Dan,
Thank you very much for your comments and function definition code!! Responses
like yours make the r-help community amazing!!
Kurt
----------------------------------------
> Date: Sun, 10 Aug 2008 12:08:42 +0100
> From: [EMAIL PROTECTED]
> To: [EMAIL PROTECTED]
> CC: r-help@r-project.org
> Subject: Re: [R] Converting nested "for" loops to an "apply" function(s)
>
> On Sat, Aug 09, 2008 at 08:53:00PM -0400, Kurt Newman wrote:
>>
>> Resending. Previous message was truncated. Sorry for possible confusion.
>>
>> ----------------------------------------
>>> From: [EMAIL PROTECTED]
>>> To: r-help@r-project.org
>>> Date: Sat, 9 Aug 2008 18:25:47 -0400
>>> Subject: [R] Converting nested "for" loops to an "apply" function(s)
>>>
>>>
>>> Hello,
>>>
>
>>> I would like to know more about how to use the "apply" family and
>>> have attempted to convert nested "for" loops in example code from
>>> Contributed Documentation ("The Friendly Beginners' R Course? by
>>> Toby Marthews (ZIP, 2007-03-01)") to an "apply" function(s). The
>>> relevant code is:
>
>>>
>>> distances=c(51,65,175,196,197,125,10,56) #distances of 8 houses from the
>>> town centre in m
>>> bearings=c(10,8,210,25,74,128,235,335) #bearings of the houses in
>>> degrees
>>>
>>> xpos=distances*sin(bearings*pi/180) #in sin and cos the argument
>>> MUST be in radians
>>> ypos=distances*cos(bearings*pi/180)
>>>
>>> numpoints=length(distances)
>>> nnd=rep(sqrt(2*400*400),times=numpoints) #start with the maximum
>>> possible distance
>>> for (i in 1:numpoints) {
>>> for (j in 1:numpoints) {
>>> if (i!=j) {
>>> diffx=abs(xpos[i]-xpos[j])
>>> diffy=abs(ypos[i]-ypos[j])
>>> nd=sqrt((diffx^2)+(diffy^2))
>>> if (nd < nnd[i]) {nnd[i]=nd}
>> }
>> }
>> }
>> print(data.frame(xpos,ypos,nnd))
>>
>
>> My attempts to convert the nested "for" loops to an "apply"
>> function(s) have not been successful. I would like to know how to
>> convert the code to increase my knowledge of R programming and to
>> evaluate operational efficiency of the different strategies.
>
> Hi Kurt,
>
> It's not just the apply() family that help in vectorising problems. In
> this case, outer() is also going to be helpful, as well as remembering
> that all the standard arithmetical operators automatically
> vectorise. I would use something like this:
>
> nearest.neighbour.distance <- function(xpos, ypos) {
> xdist <- abs(outer(xpos, xpos, "-"))
> ydist <- abs(outer(ypos, ypos, "-"))
> dist <- sqrt(xdist^2 + ydist^2)
> diag(dist) <- NA
> apply(dist, 1, min, na.rm=TRUE)
> }
>
> Dan
>
>
>>
>> Thank you in advance for your comments / suggestions.
>>
>> Kurt Newman
>>
>>
>>
>>> ______________________________________________
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>>> 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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