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INTRO
reshape를 이용한 데이터 재정렬
Rstudio Console> install.packages("reshape")
#참고 : reshape2는 reshape의 확장판 개념, 설치 오래걸림
Rstudio Console> install.packages("reshape2")
reshape
reshape 패키지는 melt, cast를 사용하여 데이터를 재구성하거나 밀집화된 데이터를 유연하게 생성해줌
melt 녹이다, cast 전 데이터 형태 추출
syntax : var = melt(data, id=)
cast 굳히다, 원하는 형태로 데이터를 변형
syntax : var = melt(data, id=)
a : 엑셀 피벗팅과 유사하게 자료 변환, 피벗테이블과는 다르게 구별된 순서가 부여됨
b : y축(행, row) 대상을 첫 칼럼에 놓고 x축(열, column) 대상별로 평균(mean)값을 table 형태로 변환
c : "|" 기호를 이용하여 산출물을 분리해서 표시
d : "margins=" 옵션을 통해 행과 열에 대한 소계(grand total)을 산출하는 기능
e : 'subset=" 옵션을 통해 특정 변수를 지정하여 나열
f : "range" 함수를 통해 min(_X1), Max(_X2)를 구하여 table 형태로 반환
test_reshape.R
rm(list=ls())
setwd = "~/Rcoding"
library(reshape)
## data load
data(airquality)
## data top 10
head(airquality,10)
names(airquality)
## convert col-name to lower case
names(airquality) = tolower(names(airquality))
names(airquality)
#### melt : select "id=month,day" and remove missing datas
agm = melt(airquality, id=c('month','day'), na.rm=TRUE)
print(agm)
#### cast
## sorted values by each id(distinct count)
## cast(data, y-dimension ~ x-dimension ~ target-id)
a = cast(agm, day ~ month ~ variable)
print(a)
## selected statistic values by each id
## cast(data, y-dimension ~ x-dimension, statistics)
b = cast(agm, month ~ variable, mean)
print(b)
## selected statistic values separated id
## cast(data, y-dimension ~ . | separate ids, statistics)
c = cast(agm, month ~. | variable, mean)
print(c)
## statistics grand total value
## cast(data, y-dimension, statistics, margins=)
d = cast(agm, month ~ variable, mean, margins=c('grand_row', 'grand_col'))
print(d)
## detail target statistics values
## cast(data, y-dimension ~ y-dimension, statistics, subset=id=="target")
e = cast(agm, day ~ month, mean, subset=variable=='ozone')
print(e)
## statistics values of min(_X1), Max(_X2) for target id
## cast(data, y-dimension ~ y-dimension, statistics, range)
f = cast(agm, month ~ variable, range)
print(f)출력결과 : reshape2를 사용해도 결과는 동일함
> source("~/Rcoding/test_reshape.R", echo=TRUE)
> rm(list=ls())
> setwd = "~/Rcoding"
> library(reshape)
> ## data load
> data(airquality)
> ## data top 10
> head(airquality,10)
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
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6
7 23 299 8.6 65 5 7
8 19 99 13.8 59 5 8
9 8 19 20.1 61 5 9
10 NA 194 8.6 69 5 10
> names(airquality)
[1] "Ozone" "Solar.R" "Wind" "Temp" "Month" "Day"
> ## convert col-name to lower case
> names(airquality) = tolower(names(airquality))
> names(airquality)
[1] "ozone" "solar.r" "wind" "temp" "month" "day"
> #### melt : select "id=month,day" and remove missing datas
> agm = melt(airquality, id=c('month','day'), na.rm=TRUE)
> print(agm)
month day variable value
1 5 1 ozone 41
2 5 2 ozone 36
3 5 3 ozone 12
4 5 4 ozone 18
6 5 6 ozone 28
7 5 7 ozone 23
....
151 9 28 ozone 14
152 9 29 ozone 18
153 9 30 ozone 20
154 5 1 solar.r 190
155 5 2 solar.r 118
156 5 3 solar.r 149
157 5 4 solar.r 313
160 5 7 solar.r 299
161 5 8 solar.r 99
162 5 9 solar.r 19
163 5 10 solar.r 194
....
....
