0401.R

##R語言資料分析期中考：
##輸入學號：1130730218  和名字：姵妤
##壹、自建資料與圖表(請見社團範例)
#(1-1)建立一個data frame，包括brand品牌與market_share市站率
brand <- c("samsung", "Apple", "oppo")
market_share <- c(31.7, 22.8, 9.3)
smartphone <- data.frame(brand,market_share)
#(1-2)畫長條圖
barplot(sort(market_share,decreasing = TRUE),
        names.arg =c("samsung", "Apple", "oppo"),
        main = "Global smartphone market share",
        sub="by Pei Yu Liu",
        xlab = "brand",
        ylab = "market_share",
        col =c("yellow","purple","pink"))

#貳、比較gender 性別與lunch 營養午餐類型
#讀入外部資料
sp <- read.csv(file = "StudentsPerformance.csv", stringsAsFactors = TRUE)
summary(sp)

##     gender    race.ethnicity     parental.level.of.education          lunch    
##  female:518   group A: 89    associate's degree:222          free/reduced:355  
##  male  :482   group B:190    bachelor's degree :118          standard    :645  
##               group C:319    high school       :196                            
##               group D:262    master's degree   : 59                            
##               group E:140    some college      :226                            
##                              some high school  :179                            
##  test.preparation.course   math.score     reading.score    writing.score   
##  completed:358           Min.   :  0.00   Min.   : 17.00   Min.   : 10.00  
##  none     :642           1st Qu.: 57.00   1st Qu.: 59.00   1st Qu.: 57.75  
##                          Median : 66.00   Median : 70.00   Median : 69.00  
##                          Mean   : 66.09   Mean   : 69.17   Mean   : 68.05  
##                          3rd Qu.: 77.00   3rd Qu.: 79.00   3rd Qu.: 79.00  
##                          Max.   :100.00   Max.   :100.00   Max.   :100.00

#欄位名稱------------------------------------------
# gender 性別
# race.ethnicity 種族分群
# parental.level.of.education  父母教育程度
# lunch 營養午餐類型(free/reduced免費或減免餐費，standard為一般類別)
# test.preparation.course
# math.score 數學成績
# reading.score 閱讀成績
# writing.score 寫作成績
#(2-1)計算不同gender 性別與lunch 營養午餐人數的比例(交叉分析表)
t <- table(sp$gender,sp$lunch)
t

##         
##          free/reduced standard
##   female          189      329
##   male            166      316

p.t <- prop.table(t)
p.t

##         
##          free/reduced standard
##   female        0.189    0.329
##   male          0.166    0.316

p.t <- p.t*100
p.t

##         
##          free/reduced standard
##   female         18.9     32.9
##   male           16.6     31.6

#(2-2)呈現gender 性別與lunch 營養午餐人數的圖表
barplot(p.t)

barplot(p.t, beside = TRUE)

rownames(p.t)

## [1] "female" "male"

label <- rownames(p.t)
label

## [1] "female" "male"

barplot(p.t, 
        beside =TRUE , 
        legend.text =   label , 
        col =c(2,5),
        main="Gender/lunch",
        sub="By pei yu")

#參、math.score 數學成績的直方圖與盒狀圖
hist(sp$math.score)

boxplot(sp$math.score)

#肆、math.score 數學成績的最大值、最小值、平均數、中位數、標準差
max(sp$math.score)

## [1] 100

min(sp$math.score)

## [1] 0

mean(sp$math.score)

## [1] 66.089

median(sp$math.score)

## [1] 66

sd(sp$math.score)

## [1] 15.16308

#五、呈現以下兩組關係的散佈圖
#(1)math.score 數學成績與與writing.score 寫作成績
#(2)reading.score 閱讀成績與writing.score 寫作成績
par(mfrow = c(1,2))
plot(sp$math.score,sp$writing.score,col="orange",sub="by pei yu")
plot(sp$reading.score,sp$writing.score,col="red",sub="by pei yu")

#六、計算以下兩組關係的相關係數
#(1)reading.score 閱讀成績與math.score 數學成績
cor(sp$reading.score,sp$math.score)

## [1] 0.8175797

#(2)writing.score 寫作成績與math.score 數學成績
cor(sp$writing.score,sp$math.score)

## [1] 0.802642

#七、計算不同gender 性別的math.score 數學成績並畫長條圖
m<-tapply(sp$math.score,sp$gender,mean)
m

##   female     male 
## 63.63320 68.72822

barplot(m, 
        col =c(3,7),
        xlab = "Gender",
        ylab = "Math score",
        main="gender/math score",
        sub="By pei yu")