subsetting to exclude different values for each subject in study

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subsetting to exclude different values for each subject in study

Monaly Mistry
Hi,

I've written a code to determine the difference in score for a single
subject and its non-neighbours

o<-(ao[,c(13,5)]) ##this is the table with the relevant information
o<-na.omit(o)  ##omitted data with NA
o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed neighbours
XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference between that
individual and average non-neighbours scores

Since each subject has a different number of non-neighbours I was wondering
if there is an efficient way of writing the code, instead of writing the
same code again and again (76 subjects) for each subject and its
non-neighbours.


Best,

Monaly.

        [[alternative HTML version deleted]]

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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
Hi,

Sorry I'm fairly new to R and I don't really understand using dput(), when
you say reproducible example do you mean the code with the output?

Best,

Monaly.


On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]> wrote:

> Hi,
>
> It would be helpful if you provide a reproducible example using ?dput().
>
> A.K.
>
>
>
>
> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry <[hidden email]>
> wrote:
> Hi,
>
> I've written a code to determine the difference in score for a single
> subject and its non-neighbours
>
> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
> o<-na.omit(o)  ##omitted data with NA
> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed neighbours
> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference between that
> individual and average non-neighbours scores
>
> Since each subject has a different number of non-neighbours I was wondering
> if there is an efficient way of writing the code, instead of writing the
> same code again and again (76 subjects) for each subject and its
> non-neighbours.
>
>
> Best,
>
> Monaly.
>
>     [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] 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.
>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] 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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Re: subsetting to exclude different values for each subject in study

Bert Gunter
Follow the link at the bottom of this message!

-- Bert

Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
H. Gilbert Welch




On Thu, May 22, 2014 at 8:31 AM, Monaly Mistry <[hidden email]> wrote:

> Hi,
>
> Sorry I'm fairly new to R and I don't really understand using dput(), when
> you say reproducible example do you mean the code with the output?
>
> Best,
>
> Monaly.
>
>
> On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]> wrote:
>
>> Hi,
>>
>> It would be helpful if you provide a reproducible example using ?dput().
>>
>> A.K.
>>
>>
>>
>>
>> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry <[hidden email]>
>> wrote:
>> Hi,
>>
>> I've written a code to determine the difference in score for a single
>> subject and its non-neighbours
>>
>> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
>> o<-na.omit(o)  ##omitted data with NA
>> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed neighbours
>> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference between that
>> individual and average non-neighbours scores
>>
>> Since each subject has a different number of non-neighbours I was wondering
>> if there is an efficient way of writing the code, instead of writing the
>> same code again and again (76 subjects) for each subject and its
>> non-neighbours.
>>
>>
>> Best,
>>
>> Monaly.
>>
>>     [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] 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.
>>
>>
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] 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.

______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
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and provide commented, minimal, self-contained, reproducible code.
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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
In reply to this post by Monaly Mistry
Hi Everyone,

I hope I did this correctly (I called my data frame ao) and Thank you very
much for the info about using dput(), I'm starting to understand all the
different things that can be done in R and I appreciate all the advice.  I
must appologize in advance since my coding is quite long but hopefully it
makes sense. and there is a efficient way to do this.

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1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan",
"year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
"ManipulatieOuders",
"LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
"NestkastNummer",
"lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names = c(NA,
-99L))


#Below is the code I made to run my analyses
XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to store my
results in
names(ao)
ao$NestkastNummer
b<-c(77:99)
abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
"adj_pop_avg", "ind_pop_dif")
colnames(XO) = c((abo))
ncol(XO)
names(ao)
t <- ao$COR_LOC;t
i <- c(77:99)
ti <- t[-i];ti
XO[1,] = c(ti);XO  #assigned values from data frame to the matrix

### average difference b/n neighbours for each individual
XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
XO["avg", "124"]<-
mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
XO["avg", "717"]<- mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
XO["avg", "42"]<-
mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
XO["avg", "90"]<- mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
XO["avg", "9"]<- mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
XO["avg", "713"]<- mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))
XO["avg", "82"]<- mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
XO["avg", "709"]<-
mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
XO["avg", "39"]<-
mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
XO["avg", "86"]<-
mean(abs((XO[1,"86"])-XO[1,c("38","39","81","82","84","88","709","36")]))
XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
XO["avg", "710"]<-
mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
XO["avg", "93"]<-
mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
XO["avg", "94"]<- mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
XO["avg", "163"]<- mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
XO["avg", "170"]<- mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))
XO["avg", "718"]<-
mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
XO["avg", "79"]<-
mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
XO["avg", "130"]<-
mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
XO["avg", "133"]<-
mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
XO["avg", "25"]<- mean(abs((XO[1,"25"])-XO[1,c("8","9","42","80","39")]))
XO["avg", "128"]<- mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
XO["avg", "164"]<-
mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
XO["avg", "162"]<- mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
XO["avg", "172"]<- mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))
XO["avg", "91"]<-
mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
XO["avg", "31"]<- mean(abs((XO[1,"31"])-XO[1,c("2","3","36","35","34")]))
XO["avg", "73"]<- mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
XO["avg", "97"]<-
mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
XO["avg", "111"]<-
mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
XO["avg", "64"]<- mean(abs((XO[1,"64"])-XO[1,c("113","62","128","124")]))
XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
XO["avg", "95"]<- mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
XO["avg", "704"]<- mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
XO["avg", "36"]<-
mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
XO["avg", "80"]<- mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
XO["avg", "68"]<-
mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
XO["avg", "105"]<- mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
XO["avg", "88"]<-
mean(abs((XO[1,"88"])-XO[1,c("86","84","90","109","105","716","710","709")]))
XO["avg", "81"]<-
mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
XO["avg", "140"]<- mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
XO["avg", "169"]<- mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
XO["avg", "109"]<-
mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
XO["avg", "719"]<- mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))
XO["avg", "35"]<-
mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
XO["avg", "185"]<-
mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
XO["avg", "34"]<- mean(abs((XO[1,"34"])-XO[1,c("31","35","707","717")]))
XO["avg", "707"]<-
mean(abs((XO[1,"707"])-XO[1,c("34","35","36","709","718","717","704")]))
XO["avg", "101"]<-
mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
XO["avg", "38"]<- mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
XO["avg", "28"]<- mean(abs((XO[1,"28"])-XO[1,c("6","39","38","36","3")]))
XO["avg", "84"]<- mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
XO["avg", "113"]<-
mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
XO["avg", "168"]<- mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
XO["avg", "23"]<- mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
XO["avg", "3"]<- mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
XO["avg", "117"]<-
mean(abs((XO[1,"117"])-XO[1,c("101","113","124","130","133","140","68")]))
XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
XO["pop_size",] <- 76
XO["pop_avg_score",]<- mean(XO["EB_score",])
for (i in XO){
  XO["adj_pop_avg",] <-
((XO["pop_avg_score",])*(XO["pop_size",])-(XO["EB_score",]))/((XO["pop_size",]-1))
  #here I ran a loop to get info
  XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
XO
XO<-rbind(XO,0)
rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score", "adj_pop_avg",
"ind_pop_dif", "non_nei")
XO["non_nei",]<-0
rowMeans(XO[,1:76])

#This is the average observed discrepancy from individuals to neighbours
#IOW on average how different is a focal bird in this year different from
its neighbours
obso=mean(XO["avg",])
print(paste("Observed=", obso))
XY[15,1]<-round(obso, digits=4)


#This is the code I previously posted to find the difference in scores
between a single subject and its non-neighbours
o<-(ao[,c(13,5)])
o<-na.omit(o)
o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))


Best,

Monaly.


