0423

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

we2 <- c(85,91,74,100,82,84,78,100,51,70)



plot(we,we2,
     pch = 17,
     col= "skyblue",
     main ="統計成績與數學成績之散佈圖",
     xlab ="統計成績",
     ylab ="數學成績")

hist(we,
     col= "lightyellow",
     main ="數學成績或統計成績的直方圖",
     xlab ="統計成績",
     ylab ="數學成績")

# 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,  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 ="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(
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_segment( aes(x=name,xend=name,y=0,yend=value)) +
  geom_point( size=5, color="red", fill=alpha("orange", 0.3), alpha=0.7, shape=21, stroke=2) 

Data <- read.csv("D:/table1_1.csv")

stem(Data$Japanese,scale = 2)

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

summary(Data$Japanese)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   49.00   54.50   62.00   67.90   82.75   91.00