#3####
attach(mtcars)
plot(wt,mpg)
abline(lm(mpg~wt))
title('Regression of MPG on Weight')
detach(mtcars)
pdf('mygraph.pdf')
attach(mtcars)
plot(wt,mpg)
abline(lm(mpg~wt))
title('Regression of MPG on Weight')
detach(mtcars)
dev.off()
dose <- c(20,30,40,45,60)
drugA <- c(16,20,27,40,60)
drugB <- c(15,18,25,31,40)
plot(dose,drugA,type = 'b')
par() #查看当前的所有图形参数/
opar <- par(no.readonly = TRUE) #默认设置
par(lty=2,pch=17)
plot(dose,drugA,type = 'b')
par(opar) #还原默认设置
plot(dose,drugA,type = 'b',lty=3,pch=13)
plot(dose,drugA,type = 'b',lty=3,
lwd=3,
cex=3,
pch=21,
col='blue',
fg='red',
bg='black',
col.lab='yellow',
col.axis='green',
)
# col /col.axis/col.lab/col.main/col.sub/fg/bg
# cex /cex.axis/cex.lab/cex.main/cex.sub
# font /font.axis/font.lab/font.main/font.sub/ps/family
par(pin=c(4,3),mai=c(1,.5,1,.2)) #size of picture
plot(dose,drugA,type = 'b',
col='red',lty=2,pch=2,lwd=2,
main='clinicacl trials for drug A',
sub='this is hypothetical data',
xlab = 'dosage',ylab = 'drug response',
xlim = c(0,60),ylim = c(0,70))
title(main = "my title",col.main='red',
sub="my subtitle",col.sub='blue',
xlab = 'my x lable',ylab = 'my y lable',
col.lab = 'green', cex.lab = '0.75')
x <- c(1:10)
y <- x
z <- 10/x
opar <- par(no.readonly = TRUE)
par(mar=c(5,4,4,8) + 0.1)
plot(x,y,type = 'b',
pch=23,col='red',
yaxt='n',xaxt='n',ann = False)
lines(x,z,type = 'b', pch=22,col='blue',lty=2)
axis(2,at=x,labels = x,col.axis='red',fg='grey')
axis(4,at=z,labels = round(z,digits = 2),las=2,cex.axis=0.7,tck=-0.01)
mtext('y=1/x',side = 4,line = 3,cex.lab=3,las=2,col='blue')
title("an example of creative axes",xlab = 'x value',ylab = 'y value')
par(opar)
abline(h=c(1,5,7))
abline(v=seq(1,10,2),lty=2,col='blue')
dose <- c(20,30,40,45,60)
drugA <- c(16,20,27,40,60)
drugB <- c(15,18,25,31,40)
opar <- par(no.readonly = TRUE)
par(lwd = 2, cex = 1.5, font.lab = 1)
plot(dose, drugA, type = 'b',
pch =15, lty = 1, col = 'red', ylim = c(0,60),
main = 'drug A vs. drug B',
xlab = 'drug dosage', ylab = 'drug response')
lines(dose,drugB, type = 'b',
pch=17, lty=2,col='blue')
abline(h=c(30),lwd=1.5,lty=2,col='gray')
library(Hmisc)
minor.tick(nx=4,ny=4,tick.ratio = 0.5)
legend('left', inset = .05,title='drug type',c('a','b'),
lty=c(1,2),pch = c(15,17),col=c('red','blue'))
par(opar)
attach(mtcars)
plot(wt,mpg,
main = 'mileage vs. car weight',
xlab = 'weight',ylab = 'mileage',
pch=18,col='blue')
text(wt,mpg,
row.names(mtcars),
cex = 0.6,pos = 4,col = 'red')
detach(mtcars)
head(mtcars)
opar <- par(no.readonly = TRUE)
par(cex=1.5)
plot(1:7,1:7,type = 'n')
text(3,3,'example of default text')
text(4,4,family='mono','example of mono-spaced text')
attach(mtcars)
opar <- par(no.readonly = TRUE)
par(mfrow=c(2,2))
plot(wt,mpg,main = 'scatterplot of wt vs. mpg')
plot(wt,disp,main='scatterplot of wt vs. disp')
hist(wt, main='hisogram of wt')
boxplot(wt,main='boxplot of wt')
par(opar)
detach(mtcars)
attach(mtcars)
rm(list = ls())
opar <- par(no.readonly = TRUE)
par(mfrow=c(3,1))
hist(wt)
hist(mpg)
hist(disp)
par(opar)
detach(mtcars)
attach(mtcars)
layout(matrix(c(1,1,2,3),2,2,byrow = TRUE))
hist(wt)
hist(mpg)
hist(disp)
detach(mtcars)
attach(mtcars)
layout(matrix(c(1,1,2,3),2,2,byrow = TRUE),
widths = c(3,1),heights = c(1,2)) #?
