x<-311 # x=3
x
## [1] 311
3>4
## [1] FALSE
3==4
## [1] FALSE
a<-10
a
## [1] 10
a<-"hello"
a
## [1] "hello"
a<-c(1,2,3)
a
## [1] 1 2 3
max(a)
## [1] 3
min(a)
## [1] 1
mean(a)
## [1] 2
library(dplyr)
##
## 다음의 패키지를 부착합니다: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
mode(a)
## [1] "numeric"
is.numeric(a)
## [1] TRUE
length(a)
## [1] 3
x<-c(1,2)
y<-c("a","b")
xy<-c(x,y)
xy
## [1] "1" "2" "a" "b"
mode(xy)
## [1] "character"
1:5
## [1] 1 2 3 4 5
gender<-c("male", "female", "male")
bloodtype<-c("AB","O","B")
height<-c(170,175,165)
weight<-c(70,65,55)
df<-data.frame(gender,bloodtype,height,weight)
df
## gender bloodtype height weight
## 1 male AB 170 70
## 2 female O 175 65
## 3 male B 165 55
car<-c("kia", "bmw", "toyota")
df2<-data.frame(df, car)
df2
## gender bloodtype height weight car
## 1 male AB 170 70 kia
## 2 female O 175 65 bmw
## 3 male B 165 55 toyota
data(iris)
iris[,c(1:2)]
## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0
iris[,c(1,3,5)]
## Sepal.Length Petal.Length Species
## 1 5.1 1.4 setosa
## 2 4.9 1.4 setosa
## 3 4.7 1.3 setosa
## 4 4.6 1.5 setosa
## 5 5.0 1.4 setosa
## 6 5.4 1.7 setosa
## 7 4.6 1.4 setosa
## 8 5.0 1.5 setosa
## 9 4.4 1.4 setosa
## 10 4.9 1.5 setosa
## 11 5.4 1.5 setosa
## 12 4.8 1.6 setosa
## 13 4.8 1.4 setosa
## 14 4.3 1.1 setosa
## 15 5.8 1.2 setosa
## 16 5.7 1.5 setosa
## 17 5.4 1.3 setosa
## 18 5.1 1.4 setosa
## 19 5.7 1.7 setosa
## 20 5.1 1.5 setosa
## 21 5.4 1.7 setosa
## 22 5.1 1.5 setosa
## 23 4.6 1.0 setosa
## 24 5.1 1.7 setosa
## 25 4.8 1.9 setosa
## 26 5.0 1.6 setosa
## 27 5.0 1.6 setosa
## 28 5.2 1.5 setosa
## 29 5.2 1.4 setosa
## 30 4.7 1.6 setosa
## 31 4.8 1.6 setosa
## 32 5.4 1.5 setosa
## 33 5.2 1.5 setosa
## 34 5.5 1.4 setosa
## 35 4.9 1.5 setosa
## 36 5.0 1.2 setosa
## 37 5.5 1.3 setosa
## 38 4.9 1.4 setosa
## 39 4.4 1.3 setosa
## 40 5.1 1.5 setosa
## 41 5.0 1.3 setosa
## 42 4.5 1.3 setosa
## 43 4.4 1.3 setosa
## 44 5.0 1.6 setosa
## 45 5.1 1.9 setosa
## 46 4.8 1.4 setosa
## 47 5.1 1.6 setosa
## 48 4.6 1.4 setosa
## 49 5.3 1.5 setosa
## 50 5.0 1.4 setosa
## 51 7.0 4.7 versicolor
## 52 6.4 4.5 versicolor
## 53 6.9 4.9 versicolor
## 54 5.5 4.0 versicolor
## 55 6.5 4.6 versicolor
## 56 5.7 4.5 versicolor
## 57 6.3 4.7 versicolor
## 58 4.9 3.3 versicolor
## 59 6.6 4.6 versicolor
## 60 5.2 3.9 versicolor
## 61 5.0 3.5 versicolor
## 62 5.9 4.2 versicolor
## 63 6.0 4.0 versicolor
## 64 6.1 4.7 versicolor
## 65 5.6 3.6 versicolor
## 66 6.7 4.4 versicolor
## 67 5.6 4.5 versicolor
## 68 5.8 4.1 versicolor
## 69 6.2 4.5 versicolor
## 70 5.6 3.9 versicolor
## 71 5.9 4.8 versicolor
## 72 6.1 4.0 versicolor
## 73 6.3 4.9 versicolor
## 74 6.1 4.7 versicolor
## 75 6.4 4.3 versicolor
## 76 6.6 4.4 versicolor
## 77 6.8 4.8 versicolor
## 78 6.7 5.0 versicolor
## 79 6.0 4.5 versicolor
## 80 5.7 3.5 versicolor
## 81 5.5 3.8 versicolor
## 82 5.5 3.7 versicolor
## 83 5.8 3.9 versicolor
## 84 6.0 5.1 versicolor
## 85 5.4 4.5 versicolor
## 86 6.0 4.5 versicolor
## 87 6.7 4.7 versicolor
## 88 6.3 4.4 versicolor
## 89 5.6 4.1 versicolor
## 90 5.5 4.0 versicolor
## 91 5.5 4.4 versicolor
## 92 6.1 4.6 versicolor
## 93 5.8 4.0 versicolor
## 94 5.0 3.3 versicolor
## 95 5.6 4.2 versicolor
## 96 5.7 4.2 versicolor
## 97 5.7 4.2 versicolor
## 98 6.2 4.3 versicolor
## 99 5.1 3.0 versicolor
## 100 5.7 4.1 versicolor
## 101 6.3 6.0 virginica
## 102 