[R] data frame vs. matrix

[Jeff Newmiller]

Did you really intend to make all of the x values the same? If so, try one line instead of the for loop:

dfr$x[ 2:n ] <- dfr$x[ 1 ]

If that was merely an error in your example, then you could use a different one-liner:

dfr$x[ 2:n ] <- dfr$x[ seq.int( n-1 ) ]

In either case, the speedup is considerable.

I use data frames far more than matrices and don't feel I am suffering for it, but then I also use creative indexing way more than for loops.

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Sent from my phone. Please excuse my brevity.

On March 16, 2014 11:57:33 AM PDT, "Göran Broström" <goran.brostrom at umu.se> wrote:
>I have always known that "matrices are faster than data frames", for 
>instance this function:
>
>
>dumkoll <- function(n = 1000, df = TRUE){
>     dfr <- data.frame(x = rnorm(n), y = rnorm(n))
>     if (df){
>         for (i in 2:NROW(dfr)){
>             if (!(i %% 100)) cat("i = ", i, "\n")
>             dfr$x[i] <- dfr$x[i-1]
>         }
>     }else{
>         dm <- as.matrix(dfr)
>         for (i in 2:NROW(dm)){
>             if (!(i %% 100)) cat("i = ", i, "\n")
>             dm[i, 1] <- dm[i-1, 1]
>         }
>         dfr$x <- dm[, 1]
>     }
>}
>
>--------------------
> > system.time(dumkoll())
>
>    user  system elapsed
>   0.046   0.000   0.045
>
> > system.time(dumkoll(df = FALSE))
>
>    user  system elapsed
>   0.007   0.000   0.008
>----------------------
>
>OK, no big deal, but I stumbled over a data frame with one million 
>records. Then, with df = TRUE,
>----------------------------
>      user    system   elapsed
>44677.141  1271.544 46016.754
>----------------------------
>This is around 12 hours.
>
>With df = FALSE, it took only six seconds! About 7500 time faster.
>
>I was really surprised by the huge difference, and I wonder if this is 
>to be expected, or if it is some peculiarity with my installation: I'm 
>running Ubuntu 13.10 on a MacBook Pro with 8 Gb memory, R-3.0.3.
>
>Göran B.
>
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