> I understand that such for loops aren't 'best practice' in R and am
> trying to learn its approach.
sapply() is an encapsulated loop and loops have their place in R.
'Best practice' is a nebulous term, but explicit loops can make
code that is hard to understand (by a compiler or by a human)
and any loop at the R-code level will generally make code run
more slowly. However, depending on your background, explicit
loops may be easier for you to write and understand, so you
may get an answer faster by using loops.
> >Then transform it to use things
> > like ifelse() and sapply() to make it more readable and run faster.
Changing your 'if' statements to calls to the vectorized 'ifelse' will
probably make looping unneeded. E.g., your q1.ans() only works
on a scalar, forcing you to use sapply (or the superior vapply) to
work on vectors:
q1.ans <- function(x)
{
retVal = 0
if (x == 1) {
retVal = 1
} else if (x ==2) {
retVal = 2
}
return (retVal)
}
as in
> q1.ans(1:3)
[1] 1
Warning message:
In if (x == 1) { :
the condition has length > 1 and only the first element will be used
> sapply(1:3, q1.ans)
[1] 1 2 0
You can change it to work on a vector by using ifelse:
q1a.ans <- function(x) {
ifelse(x==1,
1, # return 1's where x had 1's
ifelse(x==2,
2, # return 2's where x had 2's
0)) # return 0 where x had something else
}
used as
> q1a.ans(1:3)
[1] 1 2 0
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
> -----Original Message-----
> From: Walter Anderson [mailto:wandrso...@gmail.com]
> Sent: Friday, December 06, 2013 9:58 AM
> To: William Dunlap; r-help@r-project.org
> Subject: Re: [R] Need help figuring out sapply (and similar functions) with
> multiple
> parameter user defined function
>
> On 12/06/2013 10:43 AM, William Dunlap wrote:
> >> I have been researching and it appears that I should be using the sapply
> >> function to apply the evaluate.question function above to each row in
> >> the data frame like this
> > Read the documentation more closely: sapply(dataFrame, func)
> > applies func() to each column, not row, of dataFrame.
> I misunderstood. I thought it was apply the func to each row... My mistake
> >> preferences <- sapply(df, evaluate.questions, function(x,y,z)
> >> evaluate.questions(df['Q1'],df['Q2'],df['Q3']))
> > Furthermore,
> > sapply(X = dataFrame, FUN = func, extraArgument)
> > calls
> > func(dataFrame[, i], extraArgument)
> > for i in seq_len(ncol(dataFrame).
> >
> > One problem is that FUN=evaluate.questions takes 3 arguments and
> > you give it only 2. Another problem is that the third argument you
> > pass to sapply is a function (of 3 arguments) and FUN is not expecting
> > any of its arguments to be functions.
> I will need to think about this, I am not sure I understand. I really
> don't seem to understand how any of the apply functions seem to work.
> > It may be easier for you to not use sapply here, but to use for-loops and
> > come up with something that works. (Write tests that will indicate whether
> > it works or not in a variety of situations.) Then transform it to use
> > things
> > like ifelse() and sapply() to make it more readable and run faster.
> I already have tested my functions by using a for loop, and they work.
> Here is the for loop I use.
>
> for (indx in 1:length(df$ID)) {
> df$Preference <-
> evaluate.questions(df$Q1[indx],df$Q2[indx],df$Q3[indx])
> }
>
> I understand that such for loops aren't 'best practice' in R and am
> trying to learn its approach. Thank you for the suggestions!
> >> Unfortunately this doesn't work and the problem appears that the sapply
> >> function is not feeding the parameters to the evaluate.questions
> >> function as I expect. Can someone provide some guidance on what I am
> >> doing wrong?
> > Bill Dunlap
> > Spotfire, TIBCO Software
> > wdunlap tibco.com
> >
> >
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