5.8 5.1 virginica
## 103 7.1 5.9 virginica
## 104 6.3 5.6 virginica
## 105 6.5 5.8 virginica
## 106 7.6 6.6 virginica
## 107 4.9 4.5 virginica
## 108 7.3 6.3 virginica
## 109 6.7 5.8 virginica
## 110 7.2 6.1 virginica
## 111 6.5 5.1 virginica
## 112 6.4 5.3 virginica
## 113 6.8 5.5 virginica
## 114 5.7 5.0 virginica
## 115 5.8 5.1 virginica
## 116 6.4 5.3 virginica
## 117 6.5 5.5 virginica
## 118 7.7 6.7 virginica
## 119 7.7 6.9 virginica
## 120 6.0 5.0 virginica
## 121 6.9 5.7 virginica
## 122 5.6 4.9 virginica
## 123 7.7 6.7 virginica
## 124 6.3 4.9 virginica
## 125 6.7 5.7 virginica
## 126 7.2 6.0 virginica
## 127 6.2 4.8 virginica
## 128 6.1 4.9 virginica
## 129 6.4 5.6 virginica
## 130 7.2 5.8 virginica
## 131 7.4 6.1 virginica
## 132 7.9 6.4 virginica
## 133 6.4 5.6 virginica
## 134 6.3 5.1 virginica
## 135 6.1 5.6 virginica
## 136 7.7 6.1 virginica
## 137 6.3 5.6 virginica
## 138 6.4 5.5 virginica
## 139 6.0 4.8 virginica
## 140 6.9 5.4 virginica
## 141 6.7 5.6 virginica
## 142 6.9 5.1 virginica
## 143 5.8 5.1 virginica
## 144 6.8 5.9 virginica
## 145 6.7 5.7 virginica
## 146 6.7 5.2 virginica
## 147 6.3 5.0 virginica
## 148 6.5 5.2 virginica
## 149 6.2 5.4 virginica
## 150 5.9 5.1 virginica
iris[,c("Sepal.Length","Species")]
## Sepal.Length Species
## 1 5.1 setosa
## 2 4.9 setosa
## 3 4.7 setosa
## 4 4.6 setosa
## 5 5.0 setosa
## 6 5.4 setosa
## 7 4.6 setosa
## 8 5.0 setosa
## 9 4.4 setosa
## 10 4.9 setosa
## 11 5.4 setosa
## 12 4.8 setosa
## 13 4.8 setosa
## 14 4.3 setosa
## 15 5.8 setosa
## 16 5.7 setosa
## 17 5.4 setosa
## 18 5.1 setosa
## 19 5.7 setosa
## 20 5.1 setosa
## 21 5.4 setosa
## 22 5.1 setosa
## 23 4.6 setosa
## 24 5.1 setosa
## 25 4.8 setosa
## 26 5.0 setosa
## 27 5.0 setosa
## 28 5.2 setosa
## 29 5.2 setosa
## 30 4.7 setosa
## 31 4.8 setosa
## 32 5.4 setosa
## 33 5.2 setosa
## 34 5.5 setosa
## 35 4.9 setosa
## 36 5.0 setosa
## 37 5.5 setosa
## 38 4.9 setosa
## 39 4.4 setosa
## 40 5.1 setosa
## 41 5.0 setosa
## 42 4.5 setosa
## 43 4.4 setosa
## 44 5.0 setosa
## 45 5.1 setosa
## 46 4.8 setosa
## 47 5.1 setosa
## 48 4.6 setosa
## 49 5.3 setosa
## 50 5.0 setosa
## 51 7.0 versicolor
## 52 6.4 versicolor
## 53 6.9 versicolor
## 54 5.5 versicolor
## 55 6.5 versicolor
## 56 5.7 versicolor
## 57 6.3 versicolor
## 58 4.9 versicolor
## 59 6.6 versicolor
## 60 5.2 versicolor
## 61 5.0 versicolor
## 62 5.9 versicolor
## 63 6.0 versicolor
## 64 6.1 versicolor
## 65 5.6 versicolor
## 66 6.7 versicolor
## 67 5.6 versicolor
## 68 5.8 versicolor
## 69 6.2 versicolor
## 70 5.6 versicolor
## 71 5.9 versicolor
## 72 6.1 versicolor
## 73 6.3 versicolor
## 74 6.1 versicolor
## 75 6.4 versicolor
## 76 6.6 versicolor
## 77 6.8 versicolor
## 78 6.7 versicolor
## 79 6.0 versicolor
## 80 5.7 versicolor
## 81 5.5 versicolor
## 82 5.5 versicolor
## 83 5.8 versicolor
## 84 6.0 versicolor
## 85 5.4 versicolor
## 86 6.0 versicolor
## 87 6.7 versicolor
## 88 6.3 versicolor
## 89 5.6 versicolor
## 90 5.5 versicolor
## 91 5.5 versicolor
## 92 6.1 versicolor
## 93 5.8 versicolor
## 94 5.0 versicolor
## 95 5.6 versicolor
## 96 5.7 versicolor
## 97 5.7 versicolor
## 98 6.2 versicolor
## 99 5.1 versicolor
## 100 5.7 versicolor
## 101 6.3 virginica
## 102 5.8 virginica
## 103 7.1 virginica
## 104 6.3 virginica
## 105 6.5 virginica
## 106 7.6 virginica
## 107 4.9 virginica
## 108 7.3 