OOPS! I forgot to tell you that I first changed the "method" column,
which is a factor, to character, with
input.df$method <- as.character(input.df$method)
Then things will work properly.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sun, Mar 27, 2016 at 11:35 AM, Bert Gunter <bgunter.4...@gmail.com> wrote:
> 1. return() is not needed in R functions (it's harmless, however). You
> might wish to go through an R function tutorial (many good ones are on
> the web) to learn about what slick things you can do with functions in
> R.
>
> 2. The following is just a brute force loop, so more elegant
> approaches are likely possible, but this seems to do what you want:
>
> sapply(seq_len(nrow(input.df)),
> function(i)do.call(input.df[i,4],list(x=unlist(input.df[i,1:3])))
> )
>
> ## see ?do.call
>
> 3. I suspect that a change in your data structure might facilitate
> your task, but of course, not knowing the task, I would not know what
> changes would be useful. See ?switch for something that might be
> related to what you are trying to do.
>
> Cheers,
> Bert
>
>
>
>
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
>
> On Sun, Mar 27, 2016 at 8:46 AM, Naresh Gurbuxani
> <naresh_gurbux...@hotmail.com> wrote:
>> I have a data frame with with several columns of parameters and one column
>> of function name which should be used with these parameters. My goal is to
>> call the appropriate function for each row and summarize the results in a
>> list or data frame. I can partition the data frame according to function
>> name, then call each function at a time. Is there a more elegant way to
>> achieve this result with one call only?
>>
>> Below is an example. Here the functions are simple. It is possible to
>> write a function that incorporates both the functions. It is not so easy
>> in my actual task. My goal is to make one call and obtain res.df.
>>
>> Thanks,
>> Naresh
>>
>>
>>
>> sum.sq <- function(x) {return(sum(x^2))}
>>
>> sum.cube <- function(x) {return(sum(x^3))}
>>
>> input.df <- data.frame(x1 = c(1,2,3,4), x2 = c(2,3,4,5), x3 = c(3,4,5,6),
>> method = c(rep("sum.sq", 2), rep("sum.cube", 2)), case = c("a", "b", "c",
>> "d"))
>>
>> library(plyr)
>>
>> res.df1 <- ddply(subset(input.df, method == "sum.sq"), c("case"),
>> function(df) {
>> x.vec <- c(df$x1, df$x2, df$x3)
>> return(data.frame(sum.power = sum.sq(x.vec)))
>> })
>>
>> res.df2 <- ddply(subset(input.df, method == "sum.cube"), c("case"),
>> function(df) {
>> x.vec <- c(df$x1, df$x2, df$x3)
>> return(data.frame(sum.power = sum.cube(x.vec)))
>> })
>>
>> res.df <- rbind(res.df1, res.df2)
>> res.df <- merge(res.df, input.df[,c("method", "case")], by = "case")
>>
>>
>>
>>
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>> and provide commented, minimal, self-contained, reproducible code.
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