글쟁이 킬씨

4. 데이터 분리(sample, split, ifelse)

 
 

데이터 분리

 

 

 

데이터 분리 관련 함수

  • 데이터를 특정 구간별로 나누는 방법에 대하여 알아본다.
  • 특히 sample(), split(), ifelse() 함수가 이에 해당된다.
 

 

 

 

sample()

  • 데이터에서 임의로 샘플을 추출한다.
 
 
 
 
 
 
sample(1:10 , 5)
sample(1:10, 10)
​
 
 
 
  1. 9
  2. 8
  3. 3
  4. 2
  5. 7
 
  1. 1
  2. 7
  3. 4
  4. 9
  5. 6
  6. 5
  7. 2
  8. 3
  9. 8
  10. 10
 

 

 

 

split()

  • split()함수는 데이터를 조건에 따라 분리한다.
 
 
 
 
 
 
split(iris, iris$Species)    # 데이터를 Species에 따라 분리한다.
split(iris, 1:10)            # 데이터를 10개의 집합으로 분리한다.
​
 
 
 
$setosa
A data.frame: 50 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa
5.4 3.7 1.5 0.2 setosa
4.8 3.4 1.6 0.2 setosa
4.8 3.0 1.4 0.1 setosa
4.3 3.0 1.1 0.1 setosa
5.8 4.0 1.2 0.2 setosa
5.7 4.4 1.5 0.4 setosa
5.4 3.9 1.3 0.4 setosa
5.1 3.5 1.4 0.3 setosa
5.7 3.8 1.7 0.3 setosa
5.1 3.8 1.5 0.3 setosa
5.4 3.4 1.7 0.2 setosa
5.1 3.7 1.5 0.4 setosa
4.6 3.6 1.0 0.2 setosa
5.1 3.3 1.7 0.5 setosa
4.8 3.4 1.9 0.2 setosa
5.0 3.0 1.6 0.2 setosa
5.0 3.4 1.6 0.4 setosa
5.2 3.5 1.5 0.2 setosa
5.2 3.4 1.4 0.2 setosa
4.7 3.2 1.6 0.2 setosa
4.8 3.1 1.6 0.2 setosa
5.4 3.4 1.5 0.4 setosa
5.2 4.1 1.5 0.1 setosa
5.5 4.2 1.4 0.2 setosa
4.9 3.1 1.5 0.2 setosa
5.0 3.2 1.2 0.2 setosa
5.5 3.5 1.3 0.2 setosa
4.9 3.6 1.4 0.1 setosa
4.4 3.0 1.3 0.2 setosa
5.1 3.4 1.5 0.2 setosa
5.0 3.5 1.3 0.3 setosa
4.5 2.3 1.3 0.3 setosa
4.4 3.2 1.3 0.2 setosa
5.0 3.5 1.6 0.6 setosa
5.1 3.8 1.9 0.4 setosa
4.8 3.0 1.4 0.3 setosa
5.1 3.8 1.6 0.2 setosa
4.6 3.2 1.4 0.2 setosa
5.3 3.7 1.5 0.2 setosa
5.0 3.3 1.4 0.2 setosa
$versicolor
A data.frame: 50 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
$virginica
A data.frame: 50 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
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
 