virginica
## 109 6.7 virginica
## 110 7.2 virginica
## 111 6.5 virginica
## 112 6.4 virginica
## 113 6.8 virginica
## 114 5.7 virginica
## 115 5.8 virginica
## 116 6.4 virginica
## 117 6.5 virginica
## 118 7.7 virginica
## 119 7.7 virginica
## 120 6.0 virginica
## 121 6.9 virginica
## 122 5.6 virginica
## 123 7.7 virginica
## 124 6.3 virginica
## 125 6.7 virginica
## 126 7.2 virginica
## 127 6.2 virginica
## 128 6.1 virginica
## 129 6.4 virginica
## 130 7.2 virginica
## 131 7.4 virginica
## 132 7.9 virginica
## 133 6.4 virginica
## 134 6.3 virginica
## 135 6.1 virginica
## 136 7.7 virginica
## 137 6.3 virginica
## 138 6.4 virginica
## 139 6.0 virginica
## 140 6.9 virginica
## 141 6.7 virginica
## 142 6.9 virginica
## 143 5.8 virginica
## 144 6.8 virginica
## 145 6.7 virginica
## 146 6.7 virginica
## 147 6.3 virginica
## 148 6.5 virginica
## 149 6.2 virginica
## 150 5.9 virginica
iris[1:5,]
## 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
iris[1:5,c(1,3)]
## Sepal.Length Petal.Length
## 1 5.1 1.4
## 2 4.9 1.4
## 3 4.7 1.3
## 4 4.6 1.5
## 5 5.0 1.4
iris[,-5]
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8
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.integer(3.14)
## [1] 3
as.numeric(FALSE)
## [1] 0
as.logical(0.0001)
## [1] TRUE
getwd()
## [1] "C:/Users/강원지부 검사/Desktop/R"
setwd("c:/data")
getwd()
## [1] "c:/data"
#data<-read.csv("c:/data/Data1.csv")
data<-read.csv("Data1.csv", header=TRUE, sep=",")
data[1:5,]
## X Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20
## 1 1 4 4 2 3 4 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4
## 2 2 4 4 4 4 4 3 2 4 4 4 4 4 4 4 4 4 3 4 2 1
## 3 3 4 4 4 4 2 4 4 4 4 2 4 4 4 4 3 4 4 4 4 3
## 4 4 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## 5 5 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4
## Gender EDU BF BM Happiness Peace
## 1 0 1 3.4 3.2 4.0 4.0
## 2 0 1 4.0 3.4 4.0 2.8
## 3 0 2 3.6 3.6 3.8 3.8
## 4 0 1 4.2 4.0 4.0 4.0
## 5 0 2 4.0 3.6 4.0 4.0
names(data)
## [1] "X" "Q1" "Q2" "Q3" "Q4" "Q5"
## [7] "Q6" "Q7" "Q8" "Q9" "Q10" "Q11"
## [13] "Q12" "Q13" "Q14" "Q15" "Q16" "Q17"
## [19] "Q18" "Q19" "Q20" "Gender" "EDU" "BF"
## [25] "BM" "Happiness" "Peace"
library(readxl)
data1<-read_excel("Data1.xls")
head(data1);tail(data1)
## # A tibble: 6 × 26
## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 4 2 3 4 2 2 4 4 4 4 4 4
## 2 4 4 4 4 4 3 2 4 4 4 4 4 4
## 3 4 4 4 4 2 4 4 4 4 2 4 4 4
## 4 5 4 4 4 4 4 4 4 4 4 4 4 4
## 5 4 4 4 4 4 4 4 4 2 4 4 4 4
## 6 4 4 4 4 4 4 4 4 4 4 4 4 4
## # ℹ 13 more variables: Q14 <dbl>, Q15 <dbl>, Q16 <dbl>, Q17 <dbl>, Q18 <dbl>,
## # Q19 <dbl>, Q20 <dbl>, Gender <dbl>, EDU <dbl>, BF <dbl>, BM <dbl>,
## # Happiness <dbl>, Peace <dbl>
## # A tibble: 6 × 26
## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 4 4 3 4 4 2 2 3 4 2 2 4 3
## 2 2 2 2 1 2 2 2 2 2 2 1 3 2
## 3 3 2 2 2 3 1 1 1 1 1 3 3 3
## 4 5 4 4 4 4 2 2 2 2 3 3 4 3
## 5 4 4 4 2 2 4 2 4 4 3 3 2 3
## 6 3 3 1 1 2 1 1 1 1 1 4 4 3
## # ℹ 13 more variables: Q14 <dbl>, Q15 <dbl>, Q16 <dbl>, Q17 <dbl>, Q18 <dbl>,
## # Q19 <dbl>, Q20 <dbl>, Gender <dbl>, EDU <dbl>, BF <dbl>, BM <dbl>,
## # Happiness <dbl>, Peace <dbl>
data2<-read.table("Data1.txt",header=TRUE)
head(data2)
## Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Gender
