Fastest way to transpose a list in R / Rcpp

"list"s are R objects with no C equivalent, so manipulating them in C will gain efficiency only in terms of surrounding computations, since the actual transposing will need to come back and forth between R objects. Arun's transpose is a concise approach to this problem and, seemingly, can't get any better. I'll just provide some other alternatives just to show that transposing a "list" can be cranky and maybe adopting a different approach to achieve the final goal might be better.

map = function(x) .mapply(c, x, NULL)

lap = function(x) lapply(seq_along(x[[1]]), function(i) unlist(lapply(x, "[[", i)))

library(data.table)
DT = function(x) transpose(x)

# very simple C loop that proves that `data.table::transpose` is as good as it gets
loopC = inline::cfunction(sig = c(R_ls = "list"), body = '
    SEXPTYPE tp = 0;
    SEXP ans, tmp;
    PROTECT(ans = allocVector(VECSXP, LENGTH(VECTOR_ELT(R_ls, 0))));
    for(int i = 0; i < LENGTH(R_ls); i++) {
        tmp = VECTOR_ELT(R_ls, i);
        if(TYPEOF(tmp) > tp) tp = TYPEOF(tmp);
    }
    for(int i = 0; i < LENGTH(ans); i++) SET_VECTOR_ELT(ans, i, allocVector(tp, LENGTH(R_ls)));

    switch(tp) {
        case LGLSXP:
        case INTSXP: {
            for(int i = 0; i < LENGTH(R_ls); i++) {
                PROTECT(tmp = coerceVector(VECTOR_ELT(R_ls, i), tp));
                int *ptmp = INTEGER(tmp);
                for(int j = 0; j < LENGTH(ans); j++) INTEGER(VECTOR_ELT(ans, j))[i] = ptmp[j];
                UNPROTECT(1);
            }

            break;
        }
        case REALSXP: {
            for(int i = 0; i < LENGTH(R_ls); i++) {
                PROTECT(tmp = coerceVector(VECTOR_ELT(R_ls, i), tp));
                double *ptmp = REAL(tmp);
                for(int j = 0; j < LENGTH(ans); j++) REAL(VECTOR_ELT(ans, j))[i] = ptmp[j];
                UNPROTECT(1);
            }

            break;
        }
        case STRSXP: {
            for(int i = 0; i < LENGTH(R_ls); i++) {
                PROTECT(tmp = coerceVector(VECTOR_ELT(R_ls, i), tp));
                for(int j = 0; j < LENGTH(ans); j++) SET_STRING_ELT(VECTOR_ELT(ans, j), i, STRING_ELT(tmp, j));
                UNPROTECT(1);
            }

            break;
        }
    }

    UNPROTECT(1);
    return(ans);
')

spl = function(x) split(unlist(x), rep(seq_along(x[[1]]), length(x)))

map(ls)
#[[1]]
#[1] "a"   "1"   "foo"
#
#[[2]]
#[1] "b"   "2"   "bar"
#
#[[3]]
#[1] "c"   "3"   "baz"
#
lap(ls)
#[[1]]
#[1] "a"   "1"   "foo"
#
#[[2]]
#[1] "b"   "2"   "bar"
#
#[[3]]
#[1] "c"   "3"   "baz"
#
DT(ls)
#[[1]]
#[1] "a"   "1"   "foo"
#
#[[2]]
#[1] "b"   "2"   "bar"
#
#[[3]]
#[1] "c"   "3"   "baz"
#
loopC(ls)
#[[1]]
#[1] "a"   "1"   "foo"
#
#[[2]]
#[1] "b"   "2"   "bar"
#
#[[3]]
#[1] "c"   "3"   "baz"
#
spl(ls)
#$`1`
#[1] "a"   "1"   "foo"
#
#$`2`
#[1] "b"   "2"   "bar"
#
#$`3`
#[1] "c"   "3"   "baz"

And a benchmark:

myls1 = rep_len(list(sample(1e3), runif(1e3), sample(letters, 1e3, T)), 1e3)  #1e3 x 1e3
myls2 = rep_len(list(sample(1e5), runif(1e5), sample(letters, 1e5, T)), 1e1)  #10 x 1e5
myls3 = rep_len(list(sample(1e1), runif(1e1), sample(letters, 1e1, T)), 1e5)  #1e5 x 10

identical(map(myls1), lap(myls1))
#[1] TRUE
identical(map(myls1), DT(myls1))
#[1] TRUE
identical(map(myls1), loopC(myls1))
#[1] TRUE
identical(map(myls1), unname(spl(myls1)))
#[1] TRUE

microbenchmark::microbenchmark(map(myls1), lap(myls1), DT(myls1), loopC(myls1), spl(myls1),
                                map(myls2), lap(myls2), DT(myls2), loopC(myls2), spl(myls2),
                                map(myls3), lap(myls3), DT(myls3), loopC(myls3), spl(myls3), 
                                times = 10)
#Unit: milliseconds
#         expr       min        lq    median        uq       max neval
#   map(myls1) 1141.9477 1187.8107 1281.4314 1331.4490 1961.8452    10
#   lap(myls1) 1082.7023 1104.6467 1182.8303 1219.5397 1695.6164    10
#    DT(myls1)  378.0574  399.7339  433.4307  459.0293  495.2200    10
# loopC(myls1)  390.0305  392.5139  405.6461  480.7480  638.9145    10
#   spl(myls1)  676.2639  756.1798  786.8639  821.7699  869.0219    10
#   map(myls2) 1241.1010 1304.2250 1386.1915 1439.5182 1546.3835    10
#   lap(myls2) 1823.2029 1922.1878 1965.6653 2006.6102 2161.9819    10
#    DT(myls2)  471.5797  521.7380  554.2221  578.3043  887.1452    10
# loopC(myls2)  472.5713  494.9302  524.2538  591.0493  657.6087    10
#   spl(myls2) 1108.1530 1117.7448 1212.0051 1297.8838 1336.8266    10
#   map(myls3) 2005.1325 2178.3739 2214.1824 2451.7050 2539.5152    10
#   lap(myls3) 1172.3033 1215.1297 1242.0294 1292.7345 1434.1707    10
#    DT(myls3)  388.6679  393.5446  416.5494  479.1473  721.0758    10
# loopC(myls3)  389.4098  396.6768  404.9609  432.4390  451.8912    10
#   spl(myls3)  675.7749  704.3328  767.0548  817.7189  937.1469    10

In the data.table package, there's a transpose() function which does exactly this. It is implemented in C for speed.

require(data.table) # v1.9.6+
transpose(ls)
# [[1]]
# [1] "a"   "1"   "foo"

# [[2]]
# [1] "b"   "2"   "bar"

# [[3]]
# [1] "c"   "3"   "baz"

It also fills automatically with NA in case the list elements are not of equal lengths, and also coerces automatically to the highest SEXPTYPE. You can provide a different value to the fill argument if necessary. Check ?transpose.

Tags:

Performance

R

Rcpp

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