$`1`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
11 5.4 3.7 1.5 0.2 setosa
21 5.4 3.4 1.7 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
51 7.0 3.2 4.7 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
71 5.9 3.2 4.8 1.8 versicolor
81 5.5 2.4 3.8 1.1 versicolor
91 5.5 2.6 4.4 1.2 versicolor
101 6.3 3.3 6.0 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
121 6.9 3.2 5.7 2.3 virginica
131 7.4 2.8 6.1 1.9 virginica
141 6.7 3.1 5.6 2.4 virginica
$`2`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
2 4.9 3.0 1.4 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
32 5.4 3.4 1.5 0.4 setosa
42 4.5 2.3 1.3 0.3 setosa
52 6.4 3.2 4.5 1.5 versicolor
62 5.9 3.0 4.2 1.5 versicolor
72 6.1 2.8 4.0 1.3 versicolor
82 5.5 2.4 3.7 1.0 versicolor
92 6.1 3.0 4.6 1.4 versicolor
102 5.8 2.7 5.1 1.9 virginica
112 6.4 2.7 5.3 1.9 virginica
122 5.6 2.8 4.9 2.0 virginica
132 7.9 3.8 6.4 2.0 virginica
142 6.9 3.1 5.1 2.3 virginica
$`3`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
3 4.7 3.2 1.3 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
23 4.6 3.6 1.0 0.2 setosa
33 5.2 4.1 1.5 0.1 setosa
43 4.4 3.2 1.3 0.2 setosa
53 6.9 3.1 4.9 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
73 6.3 2.5 4.9 1.5 versicolor
83 5.8 2.7 3.9 1.2 versicolor
93 5.8 2.6 4.0 1.2 versicolor
103 7.1 3.0 5.9 2.1 virginica
113 6.8 3.0 5.5 2.1 virginica
123 7.7 2.8 6.7 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
143 5.8 2.7 5.1 1.9 virginica
$`4`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
4 4.6 3.1 1.5 0.2 setosa
14 4.3 3.0 1.1 0.1 setosa
24 5.1 3.3 1.7 0.5 setosa
34 5.5 4.2 1.4 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
54 5.5 2.3 4.0 1.3 versicolor
64 6.1 2.9 4.7 1.4 versicolor
74 6.1 2.8 4.7 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
94 5.0 2.3 3.3 1.0 versicolor
104 6.3 2.9 5.6 1.8 virginica
114 5.7 2.5 5.0 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
134 6.3 2.8 5.1 1.5 virginica
144 6.8 3.2 5.9 2.3 virginica
$`5`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
5 5.0 3.6 1.4 0.2 setosa
15 5.8 4.0 1.2 0.2 setosa
25 4.8 3.4 1.9 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
45 5.1 3.8 1.9 0.4 setosa
55 6.5 2.8 4.6 1.5 versicolor
65 5.6 2.9 3.6 1.3 versicolor
75 6.4 2.9 4.3 1.3 versicolor
85 5.4 3.0 4.5 1.5 versicolor
95 5.6 2.7 4.2 1.3 versicolor
105 6.5 3.0 5.8 2.2 virginica
115 5.8 2.8 5.1 2.4 virginica
125 6.7 3.3 5.7 2.1 virginica
135 6.1 2.6 5.6 1.4 virginica
145 6.7 3.3 5.7 2.5 virginica
$`6`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
6 5.4 3.9 1.7 0.4 setosa
16 5.7 4.4 1.5 0.4 setosa
26 5.0 3.0 1.6 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
46 4.8 3.0 1.4 0.3 setosa
56 5.7 2.8 4.5 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
76 6.6 3.0 4.4 1.4 versicolor
86 6.0 3.4 4.5 1.6 versicolor
96 5.7 3.0 4.2 1.2 versicolor
106 7.6 3.0 6.6 2.1 virginica
116 6.4 3.2 5.3 2.3 virginica
126 7.2 3.2 6.0 1.8 virginica
136 7.7 3.0 6.1 2.3 virginica
146 6.7 3.0 5.2 2.3 virginica
$`7`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
7 4.6 3.4 1.4 0.3 setosa
17 5.4 3.9 1.3 0.4 setosa
27 5.0 3.4 1.6 0.4 setosa
37 5.5 3.5 1.3 0.2 setosa
47 5.1 3.8 1.6 0.2 setosa
57 6.3 3.3 4.7 1.6 versicolor
67 5.6 3.0 4.5 1.5 versicolor
77 6.8 2.8 4.8 1.4 versicolor
87 6.7 3.1 4.7 1.5 versicolor
97 5.7 2.9 4.2 1.3 versicolor
107 4.9 2.5 4.5 1.7 virginica
117 6.5 3.0 5.5 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
137 6.3 3.4 5.6 2.4 virginica
147 6.3 2.5 5.0 1.9 virginica
$`8`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
8 5.0 3.4 1.5 0.2 setosa
18 5.1 3.5 1.4 0.3 setosa
28 5.2 3.5 1.5 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
48 4.6 3.2 1.4 0.2 setosa
58 4.9 2.4 3.3 1.0 versicolor
68 5.8 2.7 4.1 1.0 versicolor
78 6.7 3.0 5.0 1.7 versicolor
88 6.3 2.3 4.4 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
108 7.3 2.9 6.3 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
128 6.1 3.0 4.9 1.8 virginica
138 6.4 3.1 5.5 1.8 virginica
148 6.5 3.0 5.2 2.0 virginica
$`9`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
9 4.4 2.9 1.4 0.2 setosa
19 5.7 3.8 1.7 0.3 setosa
29 5.2 3.4 1.4 0.2 setosa
39 4.4 3.0 1.3 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
59 6.6 2.9 4.6 1.3 versicolor
69 6.2 2.2 4.5 1.5 versicolor
79 6.0 2.9 4.5 1.5 versicolor
89 5.6 3.0 4.1 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
109 6.7 2.5 5.8 1.8 virginica
119 7.7 2.6 6.9 2.3 virginica
129 6.4 2.8 5.6 2.1 virginica
139 6.0 3.0 4.8 1.8 virginica
149 6.2 3.4 5.4 2.3 virginica
$`10`
A data.frame: 15 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
  <dbl> <dbl> <dbl> <dbl> <fct>
10 4.9 3.1 1.5 0.1 setosa
20 5.1 3.8 1.5 0.3 setosa
30 4.7 3.2 1.6 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
60 5.2 2.7 3.9 1.4 versicolor
70 5.6 2.5 3.9 1.1 versicolor
80 5.7 2.6 3.5 1.0 versicolor
90 5.5 2.5 4.0 1.3 versicolor
100 5.7 2.8 4.1 1.3 versicolor
110 7.2 3.6 6.1 2.5 virginica
120 6.0 2.2 5.0 1.5 virginica
130 7.2 3.0 5.8 1.6 virginica
140 6.9 3.1 5.4 2.1 virginica
150 5.9 3.0 5.1 1.8 virginica
 

