Tidy data.frame mit wiederholten Spaltennamen

Question

ich ein Programm, das mir Daten in diesem Format ist ordentlich das Format in etwas, das zumindest mit leichter endlich aufgeräumt gibt

toy 
       file_path Condition Trial.Num A B C ID A B C ID A B C ID 
1  root/some.extension Baseline   1 2 3 5 car 2 1 7 bike 4 9 0 plane 
2 root/thing.extension Baseline   2 3 6 45 car 5 4 4 bike 9 5 4 plane 
3  root/else.extension Baseline   3 4 4 6 car 7 5 4 bike 68 7 56 plane 
4 root/uniquely.extension Treatment   1 5 3 7 car 1 7 37 bike 9 8 7 plane 
5 root/defined.extension Treatment   2 6 7 3 car 4 6 8 bike 9 0 8 plane 

Mein Ziel mit eindeutigen Spaltennamen umformen

tidy_toy 
       file_path Condition Trial.Num A B C ID 
1  root/some.extension Baseline   1 2 3 5 car 
2  root/thing.extension Baseline   2 3 6 45 car 
3  root/else.extension Baseline   3 4 4 6 car 
4 root/uniquely.extension Treatment   1 5 3 7 car 
5 root/defined.extension Treatment   2 6 7 3 car 
6  root/some.extension Baseline   1 2 1 7 bike 
7  root/thing.extension Baseline   2 5 4 4 bike 
8  root/else.extension Baseline   3 7 5 4 bike 
9 root/uniquely.extension Treatment   1 1 7 37 bike 
10 root/defined.extension Treatment   2 4 6 8 bike 
11  root/some.extension Baseline   1 4 9 0 plane 
12 root/thing.extension Baseline   2 9 5 4 plane 
13  root/else.extension Baseline   3 68 7 56 plane 
14 root/uniquely.extension Treatment   1 9 8 7 plane 
15 root/defined.extension Treatment   2 9 0 8 plane 

Wenn ich melt von toy versuche es, weil nur die erste ID nicht funktioniert Spalte für id.vars (daher wird alles wie Autos bekommen getaggt) gewöhnen. Identische Variablen werden gelöscht.

Hier ist der dput beiden Tabellen

structure(list(file_path = structure(c(3L, 4L, 2L, 5L, 1L), .Label = c("root/defined.extension", 
    "root/else.extension", "root/some.extension", "root/thing.extension", 
    "root/uniquely.extension"), class = "factor"), Condition = structure(c(1L, 
    1L, 1L, 2L, 2L), .Label = c("Baseline", "Treatment"), class = "factor"), 
     Trial.Num = c(1L, 2L, 3L, 1L, 2L), A = 2:6, B = c(3L, 6L, 
     4L, 3L, 7L), C = c(5L, 45L, 6L, 7L, 3L), ID = structure(c(1L, 
     1L, 1L, 1L, 1L), .Label = "car", class = "factor"), A = c(2L, 
     5L, 7L, 1L, 4L), B = c(1L, 4L, 5L, 7L, 6L), C = c(7L, 4L, 
     4L, 37L, 8L), ID = structure(c(1L, 1L, 1L, 1L, 1L), .Label = "bike", class = "factor"), 
     A = c(4L, 9L, 68L, 9L, 9L), B = c(9L, 5L, 7L, 8L, 0L), C = c(0L, 
     4L, 56L, 7L, 8L), ID = structure(c(1L, 1L, 1L, 1L, 1L), .Label = "plane", class = "factor")), .Names = c("file_path", 
    "Condition", "Trial.Num", "A", "B", "C", "ID", "A", "B", "C", 
    "ID", "A", "B", "C", "ID"), class = "data.frame", row.names = c(NA, 
    -5L)) 


