Comment fusionner une liste de fichiers qui se trouvent dans l'environnement après un fichier de mappage
Nov 19 2020
J'ai une liste de fichiers dans mon environnement R. Je souhaite fusionner certains d'entre eux en utilisant un fichier de mappage.
Le fichier de mappage est nommé map_rule1 et ressemble à ce qui suit.
map_rule1
# A tibble: 8 x 4
EDC_file_name Tab DatasetName GroupVar1
<chr> <chr> <chr> <chr>
1 e1 Demographics Demographics Merged Subject
2 e2 Demographics NA NA
3 e3 PatientRegister Patient Register Subject
4 e4 PatientRegister NA NA
5 e5 PatientRegister NA NA
6 e6 PatientRegister NA NA
7 e7 PatientConsent Patient Consent NA
8 e8 PatientConsent NA NA
Les éléments répertoriés dans Data col sont les fichiers qui se trouvent dans mon environnement r actuel. Je souhaite fusionner ceux qui sont classés comme le même domaine dans un fichier par la variable répertoriée dans Group_V1 et le nouveau nom de données répertorié dans New_data_Name. J'ai plus de 100 fichiers qui doivent être fusionnés. c'est pourquoi je souhaite créer une méthode de bouclage ou tout autre moyen de fusionner automatiquement ces fichiers.
Les exemples de données et Map_Rule peuvent être construits à l'aide de codes:
e1<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), SEX = structure(c(2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Female", "Male"), class = "factor")), class = "data.frame", row.names = c(NA,
-27L))
e2<-
structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), RACE = structure(c(2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L), .Label = c("Black (including African, Caribbean descent)",
"Caucasian"), class = "factor")), class = "data.frame", row.names = c(NA,
-27L))
e3<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), ETHNIC_STD = c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L)), class = "data.frame", row.names = c(NA,
-27L))
e4<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), subjectId = c(168L,
171L, 174L, 175L, 196L, 199L, 207L, 208L, 213L, 209L, 210L, 212L,
283L, 325L, 329L, 527L, 315L, 316L, 320L, 334L, 339L, 582L, 319L,
523L, 526L, 601L, 532L)), class = "data.frame", row.names = c(NA,
-27L))
e5<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), siteid = c(9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L)), class = "data.frame", row.names = c(NA,
-27L))
e5<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), siteid = c(9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L)), class = "data.frame", row.names = c(NA,
-27L))
e7<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0007", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), Location = structure(c(2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Urban", "Ural"), class = "factor")), class = "data.frame", row.names = c(NA,
-27L))
e8<-structure(list(Subject = structure(c(1L, 2L, 3L, 5L, 6L, 4L,
7L, 8L, 9L, 21L, 22L, 23L, 24L, 25L, 27L, 26L, 10L, 11L, 12L,
13L, 14L, 15L, 17L, 19L, 18L, 20L, 16L), .Label = c("300-0001",
"300-0002", "300-0003", "300-0004", "300-0005", "300-0006", "300-0007",
"300-0008", "300-0009", "301-0001", "301-0002", "301-0003", "301-0004",
"301-0005", "301-0006", "302-0001", "303-0001", "303-0002", "303-0003",
"303-0004", "304-0001", "304-0002", "304-0003", "304-0004", "304-0005",
"304-0006", "304-0007"), class = "factor"), SEX = structure(c(2L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Female", "Male"), class = "factor")), class = "data.frame", row.names = c(NA,
-27L))
map_rule1<-structure(list(EDC_file_name = c("e1", "e2", "e3",
"e4", "e5", "e6", "e7", "e8"), Tab = c("Demographics",
