1111204069

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)      #平均數

## [1] 74.7

## [1] 67
median(Statistic)    #中位數

## [1] 76

## [1] 70
min(Statistic)

## [1] 58

## [1] 55
max(Statistic)

## [1] 90

## [1] 75
as.numeric(names(table(Statistic)))[which.max(table(Statistic))] 

## [1] 63

## [1] 75
sd(Statistic)  #標準差

## [1] 11.32402

## [1] 9.082951
var(Statistic) #變異數

## [1] 128.2333

## [1] 82.5
plot(Statistic,Math,
     pch = 17,
     col= "red",
     main ="成績",
     xlab ="Statistic",
     ylab ="Math")

hist(Math,
     col= "skyblue",
     main ="成績",
     xlab ="Statistic",
     ylab ="Math")

weight <- c(185,82,36,28,25)


mean(weight)    #平均數

## [1] 71.2

median(weight)  #中位數

## [1] 36

as.numeric(names(table(weight)))[which.max(table(weight))]  #眾數

## [1] 25

sd(weight)   #standard deviation

## [1] 67.65131

var(weight)  #variance

## [1] 4576.7

#四分位:把資料切分為四等分，中間的三條線就是四分位，Q1=P25,Q2=P50,Q3=75 
Q1 <- quantile(weight, 1 / 4) 
Q2 <- quantile(weight, 2 / 4) 
Q3 <- quantile(weight, 3 / 4) 

P1 <- quantile(weight, 1 / 10) 
P2 <- quantile(weight, 2 / 10) 
P3 <- quantile(weight, 3 / 10) 

P5 <- quantile(weight, 5 / 10) 


P5

## 50% 
##  36

## 50% 
##  55
Q2

## 50% 
##  36

## 50% 
##  55
median(weight)

## [1] 36

## [1] 55
# Load ggplot2
library(ggplot2)
# Load ggplot2
library(ggplot2)

# Create data
data <- data.frame(
  name=c("娛樂休閒","知識閱讀","體育競技","科學創意","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") 

mean(weight)

## [1] 71.2

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

pie(data,labels,main ="社團類型的比例", 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","科學創意","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)