On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]> wrote:

> Re dput() etc
> https://github.com/hadley/devtools/wiki/Reproducibility
>
> http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
>
> What dput() does is take your data and ouput it in an ascii format that
> let's the reader here create an exact duplicate of your database.
>
> R is not WYSIWYG. Often what you see on the screen does not tell the whole
> tale. R supports a number of different data types: vectors, matrices,
> data.frames, lists, arrays and others. This site gives a useful though not
> complete summary of many data types
> http://www.statmethods.net/input/datatypes.html. When you have just
> created a new data set, or even when working with one that you have not
> worked with in some time it is a good idea to do a str() and class() on the
> data object just to be sure that you are working with the data types you
> think you have. What looks like a column of numbers in a data.frame may
> actually be a set of factors or a set of character (text) data and you're
> left wondering why multiplying it by some number is not working.
>
> Here is a short example to illustrate. Just copy and paste in the code
>  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
> dat1 # data looks identical on the screen
> 5*dat1[,"aa"]  # oops
> 5*dat1[, "bb"] # okay
> str(dat1)
>
>
> John Kane
> Kingston ON Canada
>
>
> > -----Original Message-----
> > From: [hidden email]
> > Sent: Thu, 22 May 2014 16:31:39 +0100
> > To: [hidden email], [hidden email]
> > Subject: Re: [R] subsetting to exclude different values for each subject
> > in study
> >
> > Hi,
> >
> > Sorry I'm fairly new to R and I don't really understand using dput(),
> > when
> > you say reproducible example do you mean the code with the output?
> >
> > Best,
> >
> > Monaly.
> >
> >
> > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]> wrote:
> >
> >> Hi,
> >>
> >> It would be helpful if you provide a reproducible example using ?dput().
> >>
> >> A.K.
> >>
> >>
> >>
> >>
> >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
> >> <[hidden email]>
> >> wrote:
> >> Hi,
> >>
> >> I've written a code to determine the difference in score for a single
> >> subject and its non-neighbours
> >>
> >> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
> >> o<-na.omit(o)  ##omitted data with NA
> >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed neighbours
> >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference between
> >> that
> >> individual and average non-neighbours scores
> >>
> >> Since each subject has a different number of non-neighbours I was
> >> wondering
> >> if there is an efficient way of writing the code, instead of writing the
> >> same code again and again (76 subjects) for each subject and its
> >> non-neighbours.
> >>
> >>
> >> Best,
> >>
> >> Monaly.
> >>
> >>     [[alternative HTML version deleted]]
> >>
> >> ______________________________________________
> >> [hidden email] 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.
> >>
> >>
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] 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.
>
> ____________________________________________________________
> FREE ONLINE PHOTOSHARING - Share your photos online with your friends and
> family!
> Visit http://www.inbox.com/photosharing to find out more!
>
>
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] 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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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
Hi,

Neighbours in this case were selected if they shared a boundary in the
voroni tesellation.

Best,
Monaly
On May 23, 2014 3:19 AM, "arun" <[hidden email]> wrote:
>
>
>
> HI Monaly,
> Thanks for the code and dput.  But, I have a doubt about how you are
selecting the neigbours.  Is there another dataset with the information?
Sorry, if I have missed something
> For e.g.
> ### average difference b/n neighbours for each individual
> XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
>
>
> A.K.
>
>
> On Thursday, May 22, 2014 5:21 PM, Monaly Mistry <[hidden email]>
wrote:

> Hi Everyone,
>
> I hope I did this correctly (I called my data frame ao) and Thank you very
> much for the info about using dput(), I'm starting to understand all the
> different things that can be done in R and I appreciate all the advice.  I
> must appologize in advance since my coding is quite long but hopefully it
> makes sense. and there is a efficient way to do this.
>
> structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
> 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
> 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
> 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
> 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
> 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
> 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
> 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
> 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
> 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
> 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L,
> 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
> 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
> 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
> 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
> 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
> 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
> 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
> "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
> "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
> "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
> "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
> "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
> "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
> "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
> "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
> "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
> "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
> "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
> "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
> "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
> "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
> "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
> "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
> "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
> "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
> "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
> "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
c(2006L,

> 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
> 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
> 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
> 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
> 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
> 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
> 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
> 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
> 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404,
> 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
> 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
> 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
> 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
> 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
> 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
> 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
> 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
> 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
> 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
> 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
> 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
> 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
> 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
> 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
> 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
> 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
> 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
> 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
> 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
> 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
> 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
> 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
> 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
> 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
c(0L,

> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
> 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
> 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
> 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
> 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
> 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
> 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
> 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
> 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
> 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
> 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
> 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
> 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
> 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
> 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
> 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
> 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
> 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
> 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
> 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
> 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
> 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
> 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
> 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
> 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
> 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
> 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
> 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
> 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
> 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
> 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
> 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
> 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
> 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
> 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
> 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
> 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
> 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
> 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
> 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
> 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
> 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
> c(729.2669944,
> 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
> 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
> 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
> 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
> 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
> 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
> 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
> 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
> 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
> 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
> 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
> 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
> 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
> 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
> 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
> 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
> 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
> 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
> 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
> 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
> 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
> 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
> 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
> 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
> 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
> 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
> 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
> 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
> 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
> 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
> 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
> 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
> 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
> 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
> 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
> 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
> 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
> 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
> 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
> 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan",
> "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
> "ManipulatieOuders",
> "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
> "NestkastNummer",
> "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
c(NA,

> -99L))
>
>
> #Below is the code I made to run my analyses
> XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to store my
> results in
> names(ao)
> ao$NestkastNummer
> b<-c(77:99)
> abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
> rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
> "adj_pop_avg", "ind_pop_dif")
> colnames(XO) = c((abo))
> ncol(XO)
> names(ao)
> t <- ao$COR_LOC;t
> i <- c(77:99)
> ti <- t[-i];ti
> XO[1,] = c(ti);XO  #assigned values from data frame to the matrix
>
> ### average difference b/n neighbours for each individual
> XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> XO["avg", "124"]<-
> mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
> XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
> XO["avg", "717"]<-
mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
> XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
> XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
> XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
> XO["avg", "42"]<-
> mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
> XO["avg", "90"]<-
mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
> XO["avg", "9"]<- mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
> XO["avg", "713"]<-
mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))

> XO["avg", "82"]<- mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
> XO["avg", "709"]<-
> mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
> XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
> XO["avg", "39"]<-
> mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
> XO["avg", "86"]<-
> mean(abs((XO[1,"86"])-XO[1,c("38","39","81","82","84","88","709","36")]))
> XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
> XO["avg", "710"]<-
> mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
> XO["avg", "93"]<-
> mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
> XO["avg", "94"]<-
mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
> XO["avg", "163"]<-
mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
> XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
> XO["avg", "170"]<-
mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))

> XO["avg", "718"]<-
> mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
> XO["avg", "79"]<-
> mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
> XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
> XO["avg", "130"]<-
> mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
> XO["avg", "133"]<-
> mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
> XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
> XO["avg", "25"]<- mean(abs((XO[1,"25"])-XO[1,c("8","9","42","80","39")]))
> XO["avg", "128"]<-
mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
> XO["avg", "164"]<-
> mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
> XO["avg", "162"]<-
mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
> XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
> XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
> XO["avg", "172"]<-
mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))

> XO["avg", "91"]<-
> mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
> XO["avg", "31"]<- mean(abs((XO[1,"31"])-XO[1,c("2","3","36","35","34")]))
> XO["avg", "73"]<- mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
> XO["avg", "97"]<-
> mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
> XO["avg", "111"]<-
> mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
> XO["avg", "64"]<- mean(abs((XO[1,"64"])-XO[1,c("113","62","128","124")]))
> XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
> XO["avg", "95"]<- mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
> XO["avg", "704"]<-
mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
> XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
> XO["avg", "36"]<-
> mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
> XO["avg", "80"]<-
mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
> XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
> XO["avg", "68"]<-
> mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
> XO["avg", "105"]<-
mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
> XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
> XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
> XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
> XO["avg", "88"]<-
>
mean(abs((XO[1,"88"])-XO[1,c("86","84","90","109","105","716","710","709")]))
> XO["avg", "81"]<-
> mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
> XO["avg", "140"]<-
mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
> XO["avg", "169"]<- mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
> XO["avg", "109"]<-
> mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
> XO["avg", "719"]<-
mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))

