1000

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 = 17,
     col= "skyblue",
     main ="班上的數學與統計成績",
     xlab ="統計成績",
     ylab ="數學成績")

Math <- c(85,91,74,100,82,84,78,100,51,70)
hist(Math,
     col= "lightyellow",
     main ="班上的數學成績",
     xlab ="數學成績",
     ylab ="次數")

# 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")

# create data for the graph
x <- 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")

# Plot the chart with title and terrain color pallet.
pie (x, labels, main="大學生最喜歡參加的社團次數-圓餅圖",col=cm.colors(length(x)))

# Libraries
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))
 
# Plot
ggplot(data, aes(x=name, y=value)) +
  geom_point() + 
  geom_segment( aes(x=name, xend=name, y=0, yend=value))

library(readr)
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

mean(Data$Japanese)      #平均數

## [1] 67.9

median(Data$Japanese)    #中位數

## [1] 62

min(Data$Japanese)

## [1] 49

max(Data$Japanese)

## [1] 91

as.numeric(names(table(Data$Japanese)))[which.max(table(Data$Japanese))] #眾數

## [1] 49

sd(Data$Japanese)  #標準差

## [1] 16.25115

var(Data$Japanese) #變異數

## [1] 264.1

Q1 <- quantile(Data$Japanese, 1 / 4) 
Q1

##  25% 
## 54.5

Q3 <- quantile(Data$Japanese, 3 / 4) 
Q3

##   75% 
## 82.75