 

 

 

ifelse()연습

ifelse()는 if와 else를 한 번에 처리한다. 

ifelse (조건, 참일 경우, 거짓일 경우)

 
 
 
 
 
 
a <-3 
ifelse(a==3, "3입니다.", "3이 아닙니다.")
​
 
 
 
'3입니다.'
 

데이터 준비

 
 
 
 
 
 
fileEncoding = "euc-kr"
setwd("C:/Users/KIIXXI/Documents/khu")
autoparts <- read.csv("autoparts.csv", header = TRUE)
autoparts1 <- autoparts[autoparts$prod_no=="90784-76001",c(2:11)]
autoparts2 <- autoparts1[autoparts1$c_thickness< 1000,]
head(autoparts2)
​
 
 
 
A data.frame: 6 × 10
fix_time a_speed b_speed separation s_separation rate_terms mpa load_time highpressure_time c_thickness
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <int> <dbl>
85.5 0.611 1.715 242.0 657.6 95 78.2 18.1 58 24.7
86.2 0.606 1.708 244.7 657.1 95 77.9 18.2 58 22.5
86.0 0.609 1.715 242.7 657.5 95 78.0 18.1 82 24.1
86.1 0.610 1.718 241.9 657.3 95 78.2 18.1 74 25.1
86.1 0.603 1.704 242.5 657.3 95 77.9 18.2 56 24.5
86.3 0.606 1.707 244.5 656.9 95 77.9 18.0 78 22.9
 

 

 

 

autoparts2 데이터에 y_faulty라는 컬럼을 생성하고, 오른쪽에서 추출한 자료를 저장한다.

 
 
 
 
 
 
autoparts2$y_faulty <- ifelse((autoparts2$c_thickness <20)|(autoparts2$c_thickness >32),1,0)
head(autoparts2)
​
 
 
 
A data.frame: 6 × 11
fix_time a_speed b_speed separation s_separation rate_terms mpa load_time highpressure_time c_thickness y_faulty
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <int> <dbl> <dbl>
85.5 0.611 1.715 242.0 657.6 95 78.2 18.1 58 24.7 0
86.2 0.606 1.708 244.7 657.1 95 77.9 18.2 58 22.5 0
86.0 0.609 1.715 242.7 657.5 95 78.0 18.1 82 24.1 0
86.1 0.610 1.718 241.9 657.3 95 78.2 18.1 74 25.1 0
86.1 0.603 1.704 242.5 657.3 95 77.9 18.2 56 24.5 0
86.3 0.606 1.707 244.5 656.9 95 77.9 18.0 78 22.9 0
 

 

 

 

20미만이면 1 , 그렇지 않으면 두번쨰 조건에서 32 미만이면 2 아니면 3

 
 
 
 
 
 
autoparts2$g_class <- as.factor(ifelse(autoparts2$c_thickness <20,1,
                                       ifelse(autoparts2$c_thickness<32,2,3)))
head(autoparts2)
​
 
 
 