## 1 4 4 2 3 4 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 0
## 2 4 4 4 4 4 3 2 4 4 4 4 4 4 4 4 4 3 4 2 1 0
## 3 4 4 4 4 2 4 4 4 4 2 4 4 4 4 3 4 4 4 4 3 0
## 4 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
## 5 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 0
## 6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
## EDU BF BM Happiness Peace
## 1 1 3.4 3.2 4.0 4.0
## 2 1 4.0 3.4 4.0 2.8
## 3 2 3.6 3.6 3.8 3.8
## 4 1 4.2 4.0 4.0 4.0
## 5 2 4.0 3.6 4.0 4.0
## 6 1 4.0 4.0 4.0 4.0
data(mtcars)
write.csv(mtcars,file="mtcars.csv")
data(iris)
head(iris)
## 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)
## 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)
## 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
str(iris)
## '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 ...
glimpse(iris)
## Rows: 150
## Columns: 5
## $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
## $ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
## $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
## $ Petal.Width <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
## $ Species <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…
View(iris)
dim(iris)
## [1] 150 5
ls(iris)
## [1] "Petal.Length" "Petal.Width" "Sepal.Length" "Sepal.Width" "Species"
ls()
## [1] "a" "bloodtype" "car" "data" "data1" "data2"
## [7] "df" "df2" "gender" "height" "iris" "mtcars"
## [13] "weight" "x" "xy" "y"
rm(list=ls())
ls()
## character(0)
mean(mtcars$mpg)
## [1] 20.09062
var(mtcars$mpg)
## [1] 36.3241
sd(mtcars$mpg)
## [1] 6.026948
sum(mtcars$mpg)
## [1] 642.9
range(mtcars$mpg)
## [1] 10.4 33.9
max(mtcars$mpg)
## [1] 33.9
min(mtcars$mpg)
## [1] 10.4
quantile(mtcars$mpg)
## 0% 25% 50% 75% 100%
## 10.400 15.425 19.200 22.800 33.900
IQR(mtcars$mpg)
## [1] 7.375
IQR(mtcars$mpg)
## [1] 7.375
summary(mtcars)
## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000
df<-read.csv("Data1.csv")
glimpse(df)
## Rows: 1,925
## Columns: 27
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ Q1 <int> 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
## $ Q2 <int> 4, 4, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 2, 2, …
## $ Q3 <int> 2, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 4, 4, 3, 2, 3, …
## $ Q4 <int> 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 4, 2, 4, 4, 4, 2, 2, 4, …
## $ Q5 <int> 4, 4, 2, 4, 4, 4, 4, 4, 2, 4, 4, 2, 4, 4, 4, 4, 4, 3, 1, 2, …
## $ Q6 <int> 2, 3, 4, 4, 4, 4, 4, 4, 1, 2, 2, 2, 4, 4, 3, 5, 2, 2, 1, 4, …
## $ Q7 <int> 2, 2, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 5, 4, 4, 5, 4, 3, 4, 4, …
## $ Q8 <int> 4, 4, 4, 4, 4, 4, 5, 5, 2, 2, 4, 4, 4, 4, 3, 5, 4, 2, 4, 4, …
## $ Q9 <int> 4, 4, 4, 4, 2, 4, 5, 5, 3, 4, 4, 4, 2, 2, 4, 5, 2, 4, 2, 4, …
## $ Q10 <int> 4, 4, 2, 4, 4, 4, 5, 5, 2, 4, 2, 4, 4, 4, 3, 4, 4, 3, 2, 3, …
## $ Q11 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 3, 4, 4, 4, 4, 5, 4, 3, 3, …
## $ Q12 <int> 4, 4, 4, 4, 4, 4, 5, 5, 3, 4, 4, 3, 4, 3, 3, 4, 5, 4, 4, 2, …
## $ Q13 <int> 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 2, 4, 4, 4, 5, 4, 4, 4, …
## $ Q14 <int> 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 4, 4, 3, 4, 5, 4, 5, 4, 4, 4, …
## $ Q15 <int> 4, 4, 3, 4, 4, 4, 4, 2, 3, 4, 4, 3, 1, 4, 4, 4, 5, 4, 4, 4, …
## $ Q16 <int> 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4, 4, 4, …
## $ Q17 <int> 4, 3, 4, 4, 4, 4, 2, 2, 4, 4, 4, 4, 3, 2, 4, 5, 4, 4, 