[R] Converting nested "for" loops to an "apply" function(s)

[Kurt Newman]

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: davison at stats.ox.ac.uk
> To: km_newman at hotmail.com
> CC: r-help at 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: km_newman at hotmail.com
>>> To: r-help at 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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>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>> 
>> ______________________________________________
>> R-help at 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.