plyr's idata.frame VS. data.frame

I had seen the function idata.frame in plyr before, but not really tested it. From the plyr documentation:

"An immutable data frame works like an ordinary data frame, except that when you subset it, it
returns a reference to the original data frame, not a a copy. This makes subsetting substantially
faster and has a big impact when you are working with large datasets with many groups."

For example, although baseball is a data.frame, its immutable counterpart is a reference to it:

> idata.frame(baseball)
<environment: 0x1022c74e8>
attr(,"class")
[1] "idf"         "environment"

Here are a few comparisons of operations on normal data frames and immutable data frames. Immutable data frames don't work with the doBy package, but do work with aggregate in base functions.  Overall, the speed gains using idata.frame are quite impressive - I will use it more often for sure.


Get the github code below here.





Here's the comparisons of idata.frames and data.frames:

> # load packages
require(plyr); require(reshape2)
 
> # Make immutable data frame
baseball_i <- idata.frame(baseball)
 
> # Example 1 - idata.frame more than twice as fast
system.time( replicate(50, ddply( baseball, "year", summarise, mean(rbi))) )
   user  system elapsed 
 14.812   0.252  15.065 
> system.time( replicate(50, ddply( baseball_i, "year", summarise, mean(rbi))) )
   user  system elapsed 
  6.895   0.020   6.915 

> # Example 2 - Bummer, this does not work with idata.frame's> colwise(max, is.numeric) ( baseball ) # works
  year stint   g  ab   r   h X2b X3b hr rbi sb cs  bb so ibb hbp sh sf gidp
1 2007     4 165 705 177 257  64  28 73  NA NA NA 232 NA  NA  NA NA NA   NA
> colwise(max, is.numeric) ( baseball_i ) # doesn't work
Error: is.data.frame(df) is not TRUE

> # Example 3 - idata.frame twice as fast
system.time( replicate(100, baseball[baseball$year == "1884", ] ) )
   user  system elapsed 
  1.155   0.048   1.203 
> system.time( replicate(100, baseball_i[baseball_i$year == "1884", ] ) )
   user  system elapsed 
  0.598   0.011   0.609 

> # Example 4 - idata.frame faster
system.time( replicate(50, melt(baseball[, 1:4], id = 1) ) )
   user  system elapsed 
 16.587   1.169  17.755 
> system.time( replicate(50, melt(baseball_i[, 1:4], id = 1) ) )
   user  system elapsed 
  0.869   0.196   1.065 

> # And you can go back to a data frame by 
d <- as.data.frame(baseball_i)
str(d)
 
'data.frame': 21699 obs. of  23 variables:
 $ id   : chr  "ansonca01" "forceda01" "mathebo01" "startjo01" ...
 $ year : int  1871 1871 1871 1871 1871 1871 1871 1872 1872 1872 ...
 $ stint: int  1 1 1 1 1 1 1 1 1 1 ...
 $ team : chr  "RC1" "WS3" "FW1" "NY2" ...
 $ lg   : chr  "" "" "" "" ...
 $ g    : int  25 32 19 33 29 29 29 46 37 25 ...
 $ ab   : int  120 162 89 161 128 146 145 217 174 130 ...
 $ r    : int  29 45 15 35 35 40 36 60 26 40 ...
 $ h    : int  39 45 24 58 45 47 37 90 46 53 ...
 $ X2b  : int  11 9 3 5 3 6 5 10 3 11 ...
 $ X3b  : int  3 4 1 1 7 5 7 7 0 0 ...
 $ hr   : int  0 0 0 1 3 1 2 0 0 0 ...
 $ rbi  : int  16 29 10 34 23 21 23 50 15 16 ...
 $ sb   : int  6 8 2 4 3 2 2 6 0 2 ...
 $ cs   : int  2 0 1 2 1 2 2 6 1 2 ...
 $ bb   : int  2 4 2 3 1 4 9 16 1 1 ...
 $ so   : int  1 0 0 0 0 1 1 3 1 0 ...
 $ ibb  : int  NA NA NA NA NA NA NA NA NA NA ...
 $ hbp  : int  NA NA NA NA NA NA NA NA NA NA ...
 $ sh   : int  NA NA NA NA NA NA NA NA NA NA ...
 $ sf   : int  NA NA NA NA NA NA NA NA NA NA ...
 $ gidp : int  NA NA NA NA NA NA NA NA NA NA ...
 $ teamf: Factor w/ 132 levels "ALT","ANA","ARI",..: 99 127 51 79 35 35 122 86 16 122 ...

> # idata.frame doesn't work with the doBy package
require(doBy)
summaryBy(rbi ~ year, baseball_i, FUN=c(mean), na.rm=T)
Error in as.vector(x, mode) : 
  cannot coerce type 'environment' to vector of type 'any'

> # But idata.frame works with aggregate in base (but with minimal speed gains)
# and aggregate is faster than ddply of course 
system.time( replicate(100, aggregate(rbi ~ year, baseball, mean) ) )
   user  system elapsed 
  4.117   0.423   4.541 
> system.time( replicate(100, aggregate(rbi ~ year, baseball_i, mean) ) )
   user  system elapsed 
  3.908   0.383   4.291 
> system.time( replicate(100, ddply( baseball_i, "year", summarise, mean(rbi)) ) )
   user  system elapsed 
 14.015   0.048  14.082 

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