3, 4, …
## $ Q18 <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 4, 4, 4, …
## $ Q19 <int> 4, 2, 4, 4, 4, 4, 4, 2, 4, 2, 4, 4, 1, 4, 4, 4, 5, 4, 2, 3, …
## $ Q20 <int> 4, 1, 3, 4, 4, 4, 4, 2, 4, 2, 4, 4, 4, 2, 4, 5, 5, 4, 2, 4, …
## $ Gender <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, …
## $ EDU <int> 1, 1, 2, 1, 2, 1, 1, 1, 4, 3, 2, 1, 1, 3, 3, 2, 1, 1, 1, 4, …
## $ BF <dbl> 3.4, 4.0, 3.6, 4.2, 4.0, 4.0, 3.6, 3.6, 3.6, 3.2, 4.0, 3.2, …
## $ BM <dbl> 3.2, 3.4, 3.6, 4.0, 3.6, 4.0, 4.6, 4.6, 2.2, 3.2, 3.2, 3.6, …
## $ Happiness <dbl> 4.0, 4.0, 3.8, 4.0, 4.0, 4.0, 4.8, 4.4, 3.8, 4.0, 4.0, 3.4, …
## $ Peace <dbl> 4.0, 2.8, 3.8, 4.0, 4.0, 4.0, 3.8, 2.4, 4.0, 3.2, 4.0, 3.9, …
df$Gender<-factor(df$Gender)
library(ggplot2)
data("diamonds")
glimpse(diamonds)
## Rows: 53,940
## Columns: 10
## $ carat <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
## $ cut <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
## $ color <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
## $ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
## $ depth <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
## $ table <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
## $ price <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
## $ x <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
## $ y <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
## $ z <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…
diamonds1<-diamonds %>% rename(c=clarity, p=price)
glimpse(diamonds1)
## Rows: 53,940
## Columns: 10
## $ carat <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.30…
## $ cut <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Very …
## $ color <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I, E…
## $ c <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, SI…
## $ depth <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64.0…
## $ table <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58, …
## $ p <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 342,…
## $ x <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.25…
## $ y <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.28…
## $ z <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.73…
diamonds %>% head %>% dim #== dim(head(diamonds))
## [1] 6 10
count(diamonds1,cut)
## # A tibble: 5 × 2
## cut n
## <ord> <int>
## 1 Fair 1610
## 2 Good 4906
## 3 Very Good 12082
## 4 Premium 13791
## 5 Ideal 21551
count(diamonds1,c)
## # A tibble: 8 × 2
## c n
## <ord> <int>
## 1 I1 741
## 2 SI2 9194
## 3 SI1 13065
## 4 VS2 12258
## 5 VS1 8171
## 6 VVS2 5066
## 7 VVS1 3655
## 8 IF 1790
df1<-diamonds %>% select(carat,price)
head(df1,3)
## # A tibble: 3 × 2
## carat price
## <dbl> <int>
## 1 0.23 326
## 2 0.21 326
## 3 0.23 327
df2<-diamonds %>% select(-carat,-price)
head(df2,3)
## # A tibble: 3 × 8
## cut color clarity depth table x y z
## <ord> <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Ideal E SI2 61.5 55 3.95 3.98 2.43
## 2 Premium E SI1 59.8 61 3.89 3.84 2.31
## 3 Good E VS1 56.9 65 4.05 4.07 2.31
df2<-diamonds %>% select(-1,-4)
head(df2,3)
## # A tibble: 3 × 8
## cut color depth table price x y z
## <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 Ideal E 61.5 55 326 3.95 3.98 2.43
## 2 Premium E 59.8 61 326 3.89 3.84 2.31
## 3 Good E 56.9 65 327 4.05 4.07 2.31
df2<-diamonds %>% select(1,3,5)
