"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