> XO["avg", "35"]<-
> mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
> XO["avg", "185"]<-
> mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
> XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
> XO["avg", "34"]<- mean(abs((XO[1,"34"])-XO[1,c("31","35","707","717")]))
> XO["avg", "707"]<-
> mean(abs((XO[1,"707"])-XO[1,c("34","35","36","709","718","717","704")]))
> XO["avg", "101"]<-
> mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
> XO["avg", "38"]<- mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
> XO["avg", "28"]<- mean(abs((XO[1,"28"])-XO[1,c("6","39","38","36","3")]))
> XO["avg", "84"]<-
mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
> XO["avg", "113"]<-
> mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
> XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
> XO["avg", "168"]<-
mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
> XO["avg", "23"]<- mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
> XO["avg", "3"]<-
mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
> XO["avg", "117"]<-
> mean(abs((XO[1,"117"])-XO[1,c("101","113","124","130","133","140","68")]))
> XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
> XO["pop_size",] <- 76
> XO["pop_avg_score",]<- mean(XO["EB_score",])
> for (i in XO){
>   XO["adj_pop_avg",] <-
>
((XO["pop_avg_score",])*(XO["pop_size",])-(XO["EB_score",]))/((XO["pop_size",]-1))
>   #here I ran a loop to get info
>   XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
> t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
> XO
> XO<-rbind(XO,0)
> rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score",
"adj_pop_avg",

> "ind_pop_dif", "non_nei")
> XO["non_nei",]<-0
> rowMeans(XO[,1:76])
>
> #This is the average observed discrepancy from individuals to neighbours
> #IOW on average how different is a focal bird in this year different from
> its neighbours
> obso=mean(XO["avg",])
> print(paste("Observed=", obso))
> XY[15,1]<-round(obso, digits=4)
>
>
> #This is the code I previously posted to find the difference in scores
> between a single subject and its non-neighbours
> o<-(ao[,c(13,5)])
> o<-na.omit(o)
> o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))
>
>
> Best,
>
> Monaly.
>
>
> On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]> wrote:
>
> > Re dput() etc
> > https://github.com/hadley/devtools/wiki/Reproducibility
> >
> >
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
> >
> > What dput() does is take your data and ouput it in an ascii format that
> > let's the reader here create an exact duplicate of your database.
> >
> > R is not WYSIWYG. Often what you see on the screen does not tell the
whole
> > tale. R supports a number of different data types: vectors, matrices,
> > data.frames, lists, arrays and others. This site gives a useful though
not
> > complete summary of many data types
> > http://www.statmethods.net/input/datatypes.html. When you have just
> > created a new data set, or even when working with one that you have not
> > worked with in some time it is a good idea to do a str() and class() on
the
> > data object just to be sure that you are working with the data types you
> > think you have. What looks like a column of numbers in a data.frame may
> > actually be a set of factors or a set of character (text) data and
you're

> > left wondering why multiplying it by some number is not working.
> >
> > Here is a short example to illustrate. Just copy and paste in the code
> >  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
> > dat1 # data looks identical on the screen
> > 5*dat1[,"aa"]  # oops
> > 5*dat1[, "bb"] # okay
> > str(dat1)
> >
> >
> > John Kane
> > Kingston ON Canada
> >
> >
> > > -----Original Message-----
> > > From: [hidden email]
> > > Sent: Thu, 22 May 2014 16:31:39 +0100
> > > To: [hidden email], [hidden email]
> > > Subject: Re: [R] subsetting to exclude different values for each
subject

> > > in study
> > >
> > > Hi,
> > >
> > > Sorry I'm fairly new to R and I don't really understand using dput(),
> > > when
> > > you say reproducible example do you mean the code with the output?
> > >
> > > Best,
> > >
> > > Monaly.
> > >
> > >
> > > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]> wrote:
> > >
> > >> Hi,
> > >>
> > >> It would be helpful if you provide a reproducible example using
?dput().

> > >>
> > >> A.K.
> > >>
> > >>
> > >>
> > >>
> > >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
> > >> <[hidden email]>
> > >> wrote:
> > >> Hi,
> > >>
> > >> I've written a code to determine the difference in score for a single
> > >> subject and its non-neighbours
> > >>
> > >> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
> > >> o<-na.omit(o)  ##omitted data with NA
> > >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed neighbours
> > >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference
between
> > >> that
> > >> individual and average non-neighbours scores
> > >>
> > >> Since each subject has a different number of non-neighbours I was
> > >> wondering
> > >> if there is an efficient way of writing the code, instead of writing
the

> > >> same code again and again (76 subjects) for each subject and its
> > >> non-neighbours.
> > >>
> > >>
> > >> Best,
> > >>
> > >> Monaly.
> > >>
> > >>     [[alternative HTML version deleted]]
> > >>
> > >> ______________________________________________
> > >> [hidden email] 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.
> > >>
> > >>
> > >
> > >       [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > [hidden email] 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.
> >
> > ____________________________________________________________
> > FREE ONLINE PHOTOSHARING - Share your photos online with your friends
and

> > family!
> > Visit http://www.inbox.com/photosharing to find out more!
> >
> >
> >
>
>     [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] 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.

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] 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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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
Hi,

I did use the library deldir, I didn't put that code in since I  wasn't
sure if it was really relevant to the question as I just made the
tesselations identifying which tessellation belonged to which individual.
Following that I by hand recorded which individuals were sharing a boundary
with each other.

Best,

Monaly.


On Fri, May 23, 2014 at 11:25 AM, arun <[hidden email]> wrote:

