R fast way for extracting elements from lists of List

Question

Hello everyone, I am working with large list, which contains lists. Each of the sub lists contains n elements. I always want to get the 3rd one, eg

l = list()
l[[1]] = list(A=runif(1), B=runif(1), C=runif(1))
l[[2]] = list(A=runif(1), B=runif(1), C=runif(1))
l[[3]] = list(A=runif(1), B=runif(1), C=runif(1))

res = sapply(l, function(x) x$C)
res = sapply(l, function(x) x[[3]]) #alternative

But my list contains several thousands of elements and I am performing this operation a lot of times. So, is there a faster way to do the operation above?

Beste regards,

Mario

Answer 1

If you do this mulitple times, then it would be better to convert your list to an easier structure like data.table .

library(data.table)
DT=rbindlist(l);
res = DT$C
# or if you prefer the 3rd element, not necessarily called 'C' then:
res = DT[[3]] # or DT[,C] which might be faster. Please check @richard-scriven comment

Alternatively if you want to keep base R you could use rbind

res = do.call(rbind.data.frame, l)$C # or [[3]]

Would this make things easier?

UPDATE

Here are some benchmarks showing different solutions to the problem:

preparations:

library(data.table)
library(microbenchmark)

# creating a list and filling it with items 
nbr   = 1e5;
l     = vector("list",nbr)
for (i in 1:nbr) {
  l[[i]] = list(A=runif(1), B=runif(1), C=runif(1))
}

# creating data.frame and data.table versions
DT <- rbindlist(l)
DF <- data.frame(rbindlist(l))

benchmarking:

# doing the benchmarking
op <- 
  microbenchmark(
    LAPPLY.1 = lapply(l, function(x) x$C),
    LAPPLY.2 = lapply(l, `[`, "C"),
    LAPPLY.3 = lapply(l, `[[`, "C"),

    SAPPLY.1 = sapply(l, function(x) x$C),
    SAPPLY.2 = sapply(l, function(x) x[[3]]),
    SAPPLY.3 = sapply(l, `[[`, 3),
    DT.1     = rbindlist(l)$C,

    DT.2     = DT$C,
    DF.2     = DF$C,
    times    = 100
  )

results:

op 

## Unit: microseconds
## expr        min     lq   mean median     uq   max neval
## LAPPLY.1 124088 142390 161672 154415 163240 396761  100
## LAPPLY.2 111397 134745 156012 150062 165229 364539  100
## LAPPLY.3  66965  71608  82975  77329  84949 323041  100
## SAPPLY.1 133220 149093 166653 159222 172495 311857  100
## SAPPLY.2 105917 119533 137990 133364 139216 346759  100
## SAPPLY.3  70391  74726  81910  80520  85792 110062  100
## DT.1      46895  48943  49113  49178  49391  51377  100
## DT.2          8     18     37     47     49     58  100
## DF.2          7     13     33     40     42     82  100

(1) In general it would be best to use a table like structure like data.frame or data.table in the first place - selecting columns from those costs the least of time.

(2) If this is not possible it is better to first turn the list into a data.frame or data.table to than extract the values in one single operation.

(3) Interestingly using sapply or lapply with the base R (optimized) [[ -function results in process times that are only twice as bad as using rbind and than extracting the values as column.