On Sat, 2007-09-15 at 09:02 -0700, Gerard Smits wrote:
> Hi All,
>
> There are a variety of functions that can be applied to a variable
> (column) in a data frame: mean, min, max, sd, range, IQR, etc.
But one their own, these are not equivalents to rowMeans, rowSums etc
below.
>
> I am aware of only two that work on the rows, using q1-q3 as example
> variables:
>
> rowMeans(cbind(q1,q2,q3),na.rm=T) #mean of multiple variables
> rowSums (cbind(q1,q2,q3),na.rm=T) #sum of multiple variables
If you really want to apply a function to the individual rows of a
matrix-like object then apply() is your friend:
?rowMeans states:
Details:
These functions are equivalent to use of 'apply' with 'FUN = mean'
or 'FUN = sum' with appropriate margins, but are a lot faster.
So see ?apply and argument 'margin'. For rows use margin = 1, e.g.:
dat <- matrix(runif(1000), ncol = 100)
apply(dat, 1, mean)
rowMeans(dat)
>
> Can the standard column functions (listed in the first sentence) be
> applied to rows, with the use of correct indexes to reference the
> columns of interest? Or, must these summary functions be programmed
> separately to work on a row?
You can only use those functions on a column via subsetting, e.g.:
mean(dat[,4])
min(dat[,4])
If all you want is a single row (the equivalent of what you seem to be
asking) then these also work:
mean(dat[4,])
min(dat[4,])
HTH
G
>
> Thanks,
>
> Gerard
>
>
>
> [[alternative HTML version deleted]]
>
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Gavin Simpson [t] +44 (0)20 7679 0522
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