> Hi,
>
> I am not sure how you did that.  May be using library(deldir).  I didn't
> find that codes in your previous email.
>
> A.K.
>
> On Friday, May 23, 2014 12:42 AM, Monaly Mistry <[hidden email]>
> wrote:
>
>
>
> Hi,
> Neighbours in this case were selected if they shared a boundary in the
> voroni tesellation.
>
> Best,
> Monaly
> On May 23, 2014 3:19 AM, "arun" <[hidden email]> wrote:
> >
> >
> >
> > HI Monaly,
> > Thanks for the code and dput.  But, I have a doubt about how you are
> selecting the neigbours.  Is there another dataset with the information?
> Sorry, if I have missed something
> > For e.g.
> > ### average difference b/n neighbours for each individual
> > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> >
> >
> > A.K.
> >
> >
> > On Thursday, May 22, 2014 5:21 PM, Monaly Mistry <
> [hidden email]> wrote:
> > Hi Everyone,
> >
> > I hope I did this correctly (I called my data frame ao) and Thank you
> very
> > much for the info about using dput(), I'm starting to understand all the
> > different things that can be done in R and I appreciate all the advice.
> I
> > must appologize in advance since my coding is quite long but hopefully it
> > makes sense. and there is a efficient way to do this.
> >
> > structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
> > 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
> > 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
> > 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
> > 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
> > 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
> > 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
> > 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
> > 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
> > 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
> > 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L,
> > 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> > 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
> > 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
> > 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
> > 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
> > 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
> > 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
> > 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
> > "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
> > "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
> > "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
> > "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
> > "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
> > "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
> > "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
> > "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
> > "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
> > "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
> > "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
> > "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
> > "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
> > "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
> > "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
> > "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
> > "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
> > "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
> > "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
> > "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
> c(2006L,
> > 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
> > 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
> > 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
> > 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
> > 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
> > 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
> > 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
> > 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
> > 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC =
> c(15.13404,
> > 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
> > 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
> > 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
> > 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
> > 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
> > 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
> > 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
> > 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
> > 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
> > 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
> > 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
> > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
> > 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
> > 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
> > 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
> > 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
> > 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
> > 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
> > 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
> > 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
> > 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
> > 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
> > 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
> > 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
> > 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
> > 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
> c(0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
> > 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
> > 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
> > 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
> > 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
> > 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
> > 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
> > 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
> > 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> > 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
> > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
> > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
> > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
> > 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
> > 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
> > 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
> > 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
> > 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
> > 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
> > 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
> > 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
> > 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
> > 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
> > 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
> > 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
> > 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
> > 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
> > 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
> > 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
> > 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
> > 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
> > 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
> > 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
> > 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
> > 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
> > 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
> > 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
> > 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
> > 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
> > 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
> > 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
> > 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
> > 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
> > 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
> > 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
> > 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
> > c(729.2669944,
> > 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
> > 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
> > 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
> > 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
> > 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
> > 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
> > 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
> > 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
> > 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
> > 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
> > 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
> > 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
> > 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
> > 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
> > 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
> > 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
> > 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
> > 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
> > 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
> > 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
> > 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
> > 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
> > 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
> > 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
> > 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
> > 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
> > 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
> > 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
> > 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
> > 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
> > 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
> > 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
> > 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
> > 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
> > 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
> > 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
> > 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
> > 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
> > 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
> > 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK",
> "RingNummerMan",
> > "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
> > "ManipulatieOuders",
> > "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
> > "NestkastNummer",
> > "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
> c(NA,
> > -99L))
> >
> >
> > #Below is the code I made to run my analyses
> > XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to store my
> > results in
> > names(ao)
> > ao$NestkastNummer
> > b<-c(77:99)
> > abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
> > rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
> > "adj_pop_avg", "ind_pop_dif")
> > colnames(XO) = c((abo))
> > ncol(XO)
> > names(ao)
> > t <- ao$COR_LOC;t
> > i <- c(77:99)
> > ti <- t[-i];ti
> > XO[1,] = c(ti);XO  #assigned values from data frame to the matrix
> >
> > ### average difference b/n neighbours for each individual
> > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> > XO["avg", "124"]<-
> > mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
> > XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
> > XO["avg", "717"]<-
> mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
> > XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
> > XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
> > XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
> > XO["avg", "42"]<-
> > mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
> > XO["avg", "90"]<-
> mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
> > XO["avg", "9"]<- mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
> > XO["avg", "713"]<-
> mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))
> > XO["avg", "82"]<- mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
> > XO["avg", "709"]<-
> > mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
> > XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
> > XO["avg", "39"]<-
> > mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
> > XO["avg", "86"]<-
> > mean(abs((XO[1,"86"])-XO[1,c("38","39","81","82","84","88","709","36")]))
> > XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
> > XO["avg", "710"]<-
> > mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
> > XO["avg", "93"]<-
> > mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
> > XO["avg", "94"]<-
> mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
> > XO["avg", "163"]<-
> mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
> > XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
> > XO["avg", "170"]<-
> mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))
> > XO["avg", "718"]<-
> > mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
> > XO["avg", "79"]<-
> > mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
> > XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
> > XO["avg", "130"]<-
> > mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
> > XO["avg", "133"]<-
> > mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
> > XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
> > XO["avg", "25"]<- mean(abs((XO[1,"25"])-XO[1,c("8","9","42","80","39")]))
> > XO["avg", "128"]<-
> mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
> > XO["avg", "164"]<-
> > mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
> > XO["avg", "162"]<-
> mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
> > XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
> > XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
> > XO["avg", "172"]<-
> mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))
> > XO["avg", "91"]<-
> > mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
> > XO["avg", "31"]<- mean(abs((XO[1,"31"])-XO[1,c("2","3","36","35","34")]))
> > XO["avg", "73"]<- mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
> > XO["avg", "97"]<-
> > mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
> > XO["avg", "111"]<-
> > mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
> > XO["avg", "64"]<- mean(abs((XO[1,"64"])-XO[1,c("113","62","128","124")]))
> > XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
> > XO["avg", "95"]<- mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
> > XO["avg", "704"]<-
> mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
> > XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
> > XO["avg", "36"]<-
> > mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
> > XO["avg", "80"]<-
> mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
> > XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
> > XO["avg", "68"]<-
> > mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
> > XO["avg", "105"]<-
> mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
> > XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
> > XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
> > XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
> > XO["avg", "88"]<-
> >
> mean(abs((XO[1,"88"])-XO[1,c("86","84","90","109","105","716","710","709")]))
> > XO["avg", "81"]<-
> > mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
> > XO["avg", "140"]<-
> mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
> > XO["avg", "169"]<- mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
> > XO["avg", "109"]<-
> > mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
> > XO["avg", "719"]<-
> mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))
> > XO["avg", "35"]<-
> > mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
> > XO["avg", "185"]<-
> > mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
> > XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
> > XO["avg", "34"]<- mean(abs((XO[1,"34"])-XO[1,c("31","35","707","717")]))
> > XO["avg", "707"]<-
> > mean(abs((XO[1,"707"])-XO[1,c("34","35","36","709","718","717","704")]))
> > XO["avg", "101"]<-
> > mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
> > XO["avg", "38"]<- mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
> > XO["avg", "28"]<- mean(abs((XO[1,"28"])-XO[1,c("6","39","38","36","3")]))
> > XO["avg", "84"]<-
> mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
> > XO["avg", "113"]<-
> > mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
> > XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
> > XO["avg", "168"]<-
> mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
> > XO["avg", "23"]<-
> mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
> > XO["avg", "3"]<-
> mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
> > XO["avg", "117"]<-
> >
> mean(abs((XO[1,"117"])-XO[1,c("101","113","124","130","133","140","68")]))
> > XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
> > XO["pop_size",] <- 76
> > XO["pop_avg_score",]<- mean(XO["EB_score",])
> > for (i in XO){
> >   XO["adj_pop_avg",] <-
> >
> ((XO["pop_avg_score",])*(XO["pop_size",])-(XO["EB_score",]))/((XO["pop_size",]-1))
> >   #here I ran a loop to get info
> >   XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
> > t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
> > XO
> > XO<-rbind(XO,0)
> > rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score",
> "adj_pop_avg",
> > "ind_pop_dif", "non_nei")
> > XO["non_nei",]<-0
> > rowMeans(XO[,1:76])
> >
> > #This is the average observed discrepancy from individuals to neighbours
> > #IOW on average how different is a focal bird in this year different from
> > its neighbours
> > obso=mean(XO["avg",])
> > print(paste("Observed=", obso))
> > XY[15,1]<-round(obso, digits=4)
> >
> >
> > #This is the code I previously posted to find the difference in scores
> > between a single subject and its non-neighbours
> > o<-(ao[,c(13,5)])
> > o<-na.omit(o)
> > o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
> > XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))
> >
> >
> > Best,
> >
> > Monaly.
> >
> >
> > On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]> wrote:
> >
> > > Re dput() etc
> > > https://github.com/hadley/devtools/wiki/Reproducibility
> > >
> > >
> http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
> > >
> > > What dput() does is take your data and ouput it in an ascii format that
> > > let's the reader here create an exact duplicate of your database.
> > >
> > > R is not WYSIWYG. Often what you see on the screen does not tell the
> whole
> > > tale. R supports a number of different data types: vectors, matrices,
> > > data.frames, lists, arrays and others. This site gives a useful though
> not
> > > complete summary of many data types
> > > http://www.statmethods.net/input/datatypes.html. When you have just
> > > created a new data set, or even when working with one that you have not
> > > worked with in some time it is a good idea to do a str() and class()
> on the
> > > data object just to be sure that you are working with the data types
> you
> > > think you have. What looks like a column of numbers in a data.frame may
> > > actually be a set of factors or a set of character (text) data and
> you're
> > > left wondering why multiplying it by some number is not working.
> > >
> > > Here is a short example to illustrate. Just copy and paste in the code
> > >  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
> > > dat1 # data looks identical on the screen
> > > 5*dat1[,"aa"]  # oops
> > > 5*dat1[, "bb"] # okay
> > > str(dat1)
> > >
> > >
> > > John Kane
> > > Kingston ON Canada
> > >
> > >
> > > > -----Original Message-----
> > > > From: [hidden email]
> > > > Sent: Thu, 22 May 2014 16:31:39 +0100
> > > > To: [hidden email], [hidden email]
> > > > Subject: Re: [R] subsetting to exclude different values for each
> subject
> > > > in study
> > > >
> > > > Hi,
> > > >
> > > > Sorry I'm fairly new to R and I don't really understand using dput(),
> > > > when
> > > > you say reproducible example do you mean the code with the output?
> > > >
> > > > Best,
> > > >
> > > > Monaly.
> > > >
> > > >
> > > > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]>
> wrote:
> > > >
> > > >> Hi,
> > > >>
> > > >> It would be helpful if you provide a reproducible example using
> ?dput().
> > > >>
> > > >> A.K.
> > > >>
> > > >>
> > > >>
> > > >>
> > > >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
> > > >> <[hidden email]>
> > > >> wrote:
> > > >> Hi,
> > > >>
> > > >> I've written a code to determine the difference in score for a
> single
> > > >> subject and its non-neighbours
> > > >>
> > > >> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
> > > >> o<-na.omit(o)  ##omitted data with NA
> > > >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed
> neighbours
> > > >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference
> between
> > > >> that
> > > >> individual and average non-neighbours scores
> > > >>
> > > >> Since each subject has a different number of non-neighbours I was
> > > >> wondering
> > > >> if there is an efficient way of writing the code, instead of
> writing the
> > > >> same code again and again (76 subjects) for each subject and its
> > > >> non-neighbours.
> > > >>
> > > >>
> > > >> Best,
> > > >>
> > > >> Monaly.
> > > >>
> > > >>     [[alternative HTML version deleted]]
> > > >>
> > > >> ______________________________________________
> > > >> [hidden email] 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.
> > > >>
> > > >>
> > > >
> > > >       [[alternative HTML version deleted]]
> > > >
> > > > ______________________________________________
> > > > [hidden email] 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.
> > >
> > > ____________________________________________________________
> > > FREE ONLINE PHOTOSHARING - Share your photos online with your friends
> and
> > > family!
> > > Visit http://www.inbox.com/photosharing to find out more!
> > >
> > >
> > >
> >
> >     [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [hidden email] 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.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] 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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Re: subsetting to exclude different values for each subject in study

