Untitled

Data <- read.csv("D:/05.csv")

Data

##      Name Statistic Math Japanese Management Accounting
## 1  張青松        68   85       84         89         86
## 2  王奕翔        85   91       63         76         66
## 3  田新雨        74   74       61         80         69
## 4  徐麗娜        88  100       49         71         66
## 5  張志傑        63   82       89         78         80
## 6  趙穎睿        78   84       51         60         60
## 7  王智強        90   78       59         72         66
## 8  宋媛婷        80  100       53         73         70
## 9  袁四方        58   51       79         91         85
## 10 張建國        63   70       91         85         82

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 ="班上的統計與數學成績",
     xlab ="Statistic",
     ylab ="Math")

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(
  n=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=n, y=value)) + 
  geom_bar(stat = "identity")

data2<- 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(data2,labels,main="大學生最喜歡參加的社團次數分配表",col=heat.colors(length(data2)))

# 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") 

# 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 data3
data3 <- data.frame(
  name = c("娛樂休閒", "知識閱讀", "體育競技", "科學創新", "公益活動"),
  score = c(185, 82, 36, 28, 25)
)

# Plot
ggplot(data3, aes(x = name, y = score)) +
  geom_segment(aes(x = name, xend = name, y = 0, yend = score)) +
  geom_point(size = 5, color = "red", fill = alpha("orange", 0.3), alpha = 0.7, shape = 21, stroke = 2) +
  labs(x = "項目", y = "分數", title = "各項活動得分分布") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Data <- read.csv("D:/05.csv")

Data

##      Name Statistic Math Japanese Management Accounting
## 1  張青松        68   85       84         89         86
## 2  王奕翔        85   91       63         76         66
## 3  田新雨        74   74       61         80         69
## 4  徐麗娜        88  100       49         71         66
## 5  張志傑        63   82       89         78         80
## 6  趙穎睿        78   84       51         60         60
## 7  王智強        90   78       59         72         66
## 8  宋媛婷        80  100       53         73         70
## 9  袁四方        58   51       79         91         85
## 10 張建國        63   70       91         85         82

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

Japanese <- Data$Japanese

# 1. 平均數
mean_Jp <- mean(Japanese, na.rm = TRUE)

# 2. 中位數
median_Jp <- median(Japanese, na.rm = TRUE)

# 3. 眾數（自訂函數）
get_mode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}
mode_Jp <- get_mode(Japanese)

# 4. 標準差
sd_Jp <- sd(Japanese, na.rm = TRUE)

# 5. 變異數
var_Jp <- var(Japanese, na.rm = TRUE)

# 6 & 7. 第一與第三四分位數
Q1_Jp <- quantile(Japanese, 1/4, na.rm = TRUE)
Q3_Jp <- quantile(Japanese, 3/4, na.rm = TRUE)