"Demographics", "PatientRegister", "PatientRegister", "PatientRegister",
"PatientRegister", "PatientConsent", "PatientConsent"), DatasetName = c("Demographics Merged",
NA, "Patient Register", NA, NA, NA, "Patient Consent", NA), GroupVar1 = c( "Subject",
NA, "Subject", NA, NA, NA,
NA, NA)), row.names = c(NA, -8L), class = c("tbl_df",
"tbl", "data.frame"))
Des conseils sur la façon de procéder? Merci
Réponses
1 IRTFM Nov 19 2020 at 20:04
Voici ce que je pense pourrait fonctionner. Testé sur une version assainie de l' map_rule1ensemble de règles: il y avait deux sources d'erreur sur lesquelles vous devrez probablement piéger ou pré-désinfecter: 1) e6était indéfini, et 2) j'ai décidé de trouver comment gérer la fusion manquante - les bycolonnes étaient un niveau supplémentaire de complexité que je ne ressentais pas à la hauteur:
temp <- lapply( split(map_rule1, map_rule1$Tab) , # breaks into groups by Domain function( d){ assign( d$DatasetName[1],
# names= first items in col
# I don't generally use assign but seems reasonable here
Reduce( function(x,y){ merge(x,y, by=d$GroupVar1[1])}, lapply(d$EDC_file_name, get) ) ,
#use first item as named by-argument
envir=globalenv() )}
# named objects need to appear outside this function
)
#need to run this before calculating `temp`
map_rule1 <-
structure(list(EDC_file_name = c("e1", "e2", "e3", "e4", "e5"
), Tab = c("Demographics", "Demographics", "PatientRegister",
"PatientRegister", "PatientRegister"), DatasetName = c("Demographics Merged",
NA, "Patient Register", NA, NA), GroupVar1 = c("Subject", NA,
"Subject", NA, NA)), row.names = c(NA, -5L), class = c("tbl_df",
"tbl", "data.frame"))
-----------résultats-------
# First what was in temp
str(temp)
List of 2
$ Demographics :'data.frame': 27 obs. of 3 variables: ..$ Subject: Factor w/ 27 levels "300-0001","300-0002",..: 1 2 3 4 5 6 7 8 9 10 ...
..$ SEX : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 1 2 2 2 2 ... ..$ RACE : Factor w/ 2 levels "Black (including African, Caribbean descent)",..: 2 2 2 2 2 1 2 2 2 2 ...
$ PatientRegister:'data.frame': 27 obs. of 4 variables: ..$ Subject : Factor w/ 27 levels "300-0001","300-0002",..: 1 2 3 4 5 6 7 8 9 10 ...
..$ ETHNIC_STD: int [1:27] 2 2 2 2 2 2 2 2 2 2 ... ..$ subjectId : int [1:27] 168 171 174 199 175 196 207 208 213 315 ...
..$ siteid : int [1:27] 9 9 9 9 9 9 9 9 9 15 ...
# Second the results in the global environment
# with the weird un-Rish names containing spaces
`Demographics Merged`
Subject SEX RACE
1 300-0001 Male Caucasian
2 300-0002 Female Caucasian
3 300-0003 Male Caucasian
4 300-0004 Female Caucasian
5 300-0005 Male Caucasian
6 300-0006 Female Black (including African, Caribbean descent)
7 300-0007 Male Caucasian
8 300-0008 Male Caucasian
9 300-0009 Male Caucasian
10 301-0001 Male Caucasian
11 301-0002 Female Caucasian
12 301-0003 Male Caucasian
13 301-0004 Male Caucasian
14 301-0005 Male Black (including African, Caribbean descent)
15 301-0006 Male Caucasian
16 302-0001 Male Caucasian
17 303-0001 Male Caucasian
18 303-0002 Male Black (including African, Caribbean descent)
19 303-0003 Male Caucasian
20 303-0004 Male Caucasian
21 304-0001 Male Caucasian
22 304-0002 Male Caucasian
23 304-0003 Female Black (including African, Caribbean descent)
24 304-0004 Male Black (including African, Caribbean descent)
25 304-0005 Male Black (including African, Caribbean descent)
26 304-0006 Female Caucasian
27 304-0007 Male Caucasian
Vous pouvez obtenir des résultats nommés unRish dans votre espace de travail simplement en exécutant le lapplycode sans affecter ses résultats à temp.