Untitled

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

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

plot(Statistics,Math,
     pch = 17,
     col= "skyblue",
     main ="班上的Statistics與Math",
     xlab ="Statistics",
     ylab ="Math")

hist(Statistics,
     col= "lightyellow",
     main ="班上的Statistics",
     xlab ="Statistics",
     ylab ="次數")

library(ggplot2)
data <- data.frame(
  編碼.大學生最喜歡參加社團的次書分配=c("5.娛樂休閒","4.知識閱讀","3.體育競技競技","2.科學創新","1.公益活動") ,  
  次書=c(185,82,36,28,25))

# Barplot
ggplot(data, aes(x=編碼.大學生最喜歡參加社團的次書分配,  y=次書)) + 
  geom_bar(stat = "identity", width=0.2) 

data<- c(185,82,36,28,25)
labels <- c("5.娛樂休閒","4.知識閱讀","3.體育競技競技","2.科學創新","1.公益活動")

pie(data,labels,main ="大學生最喜歡參加社團的次書分配", col=rainbow(length(data)))

library(readxl)


Japanese <- c (84,63,61,49,89,51,59,53,79,91)

data <-read.csv("D:/table1_1.csv")

stem(Japanese,scale = 2)

## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   4 | 9
##   5 | 13
##   5 | 9
##   6 | 13
##   6 | 
##   7 | 
##   7 | 9
##   8 | 4
##   8 | 9
##   9 | 1

mean(data$Japanese)

## [1] 67.9

median(data$Japanese)

## [1] 62

as.numeric(names(table(data$Japanese)))[which.max(table(data$Japanese))]

## [1] 49

sd(data$Japanese)

## [1] 16.25115

var(data$Japanese)

## [1] 264.1

quantile(data$Japanese, 1 / 4) 

##  25% 
## 54.5

quantile(data$Japanese, 3 / 4) 

##   75% 
## 82.75

```