Untitled

Statistic <- c(68,85,74,88,63,78,90,80,58,63)
Math <- c(85,91,74,100,82,84,78,100,51,70)
plot(Statistic,Math,
     pch=15,
     col="blue",
     main ="班上的統計與數學分數",
     xlab="統計分數",
     ylab="數學分數") 

hist(Statistic,
     col= "purple",
     main ="班上的統計分數",
     xlab ="統計分數",
     ylab ="數學成績")

library(ggplot2)
data = data.frame(
  社團類型=c("休閒娛樂","知識閱讀","體育競技","科學創新","公益活動"),
  參加次數= c(185,82,36,28,25)
  ) 
ggplot(data, aes(x=社團類型,y=參加次數)) +
  geom_bar(stat="identity",width=.8,fill ="blue")

data<- c(185,82,36,28,25)
labels <- c("休閒娛樂","知識閱讀","體育競技","科學創新","公益活動")
pie(data,labels,main ="參加社團比率", col=terrain.colors(length(data)))

we <- c(68,85,74,88,63,78,90,80,58,63)

we2 <- c(85,91,74,100,82,84,78,100,51,70)

## [1] 67
median(we)    #中位數

## [1] 76

## [1] 70
min(we)

## [1] 58

## [1] 55
max(we)

## [1] 90

## [1] 75
as.numeric(names(table(we)))[which.max(table(we))] 

## [1] 63

## [1] 75
sd(we)  #標準差

## [1] 11.32402

## [1] 9.082951
var(we) #變異數

## [1] 128.2333

plot(we,we2,
     pch = 17,
     col= "skyblue",
     main ="班上的體重與身高",
     xlab ="體重",
     ylab ="身高")

data<- c(50,23,35,48)
labels <- c("英文系","法文系","德文","翻譯")

pie(data,labels,main ="學生的比例", col=heat.colors(length(data)))