iris$Species
## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica
as.numeric(FALSE)
## [1] 0
as.integer(3.14)
## [1] 3
as.logical(0.45)
## [1] TRUE
setwd("c:/data")
getwd()
## [1] "c:/data"
data("iris")
head(iris) # 데이터셋의 처음 6행 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
head(iris, 3) # 처음 3행만 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
tail(iris) # 끝에서 6행 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
tail(iris, 3) # 끝에서 3행 출력
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica
str(iris) #structure
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
df <- read.csv("mtcars.csv")
head(df)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
tail(df)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2
str(df)
## 'data.frame': 32 obs. of 12 variables:
## $ X : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : int 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : int 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : int 0 0 1 1 0 1 0 1 1 1 ...
## $ am : int 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: int 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: int 4 4 1 1 2 1 4 2 2 4 ...
dim(df)
## [1] 32 12
ls(df)
## [1] "am" "carb" "cyl" "disp" "drat" "gear" "hp" "mpg" "qsec" "vs"
## [11] "wt" "X"
df1 <- read.csv("Data1.csv")
str(df1)
## 'data.frame': 1925 obs. of 26 variables:
## $ Q1 : int 4 4 4 5 4 4 4 4 4 4 ...
## $ Q2 : int 4 4 4 4 4 4 2 2 4 4 ...
## $ Q3 : int 2 4 4 4 4 4 4 4 4 2 ...
## $ Q4 : int 3 4 4 4 4 4 4 4 4 2 ...
## $ Q5 : int 4 4 2 4 4 4 4 4 2 4 ...
## $ Q6 : int 2 3 4 4 4 4 4 4 1 2 ...
## $ Q7 : int 2 2 4 4 4 4 4 4 3 4 ...
## $ Q8 : int 4 4 4 4 4 4 5 5 2 2 ...
## $ Q9 : int 4 4 4 4 2 4 5 5 3 4 ...
## $ Q10 : int 4 4 2 4 4 4 5 5 2 4 ...
## $ Q11 : int 4 4 4 4 4 4 5 5 4 4 ...
## $ Q12 : int 4 4 4 4 4 4 5 5 3 4 ...
## $ Q13 : int 4 4 4 4 4 4 5 5 4 4 ...
## $ Q14 : int 4 4 4 4 4 4 5 5 5 4 ...
## $ Q15 : int 4 4 3 4 4 4 4 2 3 4 ...
## $ Q16 : int 4 4 4 4 4 4 5 2 4 4 ...
## $ Q17 : int 4 3 4 4 4 4 2 2 4 4 ...
## $ Q18 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Q19 : int 4 2 4 4 4 4 4 2 4 2 ...
## $ Q20 : int 4 1 3 4 4 4 4 2 4 2 ...
## $ Gender : int 0 0 0 0 0 0 0 0 1 0 ...
## $ EDU : int 1 1 2 1 2 1 1 1 4 3 ...
## $ BF : num 3.4 4 3.6 4.2 4 4 3.6 3.6 3.6 3.2 ...
## $ BM : num 3.2 3.4 3.6 4 3.6 4 4.6 4.6 2.2 3.2 ...
## $ Happiness: num 4 4 3.8 4 4 4 4.8 4.4 3.8 4 ...
## $ Peace : num 4 2.8 3.8 4 4 4 3.8 2.4 4 3.2 ...
dim(df1)
## [1] 1925 26
ls(df1)
## [1] "BF" "BM" "EDU" "Gender" "Happiness" "Peace"
## [7] "Q1" "Q10" "Q11" "Q12" "Q13" "Q14"
## [13] "Q15" "Q16" "Q17" "Q18" "Q19" "Q2"
## [19] "Q20" "Q3" "Q4" "Q5" "Q6" "Q7"
## [25] "Q8" "Q9"
# 작업 환경의의 모든 객체 삭제
rm(list = ls())
ls()
## character(0)
data <- read.csv("mtcars.csv")
mean(data$mpg)
## [1] 20.09062
median(data$mpg)
## [1] 19.2
var(data$mpg) # 분산산
## [1] 36.3241
sd(data$mpg) # 표준편차
## [1] 6.026948
sum(data$mpg)
## [1] 642.9
range(data$mpg)
## [1] 10.4 33.9
max(data$mpg)
## [1] 33.9
min(data$mpg)
## [1] 10.4
quantile(data$mpg) # 분위수
## 0% 25% 50% 75% 100%
## 10.400 15.425 19.200 22.800 33.900
IQR(data$mpg) # 사분위수 범위
## [1] 7.375
summary(iris) # 중요 함수
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##