Question

I know there are many questions here in SO about ways to convert a list of data.frames to a single data.frame using do.call or ldply, but this questions is about understanding the inner workings of both methods and trying to figure out why I can’t get either to work for concatenating a list of almost 1 million df’s of the same structure, same field names, etc. into a single data.frame. Each data.frame is of one row and 21 columns.

The data started out as a JSON file, which I converted to lists using fromJSON, then ran another lapply to extract part of the list and converted to data.frame and ended up with a list of data.frames.

I’ve tried:

df <- do.call("rbind", list)
df <- ldply(list)

but I’ve had to kill the process after letting it run up to 3 hours and not getting anything back.

Is there a more efficient method of doing this? How can I troubleshoot what is happening and why is it taking so long?

FYI – I’m using RStudio server on a 72GB quad-core server with RHEL, so I don’t think memory is the problem. sessionInfo below:

> sessionInfo()
R version 2.14.1 (2011-12-22)
Platform: x86_64-redhat-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=C                 LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] multicore_0.1-7 plyr_1.7.1      rjson_0.2.6    

loaded via a namespace (and not attached):
[1] tools_2.14.1
> 

Answer 1

Given that you are looking for performance, it appears that a data.table solution should be suggested.

There is a function rbindlist which is the same but much faster than do.call(rbind, list)

library(data.table)
X <- replicate(50000, data.table(a=rnorm(5), b=1:5), simplify=FALSE)
system.time(rbindlist.data.table <- rbindlist(X))
##  user  system elapsed 
##  0.00    0.01    0.02

It is also very fast for a list of data.frame

Xdf <- replicate(50000, data.frame(a=rnorm(5), b=1:5), simplify=FALSE)

system.time(rbindlist.data.frame <- rbindlist(Xdf))
##  user  system elapsed 
##  0.03    0.00    0.03

For comparison

system.time(docall <- do.call(rbind, Xdf))
##  user  system elapsed 
## 50.72    9.89   60.88 

And some proper benchmarking

library(rbenchmark)
benchmark(rbindlist.data.table = rbindlist(X), 
           rbindlist.data.frame = rbindlist(Xdf),
           docall = do.call(rbind, Xdf),
           replications = 5)
##                   test replications elapsed    relative user.self sys.self 
## 3               docall            5  276.61 3073.444445    264.08     11.4 
## 2 rbindlist.data.frame            5    0.11    1.222222      0.11      0.0 
## 1 rbindlist.data.table            5    0.09    1.000000      0.09      0.0 

and against @JoshuaUlrich’s solutions

benchmark(use.rbl.dt  = rbl.dt(X), 
          use.rbl.ju  = rbl.ju (Xdf),
          use.rbindlist =rbindlist(X) ,
          replications = 5)

##              test replications elapsed relative user.self 
## 3  use.rbindlist            5    0.10      1.0      0.09
## 1     use.rbl.dt            5    0.10      1.0      0.09
## 2     use.rbl.ju            5    0.33      3.3      0.31 

I’m not sure you really need to use as.data.frame, because a data.table inherits class data.frame