="Sales Figures")
box(bty="l")
#Recipe 2.創(chuàng)建堆疊條形圖 Creating stacked bar charts
install.packages("RColorBrewer")
library(RColorBrewer)
citysales<-read.csv("citysales.csv")
barplot(as.matrix(citysales[,2:4]),
legend.text=citysales$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$City,bty="n",inset=c(-0.3,0),fill=brewer.pal(5,"Set1"))
#Recipe 3. 調(diào)整條形圖方向（水平和垂直）Adjusting the orientation of bars ?horizontal and vertical
barplot(as.matrix(citysales[,2:4]), beside=TRUE,horiz=TRUE,
legend.text=citysales$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$City,bty="n",
inset=c(-0.3,0),fill=brewer.pal(5,"Set1"))
#Recipe 4.調(diào)整桿寬度、間距、顏色和邊界 Adjusting bar widths, spacing, colours and borders
barplot(as.matrix(citysales[,2:4]), beside=TRUE,
legend.text=citysales$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$City,
args.legend=list(bty="n"),
col=brewer.pal(5,"Set1"),
border="white",
xlim=c(0,100),
xlab="Sales Revenue (1,000's of USD)",
main="Sales Figures")
x<-as.matrix(citysales[,2:4])
text(x+2,y,labels=as.character(x))
#Recipe 6. Placing labels inside bars
rain<-read.csv("cityrain.csv")
y<-barplot(as.matrix(rain[1,-1]),horiz=T,col="white",yaxt="n",
main="Monthly Rainfall in Major CitiesJanuary",
xlab="Rainfall (mm)")
x<-0.5*rain[1,-1]
text(x,y,colnames(rain[-1]))
#Recipe 7.創(chuàng)建帶垂直誤差線的條形圖Creating Bar charts with vertical error bars
sales<-t(as.matrix(citysales[,-1]))
colnames(sales)<-citysales[,1]
x<-barplot(sales,beside=T,legend.text=rownames(sales),
args.legend=list(bty="n",horiz=T),
col=brewer.pal(3,"Set2"),border="white",ylim=c(0,100),
ylab="Sales Revenue (1,000's of USD)",
main="Sales Figures")
arrows(x0=x,
y0=sales*0.95,
x1=x,
y1=sales*1.05,
angle=90,
code=3,
length=0.04,
lwd=0.4)
#Creating a function
errorbars<-function(x,y,upper,lower=upper,length=0.04,lwd=0.4,...) {
arrows(x0=x,
y0=y+upper,
x1=x,
y1=y-lower,
angle=90,
code=3,
length=length,
lwd=lwd)
}
errorbars(x,sales,0.05*sales)
#Recipe 8. 帶條件變量的點陣圖Modifying dotplots by grouping variables
install.packages("reshape")
library(reshape)
sales<-melt(citysales)
sales$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(shù) 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(diào)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())看完上述內(nèi)容，你們掌握R Graph Cookbook 代碼怎么寫的方法了嗎？如果還想學到更多技能或想了解更多相關內(nèi)容，歡迎關注創(chuàng)新互聯(lián)行業(yè)資訊頻道，感謝各位的閱讀！