Frede Aakmann Tøgersen-2
Hi Monaly

I guess that if you made the neighborhood data available (using dput()) then Arun will easily show you how to automatically with only  a couple of code lines instead of those many lines you had to make by hand.

Have a nice day.

Yours sincerely / Med venlig hilsen


Frede Aakmann Tøgersen
Specialist, M.Sc., Ph.D.
Plant Performance & Modeling

Technology & Service Solutions
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> -----Original Message-----
> From: [hidden email] [mailto:[hidden email]]
> On Behalf Of Monaly Mistry
> Sent: 23. maj 2014 12:34
> To: arun; [hidden email]
> Subject: Re: [R] subsetting to exclude different values for each subject in
> study
>
> Hi,
>
> I did use the library deldir, I didn't put that code in since I  wasn't
> sure if it was really relevant to the question as I just made the
> tesselations identifying which tessellation belonged to which individual.
> Following that I by hand recorded which individuals were sharing a boundary
> with each other.
>
> Best,
>
> Monaly.
>
>
> On Fri, May 23, 2014 at 11:25 AM, arun <[hidden email]> wrote:
>
> > Hi,
> >
> > I am not sure how you did that.  May be using library(deldir).  I didn't
> > find that codes in your previous email.
> >
> > A.K.
> >
> > On Friday, May 23, 2014 12:42 AM, Monaly Mistry
> <[hidden email]>
> > wrote:
> >
> >
> >
> > Hi,
> > Neighbours in this case were selected if they shared a boundary in the
> > voroni tesellation.
> >
> > Best,
> > Monaly
> > On May 23, 2014 3:19 AM, "arun" <[hidden email]> wrote:
> > >
> > >
> > >
> > > HI Monaly,
> > > Thanks for the code and dput.  But, I have a doubt about how you are
> > selecting the neigbours.  Is there another dataset with the information?
> > Sorry, if I have missed something
> > > For e.g.
> > > ### average difference b/n neighbours for each individual
> > > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> > >
> > >
> > > A.K.
> > >
> > >
> > > On Thursday, May 22, 2014 5:21 PM, Monaly Mistry <
> > [hidden email]> wrote:
> > > Hi Everyone,
> > >
> > > I hope I did this correctly (I called my data frame ao) and Thank you
> > very
> > > much for the info about using dput(), I'm starting to understand all the
> > > different things that can be done in R and I appreciate all the advice.
> > I
> > > must appologize in advance since my coding is quite long but hopefully it
> > > makes sense. and there is a efficient way to do this.
> > >
> > > structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
> > > 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
> > > 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
> > > 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
> > > 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
> > > 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
> > > 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
> > > 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
> > > 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
> > > 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
> > > 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan =
> structure(c(1L,
> > > 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> > > 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
> > > 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
> > > 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
> > > 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
> > > 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
> > > 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
> > > 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
> > > "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
> > > "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
> > > "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
> > > "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
> > > "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
> > > "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
> > > "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
> > > "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
> > > "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
> > > "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
> > > "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
> > > "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
> > > "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
> > > "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
> > > "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
> > > "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
> > > "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
> > > "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
> > > "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
> > > "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
> > c(2006L,
> > > 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
> > > 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
> > > 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
> > > 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
> > > 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
> > > 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
> > > 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
> > > 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
> > > 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC =
> > c(15.13404,
> > > 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
> > > 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
> > > 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
> > > 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
> > > 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
> > > 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
> > > 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
> > > 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
> > > 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
> > > 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
> > > 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
> > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
> > > 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
> > > 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
> > > 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
> > > 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
> > > 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
> > > 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
> > > 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
> > > 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
> > > 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
> > > 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
> > > 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
> > > 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
> > > 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
> > > 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
> > c(0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
> > > 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
> > > 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
> > > 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
> > > 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
> > > 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
> > > 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
> > > 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
> > > 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> > > 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > > 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
> > > 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
> > > 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
> > > 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
> > > 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
> > > 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
> > > 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
> > > 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
> > > 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
> > > 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
> > > 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
> > > 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
> > > 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
> > > 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
> > > 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
> > > 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
> > > 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
> > > 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
> > > 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
> > > 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
> > > 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
> > > 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
> > > 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
> > > 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
> > > 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
> > > 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
> > > 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
> > > 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
> > > 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
> > > 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
> > > 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
> > > 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
> > > 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
> > > 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
> > > c(729.2669944,
> > > 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
> > > 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
> > > 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
> > > 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
> > > 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
> > > 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
> > > 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
> > > 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
> > > 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
> > > 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
> > > 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
> > > 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
> > > 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
> > > 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
> > > 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
> > > 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
> > > 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
> > > 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
> > > 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
> > > 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
> > > 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
> > > 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
> > > 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
> > > 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
> > > 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
> > > 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
> > > 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
> > > 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
> > > 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
> > > 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
> > > 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
> > > 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
> > > 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
> > > 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
> > > 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
> > > 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
> > > 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
> > > 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
> > > 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
> > > 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK",
> > "RingNummerMan",
> > > "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
> > > "ManipulatieOuders",
> > > "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
> > > "NestkastNummer",
> > > "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
> > c(NA,
> > > -99L))
> > >
> > >
> > > #Below is the code I made to run my analyses
> > > XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to store my
> > > results in
> > > names(ao)
