cyjie

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)

plot(Statistic,
     pch=17,
     col="gray",
     xlab="統計成績",
     ylab="數學成績",
     main="班上的統計與數學成績")

hist(Statistic,
     col= "pink",
     main ="班上的統計成績",
     xlab ="統計成績",
     ylab ="人數")

hist(Math,
     col= "pink",
     main ="班上的數學成績",
     xlab ="數學成績",
     ylab ="人數")

library(ggplot2)

# Create data
data <- data.frame(
  type=c("娛樂休閒","知識閱讀","體育競技","科學創新","公益活動") ,  
  value=c(185,82,36,28,25)
  )

# Barplot
ggplot(data, aes(x=type, y=value)) + 
  geom_bar(stat = "identity", width=0.2,  fill="skyblue")

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

pie(data,labels,main ="大學生最喜歡參加的社團之比例", col=heat.colors(length(data)))

library(readxl)
test <- read.csv("E:/table1_1.csv")

print(test)

##      Name Statistic Math Japanese Management Accounting
## 1  張青松        68   85       84         89         86
## 2  王奕翔        85   91       63         76         66
## 3  田新雨        74   74       61         80         69
## 4  徐麗娜        88  100       49         71         66
## 5  張志傑        63   82       89         78         80
## 6  趙穎睿        78   84       51         60         60
## 7  王智強        90   78       59         72         66
## 8  宋媛婷        80  100       53         73         70
## 9  袁四方        58   51       79         91         85
## 10 張建國        63   70       91         85         82

stem(test$Japanese)

## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   4 | 9
##   5 | 139
##   6 | 13
##   7 | 9
##   8 | 49
##   9 | 1

mean(test$Japanese)

## [1] 67.9

median(test$Japanese)

## [1] 62

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

## [1] 49

sd(test$Japanese)

## [1] 16.25115

var(test$Japanese)

## [1] 264.1

Q1<- quantile(test$Japanese,1/4) 
Q3<- quantile(test$Japanese,3/4)