Untitled

library(readxl)
library(readr)

weight <- c(55,60,70,75,75)

data <- read_excel("D:/table2_2.xlsx")

head(data)

## # A tibble: 6 × 2
##   Gender Height
##   <chr>   <dbl>
## 1 男       160.
## 2 男       172.
## 3 男       170.
## 4 男       168.
## 5 男       171.
## 6 男       174.

stem(weight)

## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   5 | 5
##   6 | 0
##   6 | 
##   7 | 0
##   7 | 55

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

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


mean(Statistic) 

## [1] 74.7

Q1 <- quantile(Statistic, 1 / 4) 
Q2 <- quantile(Statistic, 2 / 4) 

median(Statistic)

## [1] 76

plot(Statistic,Math,
     pch=17,
     col="red",
     xlab="Statistic",
     ylab="Math",
     main="班級的統計與數學成績")

hist(Statistic,
     col= "lightyellow",
     main ="班上的統計與數學成績",
     xlab ="Statistic",
     ylab ="Math")

# Load ggplot2
library(ggplot2)

# Create data
data <- data.frame(
  name=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=name, y=value)) + 
  geom_bar(stat = "identity", width=0.2) 

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

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


stem(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(Japanese)   

## [1] 67.9

Q1 <- quantile(Japanese, 1 / 4) 
Q2 <- quantile(Japanese, 2 / 4) 

median(Japanese)

## [1] 62

Q1

##  25% 
## 54.5

Q2

## 50% 
##  62

sd(Japanese) 

## [1] 16.25115

var(Japanese)

## [1] 264.1