Why is rbindlist "better" than rbind?

I am going through documentation of data.table and also noticed from some of the conversations over here on SO that rbindlist is supposed to be better than rbind.

I would like to know why is rbindlist better than rbind and in which scenarios rbindlist really excels over rbind?

Is there any advantage in terms of memory utilization?

rbindlist is an optimized version of do.call(rbind, list(...)), which is known for being slow when using rbind.data.frame

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Where does it really excel

Some questions that show where rbindlist shines are

https://stackoverflow.com/questions/4863341/how-to-merge-a-list-of-data-frames-by-row/12718498#12718498

https://stackoverflow.com/questions/9728407/trouble-converting-long-list-of-data-frames-1-million-to-single-data-frame-us/9730292#9730292

These have benchmarks that show how fast it can be.

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rbind.data.frame is slow, for a reason

rbind.data.frame does lots of checking, and will match by name. (i.e. rbind.data.frame will account for the fact that columns may be in different orders, and match up by name), rbindlist doesn't do this kind of checking, and will join by position

eg

do.call(rbind, list(data.frame(a = 1:2, b = 2:3), data.frame(b = 1:2, a = 2:3)))
##    a b
## 1  1 2
## 2  2 3
## 3  2 1
## 4  3 2

rbindlist(list(data.frame(a = 1:5, b = 2:6), data.frame(b = 1:5, a = 2:6)))
##     a b
##  1: 1 2
##  2: 2 3
##  3: 1 2
##  4: 2 3

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Some other limitations of rbindlist

It used to struggle to deal with factors, due to a bug that has since been fixed:

https://stackoverflow.com/questions/15933846/ (Bug #2650)

It has problems with duplicate column names

see https://stackoverflow.com/questions/14584510/ (Bug #2384)

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rbind.data.frame rownames can be frustrating

rbindlist can handle lists data.frames and data.tables, and will return a data.table without rownames

you can get in a muddle of rownames using do.call(rbind, list(...)) see

https://stackoverflow.com/questions/14972998/how-to-avoid-renaming-of-rows-when-using-rbind-inside-do-call/14973062#14973062

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Memory efficiency

In terms of memory rbindlist is implemented in C, so is memory efficient, it uses setattr to set attributes by reference

rbind.data.frame is implemented in R, it does lots of assigning, and uses attr<- (and class<- and rownames<- all of which will (internally) create copies of the created data.frame.

By v1.9.2, rbindlist had evolved quite a bit, implementing many features including:

> * Choosing the highest SEXPTYPE of columns while binding - implemented in v1.9.2 closing FR #2456 and Bug #4981. > * Handling factor columns properly - first implemented in v1.8.10 closing Bug #2650 and extended to binding ordered factors carefully in v1.9.2 as well, closing FR #4856 and Bug #5019.

In addition, in v1.9.2, rbind.data.table also gained a fill argument, that allows to bind by filling missing columns, implemented in R.

Now in v1.9.3, there are even more improvements on these existing features:

> * rbindlist gains an argument use.names, which by default is FALSE for backwards compatibility. > * rbindlist also gains an argument fill, which by default is also FALSE for backwards compatibility. > * These features are all implemented in C, and written carefully to not compromise in speed while adding functionalities. > * Since rbindlist can now match by names and fill missing columns, rbind.data.table just calls rbindlist now. The only difference is that use.names=TRUE by default for rbind.data.table, for backwards compatibility.

rbind.data.frame slows down quite a bit mostly due to copies (which @mnel points out as well) that could be avoided (by moving to C). I think that's not the only reason. The implementation for checking/matching column names in rbind.data.frame could also get slower when there are many columns per data.frame and there are many such data.frames to bind (as shown in the benchmark below).

However, that rbindlist lack(ed) certain features (like checking factor levels or matching names) bears very tiny (or no) weight towards it being faster than rbind.data.frame. It's because they were carefully implemented in C, optimised for speed and memory.

Here's a benchmark that highlights the efficient binding while matching by column names as well using rbindlist's use.names feature from v1.9.3. The data set consists of 10000 data.frames each of size 10*500.

NB: this benchmark has been updated to include a comparison to dplyr's bind_rows

library(data.table) # 1.11.5, 2018-06-02 00:09:06 UTC
library(dplyr) # 0.7.5.9000, 2018-06-12 01:41:40 UTC
set.seed(1L)
names = paste0("V", 1:500)
cols = 500L
foo <- function() {
    data = as.data.frame(setDT(lapply(1:cols, function(x) sample(10))))
    setnames(data, sample(names))
}
n = 10e3L
ll = vector("list", n)
for (i in 1:n) {
    .Call("Csetlistelt", ll, i, foo())
}

system.time(ans1 <- rbindlist(ll))
#  user  system elapsed 
# 1.226   0.070   1.296 
  
system.time(ans2 <- rbindlist(ll, use.names=TRUE))
#  user  system elapsed 
# 2.635   0.129   2.772 
  
system.time(ans3 <- do.call("rbind", ll))
#   user  system elapsed 
# 36.932   1.628  38.594 
 
system.time(ans4 <- bind_rows(ll))
#   user  system elapsed 
# 48.754   0.384  49.224 

identical(ans2, setDT(ans3)) 
# [1] TRUE
identical(ans2, setDT(ans4))
# [1] TRUE

Binding columns as such without checking for names took just 1.3 where as checking for column names and binding appropriately took just 1.5 seconds more. Compared to base solution, this is 14x faster, and 18x faster than dplyr's version.