A data.frame: 6 × 12
fix_time a_speed b_speed separation s_separation rate_terms mpa load_time highpressure_time c_thickness y_faulty g_class
<dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <int> <dbl> <dbl> <fct>
85.5 0.611 1.715 242.0 657.6 95 78.2 18.1 58 24.7 0 2
86.2 0.606 1.708 244.7 657.1 95 77.9 18.2 58 22.5 0 2
86.0 0.609 1.715 242.7 657.5 95 78.0 18.1 82 24.1 0 2
86.1 0.610 1.718 241.9 657.3 95 78.2 18.1 74 25.1 0 2
86.1 0.603 1.704 242.5 657.3 95 77.9 18.2 56 24.5 0 2
86.3 0.606 1.707 244.5 656.9 95 77.9 18.0 78 22.9 0 2
 

 

 

 

연습 2

타이타닉데이터에서 parch 는 parent, children을 의미,해당 탑승객의 부모의 수 혹은 자식의 수를 의미한다.

데이터 준비.

 
 
 
 
 
 
train <- read.csv("train.csv", header = TRUE)
library(dplyr)
 
 
 
 

 

 

 

15세 미만이면서 parch가 0보다 큰 경우 부모와 함께 탑승한 경우로 볼 수 있다.

15세 미만이면서 parch가 0보다 큰 경우에 해당하는 탑승객은 몇명인가.?

 
 
 
 
 
 
train$count <- ifelse(train$Age < 15 & train$Parch  > 0,1,0)
length(which(train$count == 1))
​
 
 
 
70
 

 

 

 

split()함수를 사용하여 15세 미만이면서 parch가 0 인 그룹의 생존률과

parch가 1 이상인 그룹의 생존률을 구하시오.

 
 
 
 
 
 
​
Suv <- split(train,train$count)
head(Suv)
 
 
 