head(df2,3)
## # A tibble: 3 × 3
## carat color depth
## <dbl> <ord> <dbl>
## 1 0.23 E 61.5
## 2 0.21 E 59.8
## 3 0.23 E 56.9
df2<-diamonds %>% slice(2)
head(df2,3)
## # A tibble: 1 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
df2<-diamonds %>% filter(cut=="Good")
head(df2,3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 2 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 3 0.3 Good J SI1 64 55 339 4.25 4.28 2.73
d_max<-max(diamonds$price)
diamonds %>% filter(price==d_max)
## # A tibble: 1 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 2.29 Premium I VS2 60.8 60 18823 8.5 8.47 5.16
diamonds %>% filter(cut!="Premium") %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 3 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
diamonds %>% filter(price>=1000) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
## 2 0.86 Fair E SI2 55.1 69 2757 6.45 6.33 3.52
## 3 0.7 Ideal G VS2 61.6 56 2757 5.7 5.67 3.5
diamonds %>% filter(price!=1000) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
diamonds %>% filter(price!=1000 & cut=="Ideal" & color=="E") %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.26 Ideal E VVS2 62.9 58 554 4.02 4.06 2.54
## 3 0.7 Ideal E SI1 62.5 57 2757 5.7 5.72 3.57
diamonds %>% filter(carat<1 | carat>0.5) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
diamonds %>% filter(cut%in%c("Ideal","Good")) %>% head(3)
## # A tibble: 3 × 10
## carat cut color clarity depth table price x y z
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 3 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
diamonds %>% select(carat,depth,price) %>%
filter(depth==max(depth)|price==min(price))
## # A tibble: 4 × 3
## carat depth price
## <dbl> <dbl> <int>
## 1 0.23 61.5 326
## 2 0.21 59.8 326
## 3 0.5 79 2579
## 4 0.5 79 2579
diamonds %>% mutate(Ratio=price/carat,Double=Ratio*2) %>% head(3)
## # A tibble: 3 × 12
## carat cut color clarity depth table price x y z Ratio Double
## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 1417. 2835.
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 1552. 3105.
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 1422. 2843.
diamonds %>% summarise(AvgPrice=mean(price),
MedianPrice=median(price),
AvgCarat=mean(carat))
## # A tibble: 1 × 3
## AvgPrice MedianPrice AvgCarat
## <dbl> <dbl> <dbl>
## 1 3933. 2401 0.798
diamonds %>% group_by(cut) %>%
summarize(AvgPrice=mean(price),SumCarat=sum(carat))
## # A tibble: 5 × 3
## cut AvgPrice SumCarat
## <ord> <dbl> <dbl>
## 1 Fair 4359. 1684.
## 2 Good 3929. 4166.
## 3 Very Good 3982. 9743.
## 4 Premium 4584. 12301.
## 5 Ideal 3458. 15147.
diamonds %>% group_by(cut) %>%
summarize(n=n()) %>%
mutate(total=sum(n), pct=n/total*100)
## # A tibble: 5 × 4
## cut n total pct
## <ord> <int> <int> <dbl>
## 1 Fair 1610 53940 2.98
## 2 Good 4906 53940 9.10
## 3 Very Good 12082 53940 22.4
## 4 Premium 13791 53940 25.6
## 5 Ideal 21551 53940 40.0
quantile(diamonds$price)
## 0% 25% 50% 75% 100%
## 326.00 950.00 2401.00 5324.25 18823.00
diamonds1<-diamonds %>%
mutate(price_class=ifelse(price>=5324.25,"best",
ifelse(price<=2401,"good",
ifelse(price>=950,"normal","bad"))))
table(diamonds1$price_class)
##
## best good normal
## 13485 26985 13470
diamonds %>% group_by(cut) %>%
summarise(AvgPrice=mean(price)) %>%
arrange(desc(AvgPrice))