hist(wt)
hist(mpg)
hist(disp)
detach(mtcars)
opar <- par(no.readonly = TRUE)
par(fig=c(0,0.8,0,0.8))
plot(mtcars$wt,mtcars$mpg,
xlab = 'miles per gallon',
ylab = 'car weight')
par(fig=c(0,0.8,0.55,1),new=TRUE)
boxplot(mtcars$wt,horizontal = TRUE,axes=FALSE)
par(fig=c(0.65,1,0,0.8),new=TRUE)
boxplot(mtcars$mpg,axes=FALSE)
mtext('enhanced scatterplt',side = 3,outer = TRUE,line = -3)
par(opar)
#5####
library(vcd)
counts <- table(Arthritis$Improved)
counts
barplot(counts,main = 'simple bar plot',
xlab = 'improvement',ylab = 'frequency')
barplot(counts,main = 'horizontal bar plot',
xlab = 'frequency',ylab = 'improvement',
horiz = TRUE)
library(vcd)
counts <- table(Arthritis$Improved,Arthritis$Treatment)
counts
barplot(counts,main = 'stacked bar plot', #stacked
xlab = 'treatment',ylab = 'frequency',
col=c('red','yellow','green'),
legend.text = rownames(counts))
barplot(counts,main='grouped bar plot',
xlab = 'treatment',ylab = 'frequency',
col = c('red','green','yellow'),
legend.text = rownames(counts),beside = TRUE)
par(opar)
states <- data.frame(state.region,state.x77)
means <- aggregate(states$Illiteracy,by=list(state.region),FUN=mean)
means <- means[order(means$x),]
means
barplot(means$x, names.arg = means$Group.1)
title('mean Illiteracy Rate')
par(mar = c(5,8,4,2))
par(las=2)
counts <- table(Arthritis$Improved)
barplot(counts,
main = 'treatment outcome',
horiz = TRUE,cex.names = 0.8,
names.arg = c('no improvement','some improvement',
'marked improvment'))
library(vcd)
attach(Arthritis)
counts <- table(Treatment, Improved)
spine(counts,main = 'spingram example')
detach(Arthritis)
par(mfrow=c(2,2))
slices <- c(10,12,4,16,8)
lbls <- c('US','uk','ustralia','germany','france')
pie(slices,labels = lbls,
main = 'simple pie chart')
pct <- round(slices/sum(slices)*100)
lbls2 <- paste(lbls,' ',pct, '%',sep = '')
pie(slices,labels = lbls2,col = rainbow(length(lbls2)),
main = 'pie chart with percentages')
library(plotrix)
pie3D(slices,labels = lbls,explode=0.1,
main='3D pie chart')
mytable <- table(state.region)
lbls3 <- paste(names(mytable),'
',mytable,sep = ' ')
pie(mytable,labels = lbls3,
main = 'pie chart from a table
(with sample sizes)')
library(plotrix)
slices <- c(10,12,4,16,8)
lbls <- c('us','uk','australia','germany','france')
fan.plot(slices,labels = lbls,main='fan plot')
par(mfrow=c(2,2))
hist(mtcars$mpg)
hist(mtcars$mpg,breaks = 12,
col='red',
xlab = 'milees per gallon',
main = 'colored histogram with 12 bins')
hist(mtcars$mpg,freq = FALSE,
breaks=12,
col = 'red',
xlab = 'miles per gallon',
main = 'histogram, rug plot density curve')
rug(jitter(mtcars$mpg))
lines(density(mtcars$mpg),col='blue',lwd=2)
x <- mtcars$mpg
h <- hist(x,
breaks=12,col = 'red',
xlab = 'miles per gallon',
main = 'histogram with moral curve and box')
xfit <- seq(min(x),max(x),length(40))
yfit <- dnorm(xfit,mean = mean(x),sd=sd(x))
yfit <- yfit*diff(h$mids[1:2])*length(x)
