convert a data.table to a vector rowwise

Question

I have a data.table such as:

example <- data.table(fir =c(\"A\", \"B\", \"C\", \"A\",\"A\", \"B\", \"C\"), las=c( \"B\", \"C\",\"B\", \"C\", \"B\", \"C\",\"C\         


        

Answer 1

As in a matrix as well as a data.frame/data.table (though different from a matrix), data is stored column wise, you can transpose it first:

as.vector(t(example))
# [1] "A" "B" "B" "C" "C" "B" "A" "C" "A" "B" "B" "C" "C" "C"

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A benchmark testing including options provided by @Sotos, @Frank and @Wen using a dummy data set:

example <- as.data.table(matrix(sample(LETTERS, 10^7, replace = T), ncol = 1000))
dim(example)
#[1] 10000  1000

library(microbenchmark)
psidom <- function() as.vector(t(example))
sotos <- function() c(t(example))
frank <- function() unlist(transpose(example), use.names = FALSE)
wen <- function() unname(unlist(data.frame(t(example))))

# data.table 1.10.4
microbenchmark(psidom(), sotos(), frank(), wen(), times = 10)

#Unit: milliseconds
#     expr       min        lq      mean    median        uq       max neval
# psidom()  163.5993  178.9236  393.4838  198.6753  632.1086  1352.012    10
#  sotos()  186.8764  188.3734  467.2117  343.1514  618.3121  1221.721    10
#  frank() 3065.0988 3493.3691 5315.4451 4649.4643 5742.2399  9560.642    10
#    wen() 7316.6743 8497.1409 9200.4397 9038.2834 9631.5313 11931.075    10

Another test in data.table dev version 1.10.5:

# data.table 1.10.5
psidom <- function() as.vector(t(example))
sotos <- function() c(t(example))
frank <- function() unlist(transpose(example), use.names = FALSE)
fast <- function() `attributes<-`(t(example), NULL)

microbenchmark(psidom(), sotos(), frank(), fast(), times = 10)
#Unit: milliseconds
#     expr      min       lq     mean   median       uq      max neval
# psidom() 228.1248 246.4666 271.6772 256.9131 287.5072 354.2053    10
#  sotos() 254.3512 280.2504 315.3487 322.5726 344.7125 390.3482    10
#  frank() 290.5476 310.7076 374.6267 349.8021 431.8451 491.9301    10
#   fast() 159.6006 167.6316 209.8363 196.8821 272.4758 281.3146    10