> > > ao$NestkastNummer
> > > b<-c(77:99)
> > > abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
> > > rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
> > > "adj_pop_avg", "ind_pop_dif")
> > > colnames(XO) = c((abo))
> > > ncol(XO)
> > > names(ao)
> > > t <- ao$COR_LOC;t
> > > i <- c(77:99)
> > > ti <- t[-i];ti
> > > XO[1,] = c(ti);XO  #assigned values from data frame to the matrix
> > >
> > > ### average difference b/n neighbours for each individual
> > > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> > > XO["avg", "124"]<-
> > > mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
> > > XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
> > > XO["avg", "717"]<-
> > mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
> > > XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
> > > XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
> > > XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
> > > XO["avg", "42"]<-
> > > mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
> > > XO["avg", "90"]<-
> > mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
> > > XO["avg", "9"]<- mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
> > > XO["avg", "713"]<-
> > mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))
> > > XO["avg", "82"]<- mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
> > > XO["avg", "709"]<-
> > > mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
> > > XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
> > > XO["avg", "39"]<-
> > > mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
> > > XO["avg", "86"]<-
> > > mean(abs((XO[1,"86"])-
> XO[1,c("38","39","81","82","84","88","709","36")]))
> > > XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
> > > XO["avg", "710"]<-
> > > mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
> > > XO["avg", "93"]<-
> > > mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
> > > XO["avg", "94"]<-
> > mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
> > > XO["avg", "163"]<-
> > mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
> > > XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
> > > XO["avg", "170"]<-
> > mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))
> > > XO["avg", "718"]<-
> > > mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
> > > XO["avg", "79"]<-
> > > mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
> > > XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
> > > XO["avg", "130"]<-
> > > mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
> > > XO["avg", "133"]<-
> > > mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
> > > XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
> > > XO["avg", "25"]<- mean(abs((XO[1,"25"])-
> XO[1,c("8","9","42","80","39")]))
> > > XO["avg", "128"]<-
> > mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
> > > XO["avg", "164"]<-
> > > mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
> > > XO["avg", "162"]<-
> > mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
> > > XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
> > > XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
> > > XO["avg", "172"]<-
> > mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))
> > > XO["avg", "91"]<-
> > > mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
> > > XO["avg", "31"]<- mean(abs((XO[1,"31"])-
> XO[1,c("2","3","36","35","34")]))
> > > XO["avg", "73"]<- mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
> > > XO["avg", "97"]<-
> > > mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
> > > XO["avg", "111"]<-
> > > mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
> > > XO["avg", "64"]<- mean(abs((XO[1,"64"])-
> XO[1,c("113","62","128","124")]))
> > > XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
> > > XO["avg", "95"]<- mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
> > > XO["avg", "704"]<-
> > mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
> > > XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
> > > XO["avg", "36"]<-
> > > mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
> > > XO["avg", "80"]<-
> > mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
> > > XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
> > > XO["avg", "68"]<-
> > > mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
> > > XO["avg", "105"]<-
> > mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
> > > XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
> > > XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
> > > XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
> > > XO["avg", "88"]<-
> > >
> > mean(abs((XO[1,"88"])-
> XO[1,c("86","84","90","109","105","716","710","709")]))
> > > XO["avg", "81"]<-
> > > mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
> > > XO["avg", "140"]<-
> > mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
> > > XO["avg", "169"]<- mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
> > > XO["avg", "109"]<-
> > > mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
> > > XO["avg", "719"]<-
> > mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))
> > > XO["avg", "35"]<-
> > > mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
> > > XO["avg", "185"]<-
> > > mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
> > > XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
> > > XO["avg", "34"]<- mean(abs((XO[1,"34"])-
> XO[1,c("31","35","707","717")]))
> > > XO["avg", "707"]<-
> > > mean(abs((XO[1,"707"])-
> XO[1,c("34","35","36","709","718","717","704")]))
> > > XO["avg", "101"]<-
> > > mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
> > > XO["avg", "38"]<- mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
> > > XO["avg", "28"]<- mean(abs((XO[1,"28"])-
> XO[1,c("6","39","38","36","3")]))
> > > XO["avg", "84"]<-
> > mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
> > > XO["avg", "113"]<-
> > > mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
> > > XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
> > > XO["avg", "168"]<-
> > mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
> > > XO["avg", "23"]<-
> > mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
> > > XO["avg", "3"]<-
> > mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
> > > XO["avg", "117"]<-
> > >
> > mean(abs((XO[1,"117"])-
> XO[1,c("101","113","124","130","133","140","68")]))
> > > XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
> > > XO["pop_size",] <- 76
> > > XO["pop_avg_score",]<- mean(XO["EB_score",])
> > > for (i in XO){
> > >   XO["adj_pop_avg",] <-
> > >
> > ((XO["pop_avg_score",])*(XO["pop_size",])-
> (XO["EB_score",]))/((XO["pop_size",]-1))
> > >   #here I ran a loop to get info
> > >   XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
> > > t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
> > > XO
> > > XO<-rbind(XO,0)
> > > rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score",
> > "adj_pop_avg",
> > > "ind_pop_dif", "non_nei")
> > > XO["non_nei",]<-0
> > > rowMeans(XO[,1:76])
> > >
> > > #This is the average observed discrepancy from individuals to neighbours
> > > #IOW on average how different is a focal bird in this year different from
> > > its neighbours
> > > obso=mean(XO["avg",])
> > > print(paste("Observed=", obso))
> > > XY[15,1]<-round(obso, digits=4)
> > >
> > >
> > > #This is the code I previously posted to find the difference in scores
> > > between a single subject and its non-neighbours
> > > o<-(ao[,c(13,5)])
> > > o<-na.omit(o)
> > > o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
> > > XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))
> > >
> > >
> > > Best,
> > >
> > > Monaly.
> > >
> > >
> > > On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]>
> wrote:
> > >
> > > > Re dput() etc
> > > > https://github.com/hadley/devtools/wiki/Reproducibility
> > > >
> > > >
> > http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-
> reproducible-example
> > > >
> > > > What dput() does is take your data and ouput it in an ascii format that
> > > > let's the reader here create an exact duplicate of your database.
> > > >
> > > > R is not WYSIWYG. Often what you see on the screen does not tell the
> > whole
> > > > tale. R supports a number of different data types: vectors, matrices,
> > > > data.frames, lists, arrays and others. This site gives a useful though
> > not
> > > > complete summary of many data types
> > > > http://www.statmethods.net/input/datatypes.html. When you have
> just
> > > > created a new data set, or even when working with one that you have
> not
> > > > worked with in some time it is a good idea to do a str() and class()
> > on the
> > > > data object just to be sure that you are working with the data types
> > you
> > > > think you have. What looks like a column of numbers in a data.frame
> may
> > > > actually be a set of factors or a set of character (text) data and
> > you're
> > > > left wondering why multiplying it by some number is not working.
> > > >
> > > > Here is a short example to illustrate. Just copy and paste in the code
> > > >  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
> > > > dat1 # data looks identical on the screen
> > > > 5*dat1[,"aa"]  # oops
> > > > 5*dat1[, "bb"] # okay
> > > > str(dat1)
> > > >
> > > >
> > > > John Kane
> > > > Kingston ON Canada
> > > >
> > > >
> > > > > -----Original Message-----
> > > > > From: [hidden email]
> > > > > Sent: Thu, 22 May 2014 16:31:39 +0100
> > > > > To: [hidden email], [hidden email]
> > > > > Subject: Re: [R] subsetting to exclude different values for each
> > subject
> > > > > in study
> > > > >
> > > > > Hi,
> > > > >
> > > > > Sorry I'm fairly new to R and I don't really understand using dput(),
> > > > > when
> > > > > you say reproducible example do you mean the code with the
> output?
> > > > >
> > > > > Best,
> > > > >
> > > > > Monaly.
> > > > >
> > > > >
> > > > > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]>
> > wrote:
> > > > >
> > > > >> Hi,
> > > > >>
> > > > >> It would be helpful if you provide a reproducible example using
> > ?dput().
> > > > >>
> > > > >> A.K.
> > > > >>
> > > > >>
> > > > >>
> > > > >>
> > > > >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
> > > > >> <[hidden email]>
> > > > >> wrote:
> > > > >> Hi,
> > > > >>
> > > > >> I've written a code to determine the difference in score for a
> > single
> > > > >> subject and its non-neighbours
> > > > >>
> > > > >> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
> > > > >> o<-na.omit(o)  ##omitted data with NA
> > > > >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed
> > neighbours
> > > > >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference
> > between
> > > > >> that
> > > > >> individual and average non-neighbours scores
> > > > >>
> > > > >> Since each subject has a different number of non-neighbours I was
> > > > >> wondering
> > > > >> if there is an efficient way of writing the code, instead of
> > writing the
> > > > >> same code again and again (76 subjects) for each subject and its
> > > > >> non-neighbours.
> > > > >>
> > > > >>
> > > > >> Best,
> > > > >>
> > > > >> Monaly.
> > > > >>
> > > > >>     [[alternative HTML version deleted]]
> > > > >>
> > > > >> ______________________________________________
> > > > >> [hidden email] 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.
> > > > >>
> > > > >>
> > > > >
> > > > >       [[alternative HTML version deleted]]
> > > > >
> > > > > ______________________________________________
> > > > > [hidden email] 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.
> > > >
> > > >
> __________________________________________________________
> __
> > > > FREE ONLINE PHOTOSHARING - Share your photos online with your
> friends
> > and
> > > > family!
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> > > >
> > >
> > >     [[alternative HTML version deleted]]
> > >
> > > ______________________________________________
> > > [hidden email] 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.
> >
>
>       [[alternative HTML version deleted]]
>
> ______________________________________________
> [hidden email] 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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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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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
Hi Arun and Frede,