lines(xfit,yfit,col='blue',lwd=2)
box()
par(opar)
par(mfrow=c(2,1))
d <- density(mtcars$mpg)
plot(d)
plot(d,main = 'kernel density of miles per gallon')
polygon(d,col='red',borders='blue')
rug(mtcars$mpg,col='brown')
library(sm)
attach(mtcars)
cyl.f <- factor(cyl,levels = c(4,6,8),
labels = c('4 cylinder','6 cylinder',
'8 cylinder'))
sm.density.compare(mpg,cyl,xlab='miles per gallon')
title(main = 'MPG distribution by car cylinders')
colfill<- c(2:(1+length(levels(cyl.f))))
legend(locator(1),levels(cyl.f),fill = colfill)
detach(mtcars)
boxplot(mtcars$mpg,main= 'box plot',ylab='miles per gallon')
View(mtcars)
boxplot(mpg ~ carb
, data = mtcars,
main='car mileage data',
xlab = 'number of cylinders',
ylab = 'miles per gallon')
boxplot(mpg ~ cyl,
notch=TRUE,
varwidth=TRUE,
col='red',
data = mtcars,
main='car mileage data',
xlab = 'number of cylinders',
ylab = 'miles per gallon')
mtcars$cyl.f <- factor(mtcars$cyl,
levels = c(4,6,8),
labels = c('4','6','8'))
mtcars$am.f <- factor(mtcars$am,
levels = c(0,1),
labels = c('auto','standard'))
boxplot(mpg~am.f*cyl.f,
data=mtcars,
varwidth=TRUE,
col=c('gold','darkgreen'),
main='mpg disrtibution by auto type',
xlab = 'auto type',ylab = 'miles per gallon')
library(vioplot)
x1 <- mtcars$mpg[mtcars$cyl==4]
x2 <- mtcars$mpg[mtcars$cyl==6]
x3 <- mtcars$mpg[mtcars$cyl==8]
vioplot(x1,x2,x3,
names = c('4 cyl','6 cyl','8 cyl'),
col = 'gold')
title('vioin plots of miles per gallon',
ylab = 'miles per galllon',
xlab = 'number of cylinders')
dotchart(mtcars$mpg,labels = row.names(mtcars),cex = .7,
main = 'gas mileage for car models',
xlab = 'mile per gallon')
x <- mtcars[order(mtcars$mpg),]
x$cyl <- factor(x$cyl)
x$color[x$cyl==4] <- 'red'
x$color[x$cyl==6] <- 'blue'
x$color[x$cyl==8] <- 'darkgreen'
dotchart(x$mpg,
labels = row.names(x),
cex=.7,
groups = x$cyl,
gcolor = 'black',
color = x$color,
pch=19,
main = 'gas mileage for car models
grouped by cylinder',
xlab = 'miles per gallon')
attach(mtcars)
plot(wt,mpg,
main = 'basic scatter plot of MPG vc. weight',
xlab = 'car weight (1bs/1000)',
ylab = 'mile per gallon', pch=19)
abline(lm(mpg~wt,col='red',lwd=2,lty=2))
lines(lowess(wt,mpg),col='blue',lwd=2,lty=2)
library(car)
scatterplot(mpg~wt|cyl,data=mtcars,lwd=2,span=0.75,
main='scatter plot of MPG vs. weight by # cylinder',
xlab = 'weight of car (lbs/1000)',
ylab = 'mile per gallon',
legend.plot=TRUE,
id.method='identify',
labels = row.names(mtcars),
boxplots='xy'
)
pairs(~mpg+disp+drat+wt,data = mtcars,
main='basic scatter plot matrix')
plot(mpg,disp)
plot(mpg,drat)
library(car)
scatterplotMatrix(~mpg+disp+drat+wt,data=mtcars,
spread=FALSE, smoother.args=list(lty=2),
main='scatter plot matrix via car package')
set.seed(1234)
n <- 1000
c1 <- matrix(rnorm(n,mean = 0,sd=0.5),ncol = 2)
c2 <- matrix(rnorm(n,mean = 3,sd=2),ncol = 2)
mydata <- rbind(c1,c2)
mydata <- data.frame(mydata)
