R help - Jul 2010 - best way to apply a list of functions to a dataset ?

Assuming I have a matrix of data (or under some restrictions that will
become obvious, possibly a data frame), I want to be able to apply a
list of functions (initially producing a single number from a vector)
to the data and produce a data frame (for compact output) with column
1 being the function results for the first function, column 2 being
the results for the second function and so on - with each row being
the columns of the original data.

The obvious application of this is to produce summaries of data sets
(a bit like summary() does on numeric matrices), but with user
supplied functions. I am content for the moment to leave it to the
user to supply functions that work with the data they supply so as to
produce results that will actually be data-frame-able, though I'd like
to ultimately make it a bit nicer than it currently is without
compromising the niceness of the output in the "good" cases.

The example below is a simplistic approach to this problem (it should
run as is). I have named it "fapply" for fairly obvious reasons, but
added the ".1" because it doesn't accept multidimensional arrays.
I
have included the output I generated, which is what I want. There are
some obvious generalizations (e.g. being able to include functions
like range(), say, that produce several values on a vector, rather
than one, making the user's life simpler when a function already does
most of what they need).

The question is: this looks like a silly approach, growing a list
inside a for loop. Also I recall reading that if you find yourself
using "do.call" you should probably be doing something else.

So my question: Is there a better way to implement a function like this?

Or, even better, is there already a function that does this?

## example function and code to apply a list of functions to a matrix
(here a numeric data frame)

library(datasets)

fapply.1 <- function(x, fun.l, colnames=fun.l){
out.l <- list() ? # starts with an empty list
for (i in seq_along(fun.l)) out.l[[i]] <- apply(x,2,fun.l[[i]]) ? #
loop through list of functions

# set up names and make into a data frame
names(out.l) <- colnames
attr(out.l,"row.names") <- names(out.l[[1]])
attr(out.l,"class") <- "data.frame"
out.l
}

skewness <- function(x) mean(scale(x)^3) ? ? ?#define a simple numeric
function
mean.gt.med <- function(x) mean(x)>median(x) ?# define a simple
non-numeric fn
flist <-
c("mean","sd","skewness","median","mean.gt.med")
# make list
of fns to apply

fapply.1(attitude,flist)
? ? ? ? ? ? ? mean ? ? ? ?sd ? ?skewness median mean.gt.med
rating ? ? 64.63333 12.172562 -0.35792491 ? 65.5 ? ? ? FALSE
complaints 66.60000 13.314757 -0.21541749 ? 65.0 ? ? ? ?TRUE
privileges 53.13333 12.235430 ?0.37912287 ? 51.5 ? ? ? ?TRUE
learning ? 56.36667 11.737013 -0.05403354 ? 56.5 ? ? ? FALSE
raises ? ? 64.63333 10.397226 ?0.19754317 ? 63.5 ? ? ? ?TRUE
critical ? 74.76667 ?9.894908 -0.86577893 ? 77.5 ? ? ? FALSE
advance ? ?42.93333 10.288706 ?0.85039799 ? 41.0 ? ? ? ?TRUE

## end code and output

So did I miss something obvious?

Any suggestions as far as style or simple stability-enhancing
improvements would be handy.

regards,
Glen

Erk. Sorry about the wrapping issue on the comments in the code,
which will interfere with a straight copypaste.

Glen

Hi:

This might be a little easier (?):

library(datasets)
skewness <- function(x) mean(scale(x)^3)
mean.gt.med <- function(x) mean(x)>median(x)

# ------
# construct the function to apply to each variable in the data frame
f <- function(x) c(mean = mean(x), sd = sd(x), skewness = skewness(x),
     median = median(x), mean.gt.med = mean.gt.med(x))

# map function to each variable with lapply and combine with do.call():
do.call(rbind, lapply(attitude, f))
               mean        sd    skewness median mean.gt.med
rating     64.63333 12.172562 -0.35792491   65.5           0
complaints 66.60000 13.314757 -0.21541749   65.0           1
privileges 53.13333 12.235430  0.37912287   51.5           1
learning   56.36667 11.737013 -0.05403354   56.5           0
raises     64.63333 10.397226  0.19754317   63.5           1
critical   74.76667  9.894908 -0.86577893   77.5           0
advance    42.93333 10.288706  0.85039799   41.0           1

HTH,
Dennis


On Mon, Jul 19, 2010 at 10:51 PM, Glen Barnett <glnbrntt@gmail.com> wrote:
> Assuming I have a matrix of data (or under some restrictions that will
> become obvious, possibly a data frame), I want to be able to apply a
> list of functions (initially producing a single number from a vector)
> to the data and produce a data frame (for compact output) with column
> 1 being the function results for the first function, column 2 being
> the results for the second function and so on - with each row being
> the columns of the original data.
>
> The obvious application of this is to produce summaries of data sets
> (a bit like summary() does on numeric matrices), but with user
> supplied functions. I am content for the moment to leave it to the
> user to supply functions that work with the data they supply so as to
> produce results that will actually be data-frame-able, though I'd like
> to ultimately make it a bit nicer than it currently is without
> compromising the niceness of the output in the "good" cases.
>
> The example below is a simplistic approach to this problem (it should
> run as is). I have named it "fapply" for fairly obvious reasons,
but
> added the ".1" because it doesn't accept multidimensional
arrays. I
> have included the output I generated, which is what I want. There are
> some obvious generalizations (e.g. being able to include functions
> like range(), say, that produce several values on a vector, rather
> than one, making the user's life simpler when a function already does
> most of what they need).
>
> The question is: this looks like a silly approach, growing a list
> inside a for loop. Also I recall reading that if you find yourself
> using "do.call" you should probably be doing something else.
>
> So my question: Is there a better way to implement a function like this?
>
> Or, even better, is there already a function that does this?
>
> ## example function and code to apply a list of functions to a matrix
> (here a numeric data frame)
>
> library(datasets)
>
> fapply.1 <- function(x, fun.l, colnames=fun.l){
> out.l <- list()   # starts with an empty list
> for (i in seq_along(fun.l)) out.l[[i]] <- apply(x,2,fun.l[[i]])   #
> loop through list of functions
>
> # set up names and make into a data frame
> names(out.l) <- colnames
> attr(out.l,"row.names") <- names(out.l[[1]])
> attr(out.l,"class") <- "data.frame"
> out.l
> }
>
> skewness <- function(x) mean(scale(x)^3)      #define a simple numeric
> function
> mean.gt.med <- function(x) mean(x)>median(x)  # define a simple
non-numeric
> fn
> flist <-
c("mean","sd","skewness","median","mean.gt.med")
# make list
> of fns to apply
>
> fapply.1(attitude,flist)
>               mean        sd    skewness median mean.gt.med
> rating     64.63333 12.172562 -0.35792491   65.5       FALSE
> complaints 66.60000 13.314757 -0.21541749   65.0        TRUE
> privileges 53.13333 12.235430  0.37912287   51.5        TRUE
> learning   56.36667 11.737013 -0.05403354   56.5       FALSE
> raises     64.63333 10.397226  0.19754317   63.5        TRUE
> critical   74.76667  9.894908 -0.86577893   77.5       FALSE
> advance    42.93333 10.288706  0.85039799   41.0        TRUE
>
> ## end code and output
>
> So did I miss something obvious?
>
> Any suggestions as far as style or simple stability-enhancing
> improvements would be handy.
>
> regards,
> Glen
>
> ______________________________________________
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