Untitled

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="統計及數學成績散佈圖",xlab="統計成績",ylab="數學成績")

hist(Math,
     col= "lightyellow",
     main ="數學直方圖",
     xlab ="math",
     ylab ="PPL")

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


library(ggplot2)


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)))

# Library
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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

 
# plot
ggplot(data, aes(x=x, y=y)) +
  geom_segment( aes(x=x, xend=x, y=0, yend=y)) +
  geom_point( size=5, color="red", fill=alpha("orange", 0.3), alpha=0.7, shape=21, stroke=2) 

setwd("C:/Users/wenzao/Desktop/")

data <- read.csv("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

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$Japanese, 1 / 4) #第一四分位數
Q3 <- quantile(data$Japanese, 3 / 4) #第三四分位數
Q1

##  25% 
## 54.5

Q3

##   75% 
## 82.75