So the dput() is below (it's the same data file as before), but below that
is the code I used to make the tessellation.  Thanks for your help.

> dput(ao)
structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
"AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
"AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
"AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
"AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
"AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
"AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
"AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
"AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
"AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
"AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
"AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
"AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
"AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
"AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
"AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
"AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
"AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
"AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
"AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
"AU...15049", "AU...15505"), class = "factor"), year_score_taken = c(2006L,
2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404,
13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
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378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
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356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
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2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
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10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
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21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
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55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
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55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
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23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan",
"year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
"ManipulatieOuders",
"LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
"NestkastNummer",
"lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names = c(NA,
-99L))


#Code for  tessellation
library(deldir)
ao= read.table("C:/Users/Monaly/Desktop/2012_malenest.txt", header=TRUE)
a29= deldir(ao$lat_xm, ao$long_ym)
a30=tile.list(a29)
plot(a30, close=TRUE, main="2012 Male Nest", xlab="latitude (m)",
ylab="longitude (m)", wpoints="real", verbose=FALSE,num=TRUE, rw=c(0, 1200,
0, 2000))
text(ao$lat_xm, ao$long_ym,col=c(2,1,4),labels=round(ao$NestkastNummer, 3),
pos=2, offset=0.2, cex=0.7)  #this was to identify the points


On Fri, May 23, 2014 at 11:58 AM, Frede Aakmann Tøgersen
<[hidden email]>wrote:

