Vector of numbers and index into data.table

Question

What's the best way to get the following vector into a data.table along with the index of each number?

nVector <- c("20 37", "38 23", "39 48", "45 76", "65 44", "86 95 80")

Here is what I have, is there a better way?

vLength <- unname(sapply(nVector, function(x) length(unlist( strsplit(x, " "))) ))
vSeq <- seq(1, length(vLength))
vPosition <- vector("list", length(vLength))

for(i in 1:length(vLength))
{
  vPosition[[i]] <- rep(vSeq[i], vLength[i])
}

DT <- data.table(index = unlist(vPosition),
                 value = unlist(strsplit(nVector, " ")))

Note: The length of the vector is about 125k, and there are ~ 20 million values.

Answer 1

Why not simply:

data.table(v1 = nVector)[, index := .I][, list(unlist(strsplit(v1, " "))), by = index]
##     index V1
##  1:     1 20
##  2:     1 37
##  3:     2 38
##  4:     2 23
##  5:     3 39
##  6:     3 48
##  7:     4 45
##  8:     4 76
##  9:     5 65
## 10:     5 44
## 11:     6 86
## 12:     6 95
## 13:     6 80

---

Alternatively, you can create a function like the following (using a function is more for convenience of reuse--not necessary if its just a one-time problem):

fun <- function(invec) {
  x <- strsplit(invec, " ", TRUE)
  data.table(index = rep(seq_along(x), lengths(x)), V1 = unlist(x, use.names = FALSE))
}

fun(nVector)

Note that the use of fixed = TRUE will give you a good speed boost--so you should consider that even in the "data.table" approach.

---

Finally, as @Jaap suggests, you can use cSplit from my "splitstackshape" package, like this:

library(splitstackshape)
cSplit(data.table(v1 = nVector)[, index := .I], "v1", sep = " ", direction = "long")

---

Update

Since performance is an issue considering the size of your data, you might want to use the fun approach, where you manually create the "data.table".

Here's some timing on a larger version of your vector:

NVector <- rep(nVector, 10000)
length(NVector)
# [1] 60000

f1 <- function(invec) {
  data.table(v1 = invec)[, index := .I][
    , list(unlist(strsplit(v1, " ", TRUE))), by = index]
} 

f2 <- function(invec) {
  cSplit(data.table(v1 = invec)[, index := .I], 
         "v1", sep = " ", direction = "long")
} 

library(microbenchmark)
microbenchmark(fun(NVector), f1(NVector), f2(NVector), times = 50)
# Unit: milliseconds
#          expr       min        lq      mean    median        uq       max neval
#  fun(NVector)  13.26559  13.70738  15.89918  14.12573  15.11083  50.84675    50
#   f1(NVector) 196.95570 207.60004 223.74729 212.49649 224.78725 378.51007    50
#   f2(NVector) 167.38512 176.16370 196.28389 183.96098 202.00187 412.71760    50

---

Update: 28 December 2017

The performance of any of these is likely to depend at least in part on the number of resulting split pieces, so I thought I would:

• Update the answer with some more irregular data
• Add a few more options, specifically:
  • An alternative base R approach
  • An alternative to fun using stringi
  • A possible "tidyverse" approach

Here is the new sample data:

library(stringi)
set.seed(2)
NVec2 <- vapply(sample(20, 60000, TRUE), 
                function(x) paste(stri_rand_strings(x, 5, "[0-9]"), collapse = " "), 
                character(1L))

length(NVec2)
# [1] 60000

Here are the new functions:

## like `fun`, but using `stri_split_fixed`
fun_stringi <- function(invec) {
  x <- stri_split_fixed(invec, " ")
  data.table(index = rep(seq_along(x), lengths(x)), V1 = unlist(x, use.names = FALSE))
}

## A base R alternative
f3 <- function(invec) stack(setNames(strsplit(invec, " ", TRUE), seq_along(invec)))

## A tidyverse approach
f4 <- function(invec) {
  data_frame(ind = seq_along(invec), 
             val = stri_split_fixed(invec, " ")) %>% 
    unnest()
} 

And the new benchmarking:

library(microbenchmark)
res <- microbenchmark(base = fun(NVec2), stringi = fun_stringi(NVec2),
                      data_table = f1(NVec2), splitstackshape = f2(NVec2), 
                      base_alt = f3(NVec2), tidyverse = f4(NVec2), times = 50)
res
# Unit: milliseconds
#             expr      min       lq     mean   median       uq      max neval
#             base 162.6149 174.7311 204.0177 187.3446 213.7267 443.8357    50
#          stringi 146.8655 157.6717 187.1125 168.5383 192.1952 394.1169    50
#       data_table 360.0788 382.9118 427.2276 396.0421 418.1821 598.3754    50
#  splitstackshape 542.8882 578.6317 619.9677 598.5113 626.5734 901.9400    50
#         base_alt 259.2847 293.7944 325.6021 310.7322 339.1613 492.4644    50
#        tidyverse 500.1571 519.4765 545.4757 534.1167 549.4756 713.3711    50

---

Increasing the data further to get closer to simulating your actual dataset, performance really starts to converge--with the exception of "splitstackshape", which slows down horribly :-(

Here's a sample:

library(stringi)
set.seed(2)
NVec3 <- vapply(sample(100:200, 125000, TRUE), 
                function(x) paste(stri_rand_strings(x, 5, "[0-9]"), collapse = " "), 
                character(1L))

system.time({out <- f2(NVec3)})
#   user  system elapsed 
#  20.89    0.03   20.94 

## Similar to your actual data
length(NVec3)
# [1] 125000
nrow(out)
# [1] 18767938

res <- microbenchmark(base = fun(NVec3), stringi = fun_stringi(NVec3),
                      data_table = f1(NVec3), base_alt = f3(NVec3), 
                      tidyverse = f4(NVec3), times = 20)
res
## Unit: seconds
##        expr      min       lq     mean   median       uq      max neval
##        base 4.967281 5.606208 5.983120 5.978414 6.345823 7.189997    20
##     stringi 4.888080 5.292926 5.811898 5.728464 6.091029 7.923210    20
##  data_table 5.625772 5.861431 6.244174 6.092079 6.420082 7.698534    20
##    base_alt 4.635496 5.015382 5.564661 5.486531 6.090838 7.034357    20
##   tidyverse 5.634781 6.186927 6.717203 6.613003 7.198013 8.154297    20

autoplot(res, log = FALSE)

At this point, and assuming that you're unlikely to be doing this repeatedly, any of the options should be pretty decent to actually use. I was personally surprised that stack outperformed all of the other options....