292 9 16 solar.r 237
293 9 17 solar.r 224
294 9 18 solar.r 27
[ reached 'max' / getOption("max.print") -- omitted 318 rows ]
> #### cast
> ## sorted values by each id(distinct count)
> ## cast(data, y-dimension ~ x-dimension ~ target-id)
> a = cast(agm, day ~ month ~ variabl .... [TRUNCATED]
> print(a)
, , variable = ozone
month
day 5 6 7 8 9
1 41 NA 135 39 96
2 36 NA 49 9 78
3 12 NA 32 16 73
4 18 NA NA 78 91
5 NA NA 64 35 47
6 28 NA 40 66 32
7 23 29 77 122 20
8 19 NA 97 89 23
9 8 71 97 110 21
10 NA 39 85 NA 24
11 7 NA NA NA 44
12 16 NA 10 44 21
13 11 23 27 28 28
14 14 NA NA 65 9
15 18 NA 7 NA 13
16 14 21 48 22 46
17 34 37 35 59 18
18 6 20 61 23 13
19 30 12 79 31 24
20 11 13 63 44 16
21 1 NA 16 21 13
22 11 NA NA 9 23
23 4 NA NA NA 36
24 32 NA 80 45 7
25 NA NA 108 168 14
26 NA NA 20 73 30
27 NA NA 52 NA NA
28 23 NA 82 76 14
29 45 NA 50 118 18
30 115 NA 64 84 20
31 37 NA 59 85 NA
, , variable = solar.r
month
day 5 6 7 8 9
1 190 286 269 83 167
2 118 287 248 24 197
3 149 242 236 77 183
4 313 186 101 NA 189
5 NA 220 175 NA 95
6 NA 264 314 NA 92
7 299 127 276 255 252
8 99 273 267 229 220
9 19 291 272 207 230
10 194 323 175 222 259
11 NA 259 139 137 236
12 256 250 264 192 259
13 290 148 175 273 238
14 274 332 291 157 24
15 65 322 48 64 112
16 334 191 260 71 237
17 307 284 274 51 224
18 78 37 285 115 27
19 322 120 187 244 238
20 44 137 220 190 201
21 8 150 7 259 238
22 320 59 258 36 14
23 25 91 295 255 139
24 92 250 294 212 49
25 66 135 223 238 20
26 266 127 81 215 193
27 NA 47 82 153 145
28 13 98 213 203 191
29 252 31 275 225 131
30 223 138 253 237 223
31 279 NA 254 188 NA
, , variable = wind
month
day 5 6 7 8 9
1 7.4 8.6 4.1 6.9 6.9
2 8.0 9.7 9.2 13.8 5.1
3 12.6 16.1 9.2 7.4 2.8
4 11.5 9.2 10.9 6.9 4.6
5 14.3 8.6 4.6 7.4 7.4
6 14.9 14.3 10.9 4.6 15.5
7 8.6 9.7 5.1 4.0 10.9
8 13.8 6.9 6.3 10.3 10.3
9 20.1 13.8 5.7 8.0 10.9
10 8.6 11.5 7.4 8.6 9.7
11 6.9 10.9 8.6 11.5 14.9
12 9.7 9.2 14.3 11.5 15.5
13 9.2 8.0 14.9 11.5 6.3
14 10.9 13.8 14.9 9.7 10.9
15 13.2 11.5 14.3 11.5 11.5
16 11.5 14.9 6.9 10.3 6.9
17 12.0 20.7 10.3 6.3 13.8
18 18.4 9.2 6.3 7.4 10.3
19 11.5 11.5 5.1 10.9 10.3
20 9.7 10.3 11.5 10.3 8.0
21 9.7 6.3 6.9 15.5 12.6
22 16.6 1.7 9.7 14.3 9.2
23 9.7 4.6 11.5 12.6 10.3
24 12.0 6.3 8.6 9.7 10.3
25 16.6 8.0 8.0 3.4 16.6
26 14.9 8.0 8.6 8.0 6.9
27 8.0 10.3 12.0 5.7 13.2
28 12.0 11.5 7.4 9.7 14.3
29 14.9 14.9 7.4 2.3 8.0
30 5.7 8.0 7.4 6.3 11.5
31 7.4 NA 9.2 6.3 NA
, , variable = temp
month
day 5 6 7 8 9
1 67 78 84 81 91
2 72 74 85 81 92
3 74 67 81 82 93
4 62 84 84 86 93
5 56 85 83 85 87
6 66 79 83 87 84
7 65 82 88 89 80
8 59 87 92 90 78
9 61 90 92 90 75
10 69 87 89 92 73
11 74 93 82 86 81
12 69 92 73 86 76
13 66 82 81 82 77
14 68 80 91 80 71
15 58 79 80 79 71
16 64 77 81 77 78
17 66 72 82 79 67
18 57 65 84 76 76
19 68 73 87 78 68
20 62 76 85 78 82
21 59 77 74 77 64
22 73 76 81 72 71
23 61 76 82 75 81
