library(readxl)
library(readr)
Statistics <- c (68,85,74,88,63,78,90,80,58,63)
Math <- c (85,91,74,100,82,84,78,100,51,70)
Japanese <- c (84,63,61,49,89,51,59,53,79,91)
data <- read.csv("D:/table1_1.csv")
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")

# Load ggplot2
library(ggplot2)
# Create data
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)
library(readr)
Statistics <- c (68,85,74,88,63,78,90,80,58,63)
Math <- c (85,91,74,100,82,84,78,100,51,70)
Japanese <- c (84,63,61,49,89,51,59,53,79,91)
data <-read.csv("D:/table1_1.csv")
stem(data$Japanese)
##
## The decimal point is 1 digit(s) to the right of the |
##
## 4 | 9
## 5 | 139
## 6 | 13
## 7 | 9
## 8 | 49
## 9 | 1
summary(data$Japanese)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 49.00 54.50 62.00 67.90 82.75 91.00
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
library(readxl)
library(readr)
Statistics <- c (68,85,74,88,63,78,90,80,58,63)
Math <- c (85,91,74,100,82,84,78,100,51,70)
Japanese <- c (84,63,61,49,89,51,59,53,79,91)
data <-read.csv("D:/table1_1.csv")
stem(data$Japanese)
##
## The decimal point is 1 digit(s) to the right of the |
##
## 4 | 9
## 5 | 139
## 6 | 13
## 7 | 9
## 8 | 49
## 9 | 1
quantile(data$Japanese, 1 / 4)
## 25%
## 54.5
quantile(data$Japanese, 3 / 4)
## 75%
## 82.75