唐靖堯

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

plot(Statistic,Math,
     pch = 17,
     col= "skyblue",
     main ="班上數學成績採樣",
     xlab ="Statistic",
     ylab ="Math")

hist(Math,
     col= "lightyellow",
     main ="班上數學成績採樣",
     xlab ="Statistic",
     ylab ="Math")

# 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") 

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 ="參加社團的比例", col=heat.colors(length(data)))

library(readr)
Data<-read_csv("D:/1.csv")

## Rows: 10 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Name
## dbl (5): Statistic, Math, Japanese, Management, Accounting
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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)

## [1] 67.9

median(Data$Japanese)

## [1] 62

as.numeric(names(table(Data$Japanese)))[which.max(table(Data$Japanese))]

## [1] 49

sd(Data$Japanese)

## [1] 16.25115

var(Data$Japanese)

## [1] 264.1

Q1 <- quantile(Data$Japanese, 1 / 4) 
Q2 <- quantile(Data$Japanese, 2 / 4) 
Q3 <- quantile(Data$Japanese, 3 / 4) 

P1 <- quantile(Data$Japanese, 1 / 10) 
P2 <- quantile(Data$Japanese, 2 / 10) 
P3 <- quantile(Data$Japanese, 3 / 10) 

P5 <- quantile(Data$Japanese, 5 / 10)

Q1

##  25% 
## 54.5

Q3

##   75% 
## 82.75