第三回（10月30日） Task Check and Weekly Assignment
To Do
□ Rstudioのプロジェクトを始める
□ オブジェクトに代入してみる
□ 関数に代入してみる
□ ベクトル，マトリックス，リスト，データフレーム型の変数を作ってみる
□ サンプルデータをMoodleからダウンロードする
□ サンプルデータをRstudioに読み込む
□ 性別変数をfactor型に変更する
□ 変数の要約をする
Assignment
サンプルデータをsummary関数で要約した結果を提出しなさい。
SampleCode and Expected Response
obj <- 2
obj
## [1] 2
obj <- 3
obj
## [1] 3
obj2 <- 2
obj3 <- 3
obj2 + obj3
## [1] 5
obj <- c(1, 2, 3)
obj
## [1] 1 2 3
obj <- c(1:10)
obj
## [1] 1 2 3 4 5 6 7 8 9 10
obj * 2
## [1] 2 4 6 8 10 12 14 16 18 20
obj <- matrix(c(1:10), nrow = 5)
obj
## [,1] [,2]
## [1,] 1 6
## [2,] 2 7
## [3,] 3 8
## [4,] 4 9
## [5,] 5 10
obj * 2
## [,1] [,2]
## [1,] 2 12
## [2,] 4 14
## [3,] 6 16
## [4,] 8 18
## [5,] 10 20
obj <- list(name = c("kosugi", "tanaka", "suzuki"), sex = c("male", "female",
"male"), hight = c(170, 160), weight = c(70.6, 80.9, 90.6, 40.3))
obj
## $name
## [1] "kosugi" "tanaka" "suzuki"
##
## $sex
## [1] "male" "female" "male"
##
## $hight
## [1] 170 160
##
## $weight
## [1] 70.6 80.9 90.6 40.3
obj$name
## [1] "kosugi" "tanaka" "suzuki"
obj$weight
## [1] 70.6 80.9 90.6 40.3
str(obj)
## List of 4
## $ name : chr [1:3] "kosugi" "tanaka" "suzuki"
## $ sex : chr [1:3] "male" "female" "male"
## $ hight : num [1:2] 170 160
## $ weight: num [1:4] 70.6 80.9 90.6 40.3
obj <- data.frame(list(name = c("kosugi", "tanaka", "suzuki"), sex = c(1, 2,
1), hight = c(170, 160, 170), weight = c(70.6, 80.9, 90.6)))
obj
## name sex hight weight
## 1 kosugi 1 170 70.6
## 2 tanaka 2 160 80.9
## 3 suzuki 1 170 90.6
str(obj)
## 'data.frame': 3 obs. of 4 variables:
## $ name : Factor w/ 3 levels "kosugi","suzuki",..: 1 3 2
## $ sex : num 1 2 1
## $ hight : num 170 160 170
## $ weight: num 70.6 80.9 90.6
obj$sex
## [1] 1 2 1
obj$sex <- factor(obj$sex, labels = c("male", "female"))
obj
## name sex hight weight
## 1 kosugi male 170 70.6
## 2 tanaka female 160 80.9
## 3 suzuki male 170 90.6
str(obj)
## 'data.frame': 3 obs. of 4 variables:
## $ name : Factor w/ 3 levels "kosugi","suzuki",..: 1 3 2
## $ sex : Factor w/ 2 levels "male","female": 1 2 1
## $ hight : num 170 160 170
## $ weight: num 70.6 80.9 90.6
obj$hight
## [1] 170 160 170
obj[3, ]
## name sex hight weight
## 3 suzuki male 170 90.6
obj[, 2]
## [1] male female male
## Levels: male female
obj[3, 2]
## [1] male
## Levels: male female
obj[3, 2] <- NA
summary(obj)
## name sex hight weight
## kosugi:1 male :1 Min. :160 Min. :70.6
## suzuki:1 female:1 1st Qu.:165 1st Qu.:75.8
## tanaka:1 NA's :1 Median :170 Median :80.9
## Mean :167 Mean :80.7
## 3rd Qu.:170 3rd Qu.:85.8
## Max. :170 Max. :90.6
sample <- read.csv("sample(mac).csv", head = T, na.strings = "*")
summary(sample)
## ID class sex height weight
## Min. : 1.0 A:34 Min. :1.0 Min. :132 Min. :33.2
## 1st Qu.: 25.8 B:33 1st Qu.:1.0 1st Qu.:145 1st Qu.:50.6
## Median : 50.5 C:33 Median :1.5 Median :150 Median :56.0
## Mean : 50.5 Mean :1.5 Mean :151 Mean :56.8
## 3rd Qu.: 75.2 3rd Qu.:2.0 3rd Qu.:157 3rd Qu.:63.1
## Max. :100.0 Max. :2.0 Max. :172 Max. :87.0
##
## kokugo sansuu rika syakai
## Min. :34.0 Min. :58.0 Min. :34.0 Min. :20.0
## 1st Qu.:55.0 1st Qu.:68.0 1st Qu.:46.5 1st Qu.:40.8
## Median :64.0 Median :72.0 Median :51.0 Median :48.0
## Mean :64.5 Mean :71.5 Mean :50.5 Mean :49.4
## 3rd Qu.:74.0 3rd Qu.:75.5 3rd Qu.:54.0 3rd Qu.:57.2
## Max. :94.0 Max. :86.0 Max. :66.0 Max. :86.0
## NA's :1 NA's :1 NA's :1
## eigo
## Min. :25.0
## 1st Qu.:49.0
## Median :61.0
## Mean :59.9
## 3rd Qu.:71.0
## Max. :94.0