R그래프(R차트) 2

R그래프(R차트) 2

1. PGA 골프 투어 분석

players<-read.csv(‘c:/image/players.csv’)

kor<-subset(players,Country==’KOR’)

kor.o<-kor[order(kor$Ht),]

par(mar=c(5,10,5,5))

barplot(kor.o$Ht,names.arg=kor.o$Players,horiz=TRUE,border=NA,xlim=c(0,200),las=1,main=’Korea Playes’,col=rainbow(10))

2. 대륙별 선수들의 평균키로 세로 막대그래프 만들기

players<-read.csv(‘c:/image/players.csv’)

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

ah<-aggregate(Ht~Continent,data=players,mean)

ah.o<-ah[order(ah$Ht,decreasing=FALSE),]

barplot(ah.o$Ht,ylim=c(0,200),names.arg=ah.o$Continent,border=NA,las=1,col=rainbow(10))

3. 선수 키에 대한 히스토그램 만들기

players<-read.csv(‘c:/image/players.csv’)

hist(players$Ht,main=’PGA Players Height’,ylim=c(0,250),xlab=’cm’,col=rainbow(10))

4. 선수 키에 대한 히스토그램 (키의 분포 범위를 조정)

players<-read.csv(‘c:/image/players.csv’)

par(mflow=c(1,3),mar=c(3,3,3,3))

hist(players$Ht,main=’PGA Players Height’,ylim=c(0,160),xlab=’cm’,breaks=seq(160,200,1),col=rainbow(10))

//hist(players$Ht,main=’PGA Players Height’,ylim=c(0,160),xlab=’cm’,breaks=seq(160,200,2),col=rainbow(10))

//hist(players$Ht,main=’PGA Players Height’,ylim=c(0,160),xlab=’cm’,breaks=seq(160,200,5),col=rainbow(10))

5. 선수 키에 대한 히스토그램 (키의 분포 범위를 조정)

players<-read.csv(‘c:/image/players.csv’)

par(mflow=c(1,3),mar=c(3,3,3,3))

//hist(players$Ht,main=’PGA Players Height’,ylim=c(0,0.05),xlab=’cm’,breaks=c(seq(160,170,2),185,200),col=rainbow(10))

//hist(players$Ht,main=’PGA Players Height’,ylim=c(0,0.05),xlab=’cm’,breaks=c(160,170,seq(185,200,2)),col=rainbow(10))

hist(players$Ht,main=’PGA Players Height’,ylim=c(0,0.05),xlab=’cm’,breaks=c(160,seq(170,185,1),200),col=rainbow(10))

6. PGA 선수 키에 대한 히스토그램 – 대륙별 평균키

players<-read.csv(‘c:/image/players.csv’)

par(mfrow=c(2,3), las=1, mar=c(5,5,4,1))

continent<-unique(players$Continent)

for (i in 1:length(continent)) {

currPlayers <- subset(players, Continent==continent[i])

hist(currPlayers$Ht,main=continent[i],breaks=160:200,xlab=’cm’,border=’#ffffff’,col=rainbow(10),lwd=0.4)

}

7. 회귀분석

regression <-read.csv(‘c:/image/regression.csv’)

plot(regression$height ~ regression$weight, main=’평균키와 몸무게’, xlab=’Height’, ylab=’Weight’, col=rainbow(100))

8. 회귀분석과 단순(선형)회귀분석

regression <-read.csv(‘c:/image/regression.csv’)

plot(regression$height ~ regression$weight, main=’평균키와 몸무게’, xlab=’Height’, ylab=’Weight’, col=rainbow(100))

r<-lm(regression$height ~ regression$weight)

abline(r)

 

9. 월별 평균온도의 박스 플롯

install.packages(‘ggplot2’)

library(ggplot2)

seoul<-read.csv(‘c:/image/converted-weather-seoul.csv’)

ggplot(seoul,aes(factor(Month),MeanTemp))+geom_boxplot()