Hi:
On Tue, Jul 20, 2010 at 5:37 PM, Glen Barnett <glnbr...@gmail.com> wrote:
> Hi Dennis,
>
> Thanks for the reply.
>
> Yes, that's easier, but the conversion to a matrix with rbind has
> converted the output of that final function to a numeric.
>
If you look at the output of lapply(attitude, f), you'll see that the
conversion from logical to numeric has already taken place. Different
components of lists can have different types, but within a component, all of
the elements must have the same class.
You can patch up the result as follows:
x <- data.frame(do.call(rbind, lapply(attitude, f)))
x[, 5] <- as.logical(x[, 5])
> x
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
but if you're doing this sort of thing over a large data frame such fixes
may be impractical.
I included that last function in the example secifically to preclude
> people assuming that functions would always return the same type.
>
There is a plyr solution, although it's a little more long-winded than I'd
prefer in the end:
library(ggplot2)
# melt the data frame so that the variables become factor levels
ma <- melt(attitude)
Using as id variables
> dim(ma)
[1] 210 2
# Use ddply to get the set of summaries by variable:
ddply(ma, .(variable), summarise, mean = mean(value), sd = sd(value),
skewness = skewness(value), median = median(value),
mean.gt.med = mean.gt.med(value))
variable mean sd skewness median mean.gt.med
1 rating 64.63333 12.172562 -0.35792491 65.5 FALSE
2 complaints 66.60000 13.314757 -0.21541749 65.0 TRUE
3 privileges 53.13333 12.235430 0.37912287 51.5 TRUE
4 learning 56.36667 11.737013 -0.05403354 56.5 FALSE
5 raises 64.63333 10.397226 0.19754317 63.5 TRUE
6 critical 74.76667 9.894908 -0.86577893 77.5 FALSE
7 advance 42.93333 10.288706 0.85039799 41.0 TRUE
Notice that now the logical class of mean.gt.med is preserved. The trick
with ddply() in package plyr is that, in this case, it is convenient to melt
the data frame first before doing the summarizations. This is because
ddply() requires a grouping variable - in this example, the groups are the
variables themselves.
HTH,
Dennis
I guess this doesn't matter too much for a logical, but what if
> instead the function returned a character (say "mean", "median", or
> "equal" - indicating which one was larger, or "equal" which could
> easily happen with discrete data). This precludes using rbind (which I
> also used at first, before I noticed that sometimes I could have
> functions that don't return numerics).
>
> Glen
>
>
> On Tue, Jul 20, 2010 at 6:55 PM, Dennis Murphy <djmu...@gmail.com> wrote:
> > 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 <glnbr...@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
> >>
> >> ______________________________________________
> >> R-help@r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >
> >
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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