$`0`
A data.frame: 801 × 13
  PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked count
  <int> <int> <int> <fct> <fct> <dbl> <int> <int> <fct> <dbl> <fct> <fct> <dbl>
1 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500   S 0
2 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C 0
3 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250   S 0
4 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S 0
5 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500   S 0
6 6 0 3 Moran, Mr. James male NA 0 0 330877 8.4583   Q 0
7 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S 0
9 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333   S 0
10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708   C 0
12 12 1 1 Bonnell, Miss. Elizabeth female 58 0 0 113783 26.5500 C103 S 0
13 13 0 3 Saundercock, Mr. William Henry male 20 0 0 A/5. 2151 8.0500   S 0
14 14 0 3 Andersson, Mr. Anders Johan male 39 1 5 347082 31.2750   S 0
15 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 0 350406 7.8542   S 0
16 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16.0000   S 0
18 18 1 2 Williams, Mr. Charles Eugene male NA 0 0 244373 13.0000   S 0
19 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 18.0000   S 0
20 20 1 3 Masselmani, Mrs. Fatima female NA 0 0 2649 7.2250   C 0
21 21 0 2 Fynney, Mr. Joseph J male 35 0 0 239865 26.0000   S 0
22 22 1 2 Beesley, Mr. Lawrence male 34 0 0 248698 13.0000 D56 S 0
23 23 1 3 McGowan, Miss. Anna "Annie" female 15 0 0 330923 8.0292   Q 0
24 24 1 1 Sloper, Mr. William Thompson male 28 0 0 113788 35.5000 A6 S 0
26 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1 5 347077 31.3875   S 0
27 27 0 3 Emir, Mr. Farred Chehab male NA 0 0 2631 7.2250   C 0
28 28 0 1 Fortune, Mr. Charles Alexander male 19 3 2 19950 263.0000 C23 C25 C27 S 0
29 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female NA 0 0 330959 7.8792   Q 0
30 30 0 3 Todoroff, Mr. Lalio male NA 0 0 349216 7.8958   S 0
31 31 0 1 Uruchurtu, Don. Manuel E male 40 0 0 PC 17601 27.7208   C 0
32 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NA 1 0 PC 17569 146.5208 B78 C 0
33 33 1 3 Glynn, Miss. Mary Agatha female NA 0 0 335677 7.7500   Q 0
34 34 0 2 Wheadon, Mr. Edward H male 66 0 0 C.A. 24579 10.5000   S 0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
859 859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583   C 0
860 860 0 3 Razi, Mr. Raihed male NA 0 0 2629 7.2292   C 0
861 861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083   S 0
862 862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5000   S 0
863 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S 0
865 865 0 2 Gill, Mr. John William male 24 0 0 233866 13.0000   S 0
866 866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13.0000   S 0
867 867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583   C 0
868 868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S 0
869 869 0 3 van Melkebeke, Mr. Philemon male NA 0 0 345777 9.5000   S 0
871 871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958   S 0
872 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S 0
873 873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5.0000 B51 B53 B55 S 0
874 874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9.0000   S 0
875 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24.0000   C 0
876 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.2250   C 0
877 877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458   S 0
878 878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958   S 0
879 879 0 3 Laleff, Mr. Kristo male NA 0 0 349217 7.8958   S 0
880 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C 0
881 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26.0000   S 0
882 882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958   S 0
883 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167   S 0
884 884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5000   S 0
885 885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.0500   S 0
886 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.1250   Q 0
887 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13.0000   S 0
888 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30.0000 B42 S 0
890 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30.0000 C148 C 0
891 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.7500   Q 0
$`1`
A data.frame: 70 × 13
  PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked count
  <int> <int> <int> <fct> <fct> <dbl> <int> <int> <fct> <dbl> <fct> <fct> <dbl>
8 8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 21.0750   S 1
11 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 16.7000 G6 S 1
17 17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 29.1250   Q 1
25 25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 21.0750   S 1
44 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 41.5792   C 1
51 51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 39.6875   S 1
59 59 1 2 West, Miss. Constance Mirium female 5.00 1 2 C.A. 34651 27.7500   S 1
60 60 0 3 Goodwin, Master. William Frederick male 11.00 5 2 CA 2144 46.9000   S 1
64 64 0 3 Skoog, Master. Harald male 4.00 3 2 347088 27.9000   S 1
79 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.0000   S 1
120 120 0 3 Andersson, Miss. Ellis Anna Maria female 2.00 4 2 347082 31.2750   S 1
148 148 0 3 Ford, Miss. Robina Maggie "Ruby" female 9.00 2 2 W./C. 6608 34.3750   S 1
165 165 0 3 Panula, Master. Eino Viljami male 1.00 4 1 3101295 39.6875   S 1
166 166 1 3 Goldsmith, Master. Frank John William "Frankie" male 9.00 0 2 363291 20.5250   S 1
172 172 0 3 Rice, Master. Arthur male 4.00 4 1 382652 29.1250   Q 1
173 173 1 3 Johnson, Miss. Eleanor Ileen female 1.00 1 1 347742 11.1333   S 1
183 183 0 3 Asplund, Master. Clarence Gustaf Hugo male 9.00 4 2 347077 31.3875   S 1
184 184 1 2 Becker, Master. Richard F male 1.00 2 1 230136 39.0000 F4 S 1
185 185 1 3 Kink-Heilmann, Miss. Luise Gretchen female 4.00 0 2 315153 22.0250   S 1
194 194 1 2 Navratil, Master. Michel M male 3.00 1 1 230080 26.0000 F2 S 1
206 206 0 3 Strom, Miss. Telma Matilda female 2.00 0 1 347054 10.4625 G6 S 1
234 234 1 3 Asplund, Miss. Lillian Gertrud female 5.00 4 2 347077 31.3875   S 1
238 238 1 2 Collyer, Miss. Marjorie "Lottie" female 8.00 0 2 C.A. 31921 26.2500   S 1
262 262 1 3 Asplund, Master. Edvin Rojj Felix male 3.00 4 2 347077 31.3875   S 1
279 279 0 3 Rice, Master. Eric male 7.00 4 1 382652 29.1250   Q 1
298 298 0 1 Allison, Miss. Helen Loraine female 2.00 1 2 113781 151.5500 C22 C26 S 1
306 306 1 1 Allison, Master. Hudson Trevor male 0.92 1 2 113781 151.5500 C22 C26 S 1
341 341 1 2 Navratil, Master. Edmond Roger male 2.00 1 1 230080 26.0000 F2 S 1
349 349 1 3 Coutts, Master. William Loch "William" male 3.00 1 1 C.A. 37671 15.9000   S 1
375 375 0 3 Palsson, Miss. Stina Viola female 3.00 3 1 349909 21.0750   S 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
481 481 0 3 Goodwin, Master. Harold Victor male 9.00 5 2 CA 2144 46.9000   S 1
490 490 1 3 Coutts, Master. Eden Leslie "Neville" male 9.00 1 1 C.A. 37671 15.9000   S 1
531 531 1 2 Quick, Miss. Phyllis May female 2.00 1 1 26360 26.0000   S 1
536 536 1 2 Hart, Miss. Eva Miriam female 7.00 0 2 F.C.C. 13529 26.2500   S 1
542 542 0 3 Andersson, Miss. Ingeborg Constanzia female 9.00 4 2 347082 31.2750   S 1
543 543 0 3 Andersson, Miss. Sigrid Elisabeth female 11.00 4 2 347082 31.2750   S 1
550 550 1 2 Davies, Master. John Morgan Jr male 8.00 1 1 C.A. 33112 36.7500   S 1
619 619 1 2 Becker, Miss. Marion Louise female 4.00 2 1 230136 39.0000 F4 S 1
635 635 0 3 Skoog, Miss. Mabel female 9.00 3 2 347088 27.9000   S 1
643 643 0 3 Skoog, Miss. Margit Elizabeth female 2.00 3 2 347088 27.9000   S 1
645 645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583   C 1
684 684 0 3 Goodwin, Mr. Charles Edward male 14.00 5 2 CA 2144 46.9000   S 1
687 687 0 3 Panula, Mr. Jaako Arnold male 14.00 4 1 3101295 39.6875   S 1
692 692 1 3 Karun, Miss. Manca female 4.00 0 1 349256 13.4167   C 1
721 721 1 2 Harper, Miss. Annie Jessie "Nina" female 6.00 0 1 248727 33.0000   S 1
751 751 1 2 Wells, Miss. Joan female 4.00 1 1 29103 23.0000   S 1
752 752 1 3 Moor, Master. Meier male 6.00 0 1 392096 12.4750 E121 S 1
756 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5000   S 1
788 788 0 3 Rice, Master. George Hugh male 8.00 4 1 382652 29.1250   Q 1
789 789 1 3 Dean, Master. Bertram Vere male 1.00 1 2 C.A. 2315 20.5750   S 1
803 803 1 1 Carter, Master. William Thornton II male 11.00 1 2 113760 120.0000 B96 B98 S 1
804 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167   C 1
814 814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6.00 4 2 347082 31.2750   S 1
820 820 0 3 Skoog, Master. Karl Thorsten male 10.00 3 2 347088 27.9000   S 1
825 825 0 3 Panula, Master. Urho Abraham male 2.00 4 1 3101295 39.6875   S 1
828 828 1 2 Mallet, Master. Andre male 1.00 0 2 S.C./PARIS 2079 37.0042   C 1
832 832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.7500   S 1
851 851 0 3 Andersson, Master. Sigvard Harald Elias male 4.00 4 2 347082 31.2750   S 1
853 853 0 3 Boulos, Miss. Nourelain female 9.00 1 1 2678 15.2458   C 1
870 870 1 3 Johnson, Master. Harold Theodor male 4.00 1 1 347742 11.1333   S 1
 
