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Effective Collapse Variables

What is an efficient way (any welcome solution, including welcome packages) to collapse dummy variables back into a factor.

race.White race.Hispanic race.Black race.Asian 1 1 0 0 0 2 0 0 0 1 3 1 0 0 0 4 0 0 1 0 5 0 0 0 1 6 0 1 0 0 7 1 0 0 0 8 1 0 0 0 9 1 0 0 0 10 0 0 1 0

Required Conclusion:

  race 1 White 2 Asian 3 White 4 Black 5 Asian 6 Hispanic 7 White 8 White 9 White 10 Black

Data:

 dat <- structure(list(race.White = c(1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L), race.Hispanic = c(0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L), race.Black = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L), race.Asian = c(0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L)), .Names = c("race.White", "race.Hispanic", "race.Black", "race.Asian"), row.names = c(NA, -10L), class = "data.frame")

What I tried:

This is a possible solution, but I'm sure there is a better indexing solution /dplyr/data.table/.etc.

 apply(dat, 1, function(x) sub("[^.]+\\.", "", colnames(dat))[x])
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2 answers

Another idea:

 ff = function(x) { ans = integer(nrow(x)) for(i in seq_along(x)) ans[as.logical(x[[i]])] = i names(x)[ans] } sub("[^.]+\\.", "", ff(dat)) #[1] "White" "Asian" "White" "Black" "Asian" "Hispanic" "White" "White" "White" "Black"

And compare with akrun alternatives:

 akrun1 = function(x) names(x)[max.col(x, "first")] akrun2 = function(x) names(x)[(as.matrix(x) %*% seq_along(x))[, 1]] akrun3 = function(x) names(x)[do.call(pmax, x * seq_along(x)[col(x)])] akrunlike = function(x) names(x)[do.call(pmax, Map("*", x, seq_along(x)))] DF = setNames(as.data.frame("[<-"(matrix(0L, 1e4, 1e3), cbind(seq_len(1e4), sample(1e3, 1e4, TRUE)), 1L)), paste("fac", 1:1e3, sep = "")) identical(ff(DF), akrun1(DF)) #[1] TRUE identical(ff(DF), akrun2(DF)) #[1] TRUE identical(ff(DF), akrun3(DF)) #[1] TRUE identical(ff(DF), akrunlike(DF)) #[1] TRUE microbenchmark::microbenchmark(ff(DF), akrun1(DF), akrun2(DF), akrun3(DF), akrunlike(DF), as.matrix(DF), col(DF), times = 30) #Unit: milliseconds # expr min lq median uq max neval # ff(DF) 61.99124 64.56194 78.62267 102.18424 152.64891 30 # akrun1(DF) 296.89042 314.28641 327.95059 353.46185 394.46013 30 # akrun2(DF) 103.76105 114.01497 120.12191 129.86513 166.13266 30 # akrun3(DF) 1141.46478 1163.96842 1178.92961 1203.83848 1231.70346 30 # akrunlike(DF) 125.47542 130.20826 141.66123 157.92743 203.42331 30 # as.matrix(DF) 19.46940 20.54543 28.22377 35.69575 87.06001 30 # col(DF) 103.61454 112.75450 116.00120 126.09138 176.97435 30

I have included as.matrix() and col() to show that the "list" -y structures can be convenient with an efficient loop as it is. For example, unlike a looping cycle, using a loop using columns does not take time to transform the data structure.

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We can use max.col to get the column index, a subset of column names based on this, and use sub to remove the prefix.

 sub('[^.]+\\.', '', names(dat)[max.col(dat)]) #[1] "White" "Asian" "White" "Black" "Asian" "Hispanic" #[7] "White" "White" "White" "Black"

Here I assumed that for each row there is one 1 . If there are multiple 1s, we can use the ties.method='first' or ties.method='last' option.

Or another option executes %*% with a sequence of columns, a subset of column names, and removes the prefix with sub .

  sub('[^.]+\\.', '', names(dat)[(as.matrix(dat) %*%seq_along(dat))[,1]])

Or we can use pmax

 sub('[^.]+\\.', '', names(dat)[do.call(pmax,dat*seq_along(dat)[col(dat)])])
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