On Feb 27, 2010, at 9:49 PM, Noah Silverman wrote:
I'm a bit confused on how to use lapply with a data.frame.
For example.
lapply(data, function(x) print(x))
WHAT exactly is passed to the function. Is it each ROW in the data
frame,
No.
one by one, or each column,
Yes. Dataframes are lists of columns.
or the entire frame in one shot?
What I want to do apply a function to each row in the data frame.
Is lapply the right way.
No. Use apply(dtfrm, 1, ......)
A second application is to normalize a column value by group.
Which is, as you suggested, a different problem for which apply()
would not be particularly useful because you have a group. Hence
tapply or one of its variants, aggregate() or by() would be used:
For your example, I am guessing that:
tapply(dfrm$value, dtrm$group, sum)
... might be more economical (at least in single core practice.)
--
David
For example, if I have the following table:
id group value norm
1 A 3.2
2 A 3.0
3 A 3.1
4 B 5.5
5 B 6.0
6 B 6.2
I could not quite figure out how that might have been printed on a
console, since there are more variable names than columns????
etc...
Yes. I do think there is more than you are revealing.
The long version would be:
foreach is not a base function:
foreach (group in unique(data$group)){
data$norm[group==group] <- data$value[group==group] / sum(data
$value[group==group])
}
There must be a faster way to do this with lapply. (Ideally, I'd
then use mclapply to run on multi-cores and really crank up the
speed.)
Learn your basics first. libraries or packages need to be specified.
Any suggestions?
______________________________________________
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.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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.