## # A tibble: 5 × 2
## cut AvgPrice
## <ord> <dbl>
## 1 Premium 4584.
## 2 Fair 4359.
## 3 Very Good 3982.
## 4 Good 3929.
## 5 Ideal 3458.
data("airquality")
summary(airquality)
## Ozone Solar.R Wind Temp
## Min. : 1.00 Min. : 7.0 Min. : 1.700 Min. :56.00
## 1st Qu.: 18.00 1st Qu.:115.8 1st Qu.: 7.400 1st Qu.:72.00
## Median : 31.50 Median :205.0 Median : 9.700 Median :79.00
## Mean : 42.13 Mean :185.9 Mean : 9.958 Mean :77.88
## 3rd Qu.: 63.25 3rd Qu.:258.8 3rd Qu.:11.500 3rd Qu.:85.00
## Max. :168.00 Max. :334.0 Max. :20.700 Max. :97.00
## NA's :37 NA's :7
## Month Day
## Min. :5.000 Min. : 1.0
## 1st Qu.:6.000 1st Qu.: 8.0
## Median :7.000 Median :16.0
## Mean :6.993 Mean :15.8
## 3rd Qu.:8.000 3rd Qu.:23.0
## Max. :9.000 Max. :31.0
##
View(airquality)
names(airquality)<-tolower(names(airquality))
is.na(airquality$ozone)
## [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] TRUE TRUE TRUE FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE
## [37] TRUE FALSE TRUE FALSE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
## [49] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [73] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
table(is.na(airquality))
##
## FALSE TRUE
## 874 44
table(is.na(airquality$ozone))
##
## FALSE TRUE
## 116 37
summary(is.na(airquality))
## ozone solar.r wind temp
## Mode :logical Mode :logical Mode :logical Mode :logical
## FALSE:116 FALSE:146 FALSE:153 FALSE:153
## TRUE :37 TRUE :7
## month day
## Mode :logical Mode :logical
## FALSE:153 FALSE:153
##
sum(airquality$ozone)
## [1] NA
mean(airquality$ozone)
## [1] NA
sum(airquality$ozone,na.rm=TRUE)
## [1] 4887
mean(airquality$ozone,na.rm=TRUE)
## [1] 42.12931
airquality<-na.omit(airquality)
table(is.na(airquality))
##
## FALSE
## 666
airquality %>% filter(!is.na(ozone)) %>% head(3)
## ozone solar.r wind temp month day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
airquality %>% filter(!is.na(ozone)&!is.na(solar.r)) %>% head(3)
## ozone solar.r wind temp month day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
mean <- mean(airquality$ozone,na.rm=TRUE)
airquality$ozone <- ifelse(is.na(airquality$ozone),mean,airquality$ozone)
table(airquality$ozone)
##
## 1 4 6 7 8 9 10 11 12 13 14 16 18 19 20 21 22 23 24 27
## 1 1 1 2 1 3 1 3 2 4 4 4 4 1 4 4 1 6 2 1
## 28 29 30 31 32 34 35 36 37 39 40 41 44 45 46 47 48 49 50 52
## 2 1 2 1 3 1 1 2 2 2 1 1 3 2 1 1 1 1 1 1
## 59 61 63 64 65 71 73 76 77 78 79 80 82 84 85 89 91 96 97 108
## 2 1 1 2 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1
## 110 115 118 122 135 168
## 1 1 1 1 1 1
ott7 <- data.frame(gender=c("1","1","2","2","2","3"),
income=c(200,250,200,300,200,150))
ott7
## gender income
## 1 1 200
## 2 1 250
## 3 2 200
## 4 2 300
## 5 2 200
## 6 3 150
boxplot(iris$Sepal.Width)$stats
## [,1]
## [1,] 2.2
## [2,] 2.8
## [3,] 3.0
## [4,] 3.3
## [5,] 4.0
iris %>% filter(Sepal.Width>4.0|Sepal.Width<2.2)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.7 4.4 1.5 0.4 setosa
## 2 5.2 4.1 1.5 0.1 setosa
## 3 5.5 4.2 1.4 0.2 setosa
## 4 5.0 2.0 3.5 1.0 versicolor
iris$Sepal.Width <- ifelse(iris$Sepal.Width>4.0 | iris$Sepal.Width<2.2,
NA, iris$Sepal.Width)
table(is.na(iris$Sepal.Width))
##
## FALSE TRUE
## 146 4
iris %>% filter(!is.na(Sepal.Width)) %>% dim
## [1] 146 5
library(readxl)
airseoul<-read_excel("period1.xlsx")
names(airseoul)
## [1] "날짜" "측정소명"
## [3] "미세먼지 PM10\r\n(㎍/m3)" "초미세먼지\r\nPM2.5 (㎍/m3)"
## [5] "오존\r\nO3 (ppm)" "이산화질소\r\nNO2 (ppm)"
## [7] "일산화탄소\r\nCO (ppm)" "아황산가스\r\nSO2(ppm)"
airseoul1 <- airseoul %>% rename(date="날짜", region="측정소명",
pm10="미세먼지 PM10\r\n(㎍/m3)",
pm2.5="초미세먼지\r\nPM2.5 (㎍/m3)") %>%
select(date,region,pm10,pm2.5)
table(airseoul1$date)