24 61 76 86 79 69
25 57 75 85 81 63
26 58 78 82 86 70
27 57 73 86 88 77
28 67 80 88 97 75
29 81 77 86 94 76
30 79 83 83 96 68
31 76 NA 81 94 NA
> ## selected statistic values by each id
> ## cast(data, y-dimension ~ x-dimension, statistics)
> b = cast(agm, month ~ variable, mean)
> print(b)
month ozone solar.r wind temp
1 5 23.61538 181.2963 11.622581 65.54839
2 6 29.44444 190.1667 10.266667 79.10000
3 7 59.11538 216.4839 8.941935 83.90323
4 8 59.96154 171.8571 8.793548 83.96774
5 9 31.44828 167.4333 10.180000 76.90000
> ## selected statistic values separated id
> ## cast(data, y-dimension ~ . | separate ids, statistics)
> c = cast(agm, month ~. | variable, mean)
> print(c)
$ozone
month (all)
1 5 23.61538
2 6 29.44444
3 7 59.11538
4 8 59.96154
5 9 31.44828
$solar.r
month (all)
1 5 181.2963
2 6 190.1667
3 7 216.4839
4 8 171.8571
5 9 167.4333
$wind
month (all)
1 5 11.622581
2 6 10.266667
3 7 8.941935
4 8 8.793548
5 9 10.180000
$temp
month (all)
1 5 65.54839
2 6 79.10000
3 7 83.90323
4 8 83.96774
5 9 76.90000
> ## statistics grand total value
> ## cast(data, y-dimension, statistics, margins=)
> d = cast(agm, month ~ variable, mean, margins=c('grand_row', ' .... [TRUNCATED]
> print(d)
month ozone solar.r wind temp (all)
1 5 23.61538 181.2963 11.622581 65.54839 68.70696
2 6 29.44444 190.1667 10.266667 79.10000 87.38384
3 7 59.11538 216.4839 8.941935 83.90323 93.49748
4 8 59.96154 171.8571 8.793548 83.96774 79.71207
5 9 31.44828 167.4333 10.180000 76.90000 71.82689
6 (all) 42.12931 185.9315 9.957516 77.88235 80.05722
> ## detail target statistics values
> ## cast(data, y-dimension ~ y-dimension, statistics, subset=id=="target")
> e = cast(agm, day ~ month, mean, su .... [TRUNCATED]
> print(e)
day 5 6 7 8 9
1 1 41 NaN 135 39 96
2 2 36 NaN 49 9 78
3 3 12 NaN 32 16 73
4 4 18 NaN NaN 78 91
5 5 NaN NaN 64 35 47
6 6 28 NaN 40 66 32
7 7 23 29 77 122 20
8 8 19 NaN 97 89 23
9 9 8 71 97 110 21
10 10 NaN 39 85 NaN 24
11 11 7 NaN NaN NaN 44
12 12 16 NaN 10 44 21
13 13 11 23 27 28 28
14 14 14 NaN NaN 65 9
15 15 18 NaN 7 NaN 13
16 16 14 21 48 22 46
17 17 34 37 35 59 18
18 18 6 20 61 23 13
19 19 30 12 79 31 24
20 20 11 13 63 44 16
21 21 1 NaN 16 21 13
22 22 11 NaN NaN 9 23
23 23 4 NaN NaN NaN 36
24 24 32 NaN 80 45 7
25 25 NaN NaN 108 168 14
26 26 NaN NaN 20 73 30
27 27 NaN NaN 52 NaN NaN
28 28 23 NaN 82 76 14
29 29 45 NaN 50 118 18
30 30 115 NaN 64 84 20
31 31 37 NaN 59 85 NaN
> ## statistics values of min(_X1), Max(_X2) for target id
> ## cast(data, y-dimension ~ y-dimension, statistics, range)
> f = cast(agm, month ~ varia .... [TRUNCATED]
> print(f)
month ozone_X1 ozone_X2 solar.r_X1 solar.r_X2 wind_X1 wind_X2 temp_X1 temp_X2
1 5 1 115 8 334 5.7 20.1 56 81
2 6 12 71 31 332 1.7 20.7 65 93
3 7 7 135 7 314 4.1 14.9 73 92
4 8 9 168 24 273 2.3 15.5 72 97
5 9 7 96 14 259 2.8 16.6 63 93
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