names(mydata) <- c('x','y')
with(mydata,plot(x,y,pch=19,main='scatter plot with 10,000 observations'))
with(mydata,smoothScatter(x,y,main='scatter plot colored by smoothed densities'))
library(hexbin)
with(mydata,{
bin <- hexbin(x,y,xbins=50)
plot(bin,main='hexagonal binning with 10,000 observations')
})
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg,
main = 'basic 3d scatter plot')
detach(mtcars)
library(scatterplot3d)
attach(mtcars)
s3d <- scatterplot3d(wt,disp,mpg,
pch=16,
highlight.3d= T,
type="h",
main='3d scatter plot with vertical lines')
fit <- lm(mpg~wt+disp)
s3d$plane3d(fit)
library(rgl)
attach(mtcars)
plot3d(wt,disp,mpg,col='red',size = 5) ##交互
detach(mtcars)
library(car)
with(mtcars,
scatter3d(wt,disp,mpg))
attach(mtcars)
r <- sqrt(disp/pi)
symbols(wt,mpg,circles = r,inches=0.3,
fg='white',bg='lightblue',
main = 'bubble plot with point size proportional to displacement',
ylab = 'mile per gallon',
xlab = 'weight of car (lbs/1000)')
text(wt,mpg,rownames(mtcars),cex = 0.6)
detach(mtcars)
opar <- par(no.readonly = T)
par(mfrow=c(1,2))
t1 <- subset(Orange,Tree==1)
plot(t1$age,t1$circumference,
xlab='Age(days',
ylab='cricumstance(mm)',
main='orange tree 1 growth')
plot(t1$age,t1$circumference,
xlab='age (days)',
ylab='cricumstance (mm)',
main='orange tree 1 growth',
type='b')
par(opar)
plot(t1$age,t1$circumference,type='n')
class(Orange$Tree)
rm(list = ls())
Orange$Tree <- as.numeric(Orange$Tree)
ntrees <- max(Orange$Tree)
xrange <- range(Orange$age)
yrange <- range(Orange$circumference)
plot(xrange,yrange,type='n',
xlab='Age (days)',
ylab='circumstance (mm)')
colors <- rainbow(ntrees)
linetype <- c(1:ntrees)
plotchar <- seq(18,18+ntrees,1)
for (i in 1:ntrees){
tree <- subset(Orange,Tree==i)
lines(tree$age,tree$circumference,
type = 'b',
lwd=2,
lty=linetype[i],
col=colors[i],
pch=plotchar[i]
)
}
title('Tree growth','example of line plot')
legend(xrange[1],yrange[2],
1:ntrees,
cex = 0.8,
col=colors,
pch = plotchar,lty = linetype,
title = 'Tree')
options(digits = 2)
cor(mtcars)
library(corrgram)
corrgram(mtcars,order = TRUE,lower.panel = panel.shade,
upper.panel = panel.pie,text.panel = panel.txt,
main='corrgram of mtcars intercorrelations')
corrgram(mtcars,order=TRUE,lower.panel = panel.ellipse,
upper.panel = panel.pts,text.panel = panel.txt,
diag.panel = panel.minmax,
main='corrgram of mtcars data using scatter plots anf ellipses')
corrgram(mtcars,order=TRUE,lower.panel = panel.shade,
upper.panel = NULL, text.panel = panel.txt,
main='car milieage data(unsorted)')
cols <- colorRampPalette(c('darkgoldenrod4','burlywood1','darkkhaki','darkgreen'))
corrgram(mtcars,order = TRUE,col.regions = cols,
lower.panel = panel.shade,
upper.panel = panel.conf,
text.panel = panel.txt,
main='A CORRGRAM (or hourse) of a different color')
ftable(Titanic)
library(vcd)
mosaic(~Class+Sex+Age+Survived,data = Titanic,shade=TRUE,legend=TRUE)