##
## 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 26 26 26 26 26 26 26
## 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 26 26 26 26 26 26 26
## 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 26 26 26 26 26 26 26
## 2022-02-22 2022-02-23 2022-02-24 2022-02-25 2022-02-26 2022-02-27 2022-02-28
## 26 26 26 26 26 26 26
## 2022-03-01 2022-03-02 2022-03-03 2022-03-04 2022-03-05 2022-03-06 2022-03-07
## 26 26 26 26 26 26 26
## 2022-03-08 2022-03-09 2022-03-10 2022-03-11 2022-03-12 2022-03-13 2022-03-14
## 26 26 26 26 26 26 26
## 2022-03-15 2022-03-16 2022-03-17 2022-03-18 2022-03-19 2022-03-20 2022-03-21
## 26 26 26 26 26 26 26
## 2022-03-22 2022-03-23 2022-03-24 2022-03-25 2022-03-26 2022-03-27 2022-03-28
## 26 26 26 26 26 26 26
## 2022-03-29 2022-03-30 2022-03-31 전체
## 26 26 26 1
table(airseoul1$region)
##
## 강남구 강동구 강북구 강서구 관악구 광진구 구로구 금천구
## 59 59 59 59 59 59 59 59
## 노원구 도봉구 동대문구 동작구 마포구 서대문구 서초구 성동구
## 59 59 59 59 59 59 59 59
## 성북구 송파구 양천구 영등포구 용산구 은평구 종로구 중구
## 59 59 59 59 59 59 59 59
## 중랑구 평균
## 59 60
airseoul1<-airseoul1 %>% filter(date!="전체"®ion!="평균")
table(airseoul1$date)
##
## 2022-02-01 2022-02-02 2022-02-03 2022-02-04 2022-02-05 2022-02-06 2022-02-07
## 25 25 25 25 25 25 25
## 2022-02-08 2022-02-09 2022-02-10 2022-02-11 2022-02-12 2022-02-13 2022-02-14
## 25 25 25 25 25 25 25
## 2022-02-15 2022-02-16 2022-02-17 2022-02-18 2022-02-19 2022-02-20 2022-02-21
## 25 25 25 25 25 25 25
## 2022-02-22 2022-02-23 2022-02-24 2022-02-25 2022-02-26 2022-02-27 2022-02-28
## 25 25 25 25 25 25 25
## 2022-03-01 2022-03-02 2022-03-03 2022-03-04 2022-03-05 2022-03-06 2022-03-07
## 25 25 25 25 25 25 25
## 2022-03-08 2022-03-09 2022-03-10 2022-03-11 2022-03-12 2022-03-13 2022-03-14
## 25 25 25 25 25 25 25
## 2022-03-15 2022-03-16 2022-03-17 2022-03-18 2022-03-19 2022-03-20 2022-03-21
## 25 25 25 25 25 25 25
## 2022-03-22 2022-03-23 2022-03-24 2022-03-25 2022-03-26 2022-03-27 2022-03-28
## 25 25 25 25 25 25 25
## 2022-03-29 2022-03-30 2022-03-31
## 25 25 25
table(airseoul1$region)
##
## 강남구 강동구 강북구 강서구 관악구 광진구 구로구 금천구
## 59 59 59 59 59 59 59 59
## 노원구 도봉구 동대문구 동작구 마포구 서대문구 서초구 성동구
## 59 59 59 59 59 59 59 59
## 성북구 송파구 양천구 영등포구 용산구 은평구 종로구 중구
## 59 59 59 59 59 59 59 59
## 중랑구
## 59
summary(airseoul1$pm10)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.00 27.00 36.00 40.54 50.00 112.00 7
summary(airseoul1$pm2.5)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 13.00 19.00 23.28 32.00 92.00 3
airseoul1<-airseoul1%>%filter(!is.na(pm10)&!is.na(pm2.5))
airseoul1 %>% filter(pm10==max(pm10)) %>% select(date,region,pm10)
## # A tibble: 1 × 3
## date region pm10
## <chr> <chr> <dbl>
## 1 2022-03-05 구로구 112
airseoul1 %>% filter(pm10==min(pm10)) %>% select(date,region,pm10)
## # A tibble: 3 × 3
## date region pm10
## <chr> <chr> <dbl>
## 1 2022-03-19 은평구 3
## 2 2022-03-18 도봉구 3
## 3 2022-03-18 은평구 3
airseoul1 %>% group_by(region) %>% summarise(m=mean(pm10)) %>%
arrange(desc(m)) %>% head(5)
## # A tibble: 5 × 2
## region m
## <chr> <dbl>
## 1 양천구 44.4
## 2 강북구 44.2
## 3 강서구 43.8
## 4 노원구 43.7
## 5 강동구 43.6
airseoul1 %>% mutate(pm_grade=ifelse(pm10<=30,"good",
ifelse(pm10<=81,"nomal",
ifelse(pm10<150,"bad","worse")))) %>%
group_by(pm_grade) %>% summarize(n=n()) %>%
mutate(total=sum(n),pct=n/total*100)
## # A tibble: 3 × 4
## pm_grade n total pct
## <chr> <int> <int> <dbl>
## 1 bad 71 1467 4.84
## 2 good 538 1467 36.7
## 3 nomal 858 1467 58.5
airseoul1 %>% filter(pm2.5==min(pm2.5)) %>% arrange(desc(pm10))
## # A tibble: 6 × 4
## date region pm10 pm2.5
## <chr> <chr> <dbl> <dbl>
## 1 2022-03-18 성동구 7 1
## 2 2022-03-18 구로구 6 1
## 3 2022-03-18 서초구 6 1
## 4 2022-03-19 구로구 5 1
## 5 2022-03-18 서대문구 5 1
## 6 2022-03-19 종로구 4 1