So führen Sie eine Liste von Dateien zusammen, die sich nach einer Zuordnungsdatei in der Umgebung befinden
Nov 19 2020
Ich habe eine Liste der Dateien in meiner R-Umgebung. Ich möchte einige von ihnen mithilfe einer Zuordnungsdatei zusammenführen.
Die Zuordnungsdatei heißt map_rule1 und sieht folgendermaßen aus.
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
Die in der Spalte "Daten" aufgeführten Elemente sind die Dateien, die sich in meiner aktuellen Umgebung befinden. Ich möchte diejenigen, die als dieselbe Domäne kategorisiert sind, durch die in Group_V1 aufgeführte Variable und den in New_data_Name aufgelisteten neuen Datennamen in einer Datei zusammenführen. Ich habe mehr als 100 Dateien, die zusammengeführt werden müssen. Aus diesem Grund möchte ich eine Schleifenmethode oder eine andere Methode zum automatischen Zusammenführen dieser Dateien erstellen.
Beispieldaten und Map_Rule können mithilfe von Codes erstellt werden:
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"))
Irgendwelche Ratschläge, wie es geht? Vielen Dank
Antworten
1 IRTFM Nov 19 2020 at 20:04
Hier ist, was ich denke, könnte funktionieren. Getestet an einer bereinigten Version des map_rule1Regelsatzes: Es gab zwei Fehlerquellen, gegen die Sie wahrscheinlich einfangen oder vorbereinigen müssen: 1) e6war undefiniert und 2) ich habe beschlossen, herauszufinden, wie mit der fehlenden Zusammenführung umgegangen werden soll - bySpalten waren eine zusätzliche Komplexitätsebene, der ich mich nicht gewachsen fühlte:
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"))
-----------Ergebnisse-------
# 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
Sie können unRish-named-results in Ihrem Arbeitsbereich erhalten, indem Sie den lapplyCode ausführen, ohne seine Ergebnisse zuzuweisen temp.