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
> 

```