structure(list(file_path = structure(c(3L, 4L, 2L, 5L, 1L, 3L, 
4L, 2L, 5L, 1L, 3L, 4L, 2L, 5L, 1L), .Label = c("root/defined.extension", 
"root/else.extension", "root/some.extension", "root/thing.extension", 
"root/uniquely.extension"), class = "factor"), Condition = structure(c(1L, 
1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L), .Label = c("Baseline", 
"Treatment"), class = "factor"), Trial.Num = c(1L, 2L, 3L, 1L, 
2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L, 2L), A = c(2L, 3L, 4L, 
5L, 6L, 2L, 5L, 7L, 1L, 4L, 4L, 9L, 68L, 9L, 9L), B = c(3L, 6L, 
4L, 3L, 7L, 1L, 4L, 5L, 7L, 6L, 9L, 5L, 7L, 8L, 0L), C = c(5L, 
45L, 6L, 7L, 3L, 7L, 4L, 4L, 37L, 8L, 0L, 4L, 56L, 7L, 8L), ID = structure(c(2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L), .Label = c("bike", 
"car", "plane"), class = "factor")), .Names = c("file_path", 
"Condition", "Trial.Num", "A", "B", "C", "ID"), class = "data.frame", row.names = c(NA, 
-15L)) 

Answer 1

Sie die make.unique -function verwenden können eindeutige Spaltennamen zu erstellen. Danach Sie melt vom data.table -package verwenden können, die mehrere Wertspalten basierend auf patterns in den Spaltennamen erstellen Lage ist:

# make the column names unique 
names(toy) <- make.unique(names(toy)) 
# let the 'Condition' column start with a small letter 'c' 
# so it won't be detected by the patterns argument from melt 
names(toy)[2] <- tolower(names(toy)[2]) 

# load the 'data.table' package 
library(data.table) 
# tidy the data into long format 
tidy_toy <- melt(setDT(toy), 
       measure.vars = patterns('^A','^B','^C','^ID'), 
       value.name = c('A','B','C','ID')) 

die gibt:

> tidy_toy 
        file_path condition Trial.Num variable A B C ID 
1:  root/some.extension Baseline   1  1 2 3 5 car 
2: root/thing.extension Baseline   2  1 3 6 45 car 
3:  root/else.extension Baseline   3  1 4 4 6 car 
4: root/uniquely.extension Treatment   1  1 5 3 7 car 
5: root/defined.extension Treatment   2  1 6 7 3 car 
6:  root/some.extension Baseline   1  2 2 1 7 bike 
7: root/thing.extension Baseline   2  2 5 4 4 bike 
8:  root/else.extension Baseline   3  2 7 5 4 bike 
9: root/uniquely.extension Treatment   1  2 1 7 37 bike 
10: root/defined.extension Treatment   2  2 4 6 8 bike 
11:  root/some.extension Baseline   1  3 4 9 0 plane 
12: root/thing.extension Baseline   2  3 9 5 4 plane 
13:  root/else.extension Baseline   3  3 68 7 56 plane 
14: root/uniquely.extension Treatment   1  3 9 8 7 plane 
15: root/defined.extension Treatment   2  3 9 0 8 plane 

Eine weitere Option ist ein zu verwenden, Liste der Spalten-Indizes für measure.vars:

tidy_toy <- melt(setDT(toy), 
       measure.vars = list(c(4,8,12), c(5,9,13), c(6,10,14), c(7,11,15)), 
       value.name = c('A','B','C','ID')) 

machen die Spaltennamen eindeutig ist dann nicht erforderlich.

---

Eine kompliziertere Methode, die Namen, die sind besser unterscheidbar durch das patterns Argument erzeugt:

# select the names that are not unique 
tt <- table(names(toy)) 
idx <- which(names(toy) %in% names(tt)[tt > 1]) 
nms <- names(toy)[idx] 

# make them unique 
names(toy)[idx] <- paste(nms, 
         rep(seq(length(nms)/length(names(tt)[tt > 1])), 
          each = length(names(tt)[tt > 1])), 
         sep = '.') 

# your columnnames are now unique: 
> names(toy) 
[1] "file_path" "Condition" "Trial.Num" "A.1"  "B.1"  "C.1"  "ID.1"  "A.2"  
[9] "B.2"  "C.2"  "ID.2"  "A.3"  "B.3"  "C.3"  "ID.3"  

# tidy the data into long format 
tidy_toy <- melt(setDT(toy), 
       measure.vars = patterns('^A.\\d','^B.\\d','^C.\\d','^ID.\\d'), 
       value.name = c('A','B','C','ID')) 

, die das gleiche Endergebnis geben.

---

Wie in den Kommentaren erwähnt, die janitor -Paket kann auch für dieses Problem hilfreich sein. Die clean_names() funktioniert ähnlich wie die make.unique Funktion. See here für eine Erklärung.