Untitled

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 ="數學和統計成績",
     xlab ="統計",
     ylab ="數學")

we <- c(68,85,74,88,63,78,90,80,58,63)
hist(we,
     col= "lightyellow",
     main ="統計成績",
     xlab ="成績",
     ylab ="次數")

# Load ggplot2
library(ggplot2)
# Load ggplot2
library(ggplot2)

# Create data
data <- data.frame(
  name=c("公益活動","知識閱讀","科技創新","娛樂休閒","體育競技") ,  
  value=c(25,82,28,185,36)
  )

# Barplot
ggplot(data, aes(x=name, y=value)) + 
  geom_bar(stat = "identity", width=0.2,  fill="skyblue") 

data2<- c(25,82,28,185,36)
labels <- c("公益活動","知識閱讀","科技創新","娛樂休閒","體育競技")

pie(data2,labels,main ="大學生最喜歡參加的社團", col=heat.colors(length(data2)))

# 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

# 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) 

v<-c(84,63,61,49,89,51,59,53,79,91)
stem(v)

## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   4 | 9
##   5 | 139
##   6 | 13
##   7 | 9
##   8 | 49
##   9 | 1

japanes <- c(84,63,61,49,89,51,59,53,79,91)
mean(japanes)

## [1] 67.9

median(japanes)

## [1] 62

as.numeric(names(table(japanes)))[which.max(table(japanes))]

## [1] 49

sd(japanes)

## [1] 16.25115

var(japanes)

## [1] 264.1

Q1 <- quantile(japanes, 1 / 4) 
Q2 <- quantile(japanes, 2 / 4) 
Q3 <- quantile(japanes, 3 / 4) 

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

P5 <- quantile(japanes, 5 / 10) 

Q1

##  25% 
## 54.5

Q3

##   75% 
## 82.75