 
 
 
 
 
Suv1 <- Suv$`0`
Suv2 <- Suv$`1`
mean(Suv1$Survived)
mean(Suv2$Survived)
​
 
 
 
0.370786516853933
 
0.571428571428571

'Study of GifMan > R of GifMan' 카테고리의 다른 글

2. Tableau를 활용한 간단한 시각화  (0) 2019.08.07
1. Tableau 이해하기  (0) 2019.08.07
3. 데이터프레임 처리 2 (행제외, 열 제외, 행추가, 열추가, 병합, rename)  (0) 2019.08.06
2. 데이터 프레임 처리 1 (data.frame, stringAsFactors, dplyr패키지, which, subset)  (0) 2019.08.06
1. Feature Engineering(Scaling, Binnig, Creating Feature, as.Date, Creating Dummy)  (0) 2019.08.06

'Study of GifMan/R of GifMan' 관련 글

더 보기

2. Tableau를 활용한 간단한 시각화

  • 2019.08.07
더 읽기

1. Tableau 이해하기

  • 2019.08.07
더 읽기

3. 데이터프레임 처리 2 (행제외, 열 제외, 행추가, 열추가, 병합, rename)

  • 2019.08.06
더 읽기

2. 데이터 프레임 처리 1 (data.frame, stringAsFactors, dplyr패키지, which, subset)

  • 2019.08.06
더 읽기

댓글

Copyright © 글쟁이 킬씨 All Rights Reserved

Designed by JB FACTORY

티스토리툴바