> Hi Monaly
>
> I guess that if you made the neighborhood data available (using dput())
> then Arun will easily show you how to automatically with only  a couple of
> code lines instead of those many lines you had to make by hand.
>
> Have a nice day.
>
> Yours sincerely / Med venlig hilsen
>
>
> Frede Aakmann Tøgersen
> Specialist, M.Sc., Ph.D.
> Plant Performance & Modeling
>
> Technology & Service Solutions
> T +45 9730 5135
> M +45 2547 6050
> [hidden email]
> http://www.vestas.com
>
> Company reg. name: Vestas Wind Systems A/S
> This e-mail is subject to our e-mail disclaimer statement.
> Please refer to www.vestas.com/legal/notice
> If you have received this e-mail in error please contact the sender.
>
>
> > -----Original Message-----
> > From: [hidden email] [mailto:[hidden email]]
> > On Behalf Of Monaly Mistry
> > Sent: 23. maj 2014 12:34
> > To: arun; [hidden email]
> > Subject: Re: [R] subsetting to exclude different values for each subject
> in
> > study
> >
> > Hi,
> >
> > I did use the library deldir, I didn't put that code in since I  wasn't
> > sure if it was really relevant to the question as I just made the
> > tesselations identifying which tessellation belonged to which individual.
> > Following that I by hand recorded which individuals were sharing a
> boundary
> > with each other.
> >
> > Best,
> >
> > Monaly.
> >
> >
> > On Fri, May 23, 2014 at 11:25 AM, arun <[hidden email]> wrote:
> >
> > > Hi,
> > >
> > > I am not sure how you did that.  May be using library(deldir).  I
> didn't
> > > find that codes in your previous email.
> > >
> > > A.K.
> > >
> > > On Friday, May 23, 2014 12:42 AM, Monaly Mistry
> > <[hidden email]>
> > > wrote:
> > >
> > >
> > >
> > > Hi,
> > > Neighbours in this case were selected if they shared a boundary in the
> > > voroni tesellation.
> > >
> > > Best,
> > > Monaly
> > > On May 23, 2014 3:19 AM, "arun" <[hidden email]> wrote:
> > > >
> > > >
> > > >
> > > > HI Monaly,
> > > > Thanks for the code and dput.  But, I have a doubt about how you are
> > > selecting the neigbours.  Is there another dataset with the
> information?
> > > Sorry, if I have missed something
> > > > For e.g.
> > > > ### average difference b/n neighbours for each individual
> > > > XO["avg", "176"]<-
> mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> > > >
> > > >
> > > > A.K.
> > > >
> > > >
> > > > On Thursday, May 22, 2014 5:21 PM, Monaly Mistry <
> > > [hidden email]> wrote:
> > > > Hi Everyone,
> > > >
> > > > I hope I did this correctly (I called my data frame ao) and Thank you
> > > very
> > > > much for the info about using dput(), I'm starting to understand all
> the
> > > > different things that can be done in R and I appreciate all the
> advice.
> > > I
> > > > must appologize in advance since my coding is quite long but
> hopefully it
> > > > makes sense. and there is a efficient way to do this.
> > > >
> > > > structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
> > > > 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
> > > > 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
> > > > 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
> > > > 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
> > > > 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
> > > > 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
> > > > 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
> > > > 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
> > > > 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
> > > > 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > > NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan =
> > structure(c(1L,
> > > > 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> > > > 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
> > > > 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
> > > > 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
> > > > 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
> > > > 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
> > > > 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
> > > > 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
> > > > "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
> > > > "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
> > > > "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
> > > > "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
> > > > "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
> > > > "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
> > > > "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
> > > > "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
> > > > "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
> > > > "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
> > > > "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
> > > > "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
> > > > "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
> > > > "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
> > > > "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
> > > > "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
> > > > "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
> > > > "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
> > > > "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
> > > > "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
> > > c(2006L,
> > > > 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
> > > > 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
> > > > 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
> > > > 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
> > > > 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
> > > > 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
> > > > 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
> > > > 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
> > > > 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC =
> > > c(15.13404,
> > > > 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
> > > > 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
> > > > 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
> > > > 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
> > > > 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
> > > > 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
> > > > 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
> > > > 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
> > > > 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
> > > > 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
> > > > 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
> > > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> > > > NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
> > > > 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
> > > > 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
> > > > 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
> > > > 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
> > > > 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
> > > > 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
> > > > 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
> > > > 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
> > > > 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
> > > > 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
> > > > 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
> > > > 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
> > > > 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
> > > > 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> > > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
> > > c(0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> > > > 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
> > > > 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
> > > > 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
> > > > 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
> > > > 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
> > > > 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
> > > > 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
> > > > 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
> > > > 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> > > > 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
> > > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
> > > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
> > > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> > > > 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
> > > > 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
> > > > 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
> > > > 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
> > > > 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
> > > > 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
> > > > 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
> > > > 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
> > > > 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
> > > > 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
> > > > 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
> > > > 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
> > > > 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
> > > > 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
> > > > 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
> > > > 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
> > > > 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
> > > > 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
> > > > 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
> > > > 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
> > > > 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
> > > > 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
> > > > 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
> > > > 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
> > > > 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
> > > > 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
> > > > 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
> > > > 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
> > > > 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
> > > > 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
> > > > 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
> > > > 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
> > > > 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
> > > > 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
> > > > c(729.2669944,
> > > > 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
> > > > 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
> > > > 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
> > > > 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
> > > > 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
> > > > 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
> > > > 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
> > > > 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
> > > > 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
> > > > 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
> > > > 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
> > > > 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
> > > > 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
> > > > 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
> > > > 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
> > > > 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
> > > > 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
> > > > 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
> > > > 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
> > > > 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
> > > > 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
> > > > 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
> > > > 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
> > > > 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
> > > > 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
> > > > 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
> > > > 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
> > > > 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
> > > > 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
> > > > 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
> > > > 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
> > > > 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
> > > > 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
> > > > 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
> > > > 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
> > > > 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
> > > > 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
> > > > 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
> > > > 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
> > > > 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> > > > 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK",
> > > "RingNummerMan",
> > > > "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
> > > > "ManipulatieOuders",
> > > > "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
> > > > "NestkastNummer",
> > > > "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
> > > c(NA,
> > > > -99L))
> > > >
> > > >
> > > > #Below is the code I made to run my analyses
> > > > XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to
> store my
> > > > results in
> > > > names(ao)
> > > > ao$NestkastNummer
> > > > b<-c(77:99)
> > > > abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
> > > > rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
> > > > "adj_pop_avg", "ind_pop_dif")
> > > > colnames(XO) = c((abo))
> > > > ncol(XO)
> > > > names(ao)
> > > > t <- ao$COR_LOC;t
> > > > i <- c(77:99)
> > > > ti <- t[-i];ti
> > > > XO[1,] = c(ti);XO  #assigned values from data frame to the matrix
> > > >
> > > > ### average difference b/n neighbours for each individual
> > > > XO["avg", "176"]<-
> mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
> > > > XO["avg", "124"]<-
> > > > mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
> > > > XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
> > > > XO["avg", "717"]<-
> > > mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
> > > > XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
> > > > XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
> > > > XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
> > > > XO["avg", "42"]<-
> > > > mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
> > > > XO["avg", "90"]<-
> > > mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
> > > > XO["avg", "9"]<-
> mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
> > > > XO["avg", "713"]<-
> > > mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))
> > > > XO["avg", "82"]<-
> mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
> > > > XO["avg", "709"]<-
> > > >
> mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
> > > > XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
> > > > XO["avg", "39"]<-
> > > >
> mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
> > > > XO["avg", "86"]<-
> > > > mean(abs((XO[1,"86"])-
> > XO[1,c("38","39","81","82","84","88","709","36")]))
> > > > XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
> > > > XO["avg", "710"]<-
> > > > mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
> > > > XO["avg", "93"]<-
> > > > mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
> > > > XO["avg", "94"]<-
> > > mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
> > > > XO["avg", "163"]<-
> > > mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
> > > > XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
> > > > XO["avg", "170"]<-
> > > mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))
> > > > XO["avg", "718"]<-
> > > > mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
> > > > XO["avg", "79"]<-
> > > > mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
> > > > XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
> > > > XO["avg", "130"]<-
> > > > mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
> > > > XO["avg", "133"]<-
> > > > mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
> > > > XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
> > > > XO["avg", "25"]<- mean(abs((XO[1,"25"])-
> > XO[1,c("8","9","42","80","39")]))
> > > > XO["avg", "128"]<-
> > > mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
> > > > XO["avg", "164"]<-
> > > > mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
> > > > XO["avg", "162"]<-
> > > mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
> > > > XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
> > > > XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
> > > > XO["avg", "172"]<-
> > > mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))
> > > > XO["avg", "91"]<-
> > > > mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
> > > > XO["avg", "31"]<- mean(abs((XO[1,"31"])-
> > XO[1,c("2","3","36","35","34")]))
> > > > XO["avg", "73"]<-
> mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
> > > > XO["avg", "97"]<-
> > > > mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
> > > > XO["avg", "111"]<-
> > > >
> mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
> > > > XO["avg", "64"]<- mean(abs((XO[1,"64"])-
> > XO[1,c("113","62","128","124")]))
> > > > XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
> > > > XO["avg", "95"]<-
> mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
> > > > XO["avg", "704"]<-
> > > mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
> > > > XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
> > > > XO["avg", "36"]<-
> > > > mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
> > > > XO["avg", "80"]<-
> > > mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
> > > > XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
> > > > XO["avg", "68"]<-
> > > > mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
> > > > XO["avg", "105"]<-
> > > mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
> > > > XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
> > > > XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
> > > > XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
> > > > XO["avg", "88"]<-
> > > >
> > > mean(abs((XO[1,"88"])-
> > XO[1,c("86","84","90","109","105","716","710","709")]))
> > > > XO["avg", "81"]<-
> > > > mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
> > > > XO["avg", "140"]<-
> > > mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
> > > > XO["avg", "169"]<-
> mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
> > > > XO["avg", "109"]<-
> > > > mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
> > > > XO["avg", "719"]<-
> > > mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))
> > > > XO["avg", "35"]<-
> > > > mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
> > > > XO["avg", "185"]<-
> > > > mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
> > > > XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
> > > > XO["avg", "34"]<- mean(abs((XO[1,"34"])-
> > XO[1,c("31","35","707","717")]))
> > > > XO["avg", "707"]<-
> > > > mean(abs((XO[1,"707"])-
> > XO[1,c("34","35","36","709","718","717","704")]))
> > > > XO["avg", "101"]<-
> > > > mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
> > > > XO["avg", "38"]<-
> mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
> > > > XO["avg", "28"]<- mean(abs((XO[1,"28"])-
> > XO[1,c("6","39","38","36","3")]))
> > > > XO["avg", "84"]<-
> > > mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
> > > > XO["avg", "113"]<-
> > > >
> mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
> > > > XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
> > > > XO["avg", "168"]<-
> > > mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
> > > > XO["avg", "23"]<-
> > > mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
> > > > XO["avg", "3"]<-
> > > mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
> > > > XO["avg", "117"]<-
> > > >
> > > mean(abs((XO[1,"117"])-
> > XO[1,c("101","113","124","130","133","140","68")]))
> > > > XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
> > > > XO["pop_size",] <- 76
> > > > XO["pop_avg_score",]<- mean(XO["EB_score",])
> > > > for (i in XO){
> > > >   XO["adj_pop_avg",] <-
> > > >
> > > ((XO["pop_avg_score",])*(XO["pop_size",])-
> > (XO["EB_score",]))/((XO["pop_size",]-1))
> > > >   #here I ran a loop to get info
> > > >   XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
> > > > t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
> > > > XO
> > > > XO<-rbind(XO,0)
> > > > rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score",
> > > "adj_pop_avg",
> > > > "ind_pop_dif", "non_nei")
> > > > XO["non_nei",]<-0
> > > > rowMeans(XO[,1:76])
> > > >
> > > > #This is the average observed discrepancy from individuals to
> neighbours
> > > > #IOW on average how different is a focal bird in this year different
> from
> > > > its neighbours
> > > > obso=mean(XO["avg",])
> > > > print(paste("Observed=", obso))
> > > > XY[15,1]<-round(obso, digits=4)
> > > >
> > > >
> > > > #This is the code I previously posted to find the difference in
> scores
> > > > between a single subject and its non-neighbours
> > > > o<-(ao[,c(13,5)])
> > > > o<-na.omit(o)
> > > > o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
> > > > XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))
> > > >
> > > >
> > > > Best,
> > > >
> > > > Monaly.
> > > >
> > > >
> > > > On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]>
> > wrote:
> > > >
> > > > > Re dput() etc
> > > > > https://github.com/hadley/devtools/wiki/Reproducibility
> > > > >
> > > > >
> > > http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-
> > reproducible-example
> > > > >
> > > > > What dput() does is take your data and ouput it in an ascii format
> that
> > > > > let's the reader here create an exact duplicate of your database.
> > > > >
> > > > > R is not WYSIWYG. Often what you see on the screen does not tell
> the
> > > whole
> > > > > tale. R supports a number of different data types: vectors,
> matrices,
> > > > > data.frames, lists, arrays and others. This site gives a useful
> though
> > > not
> > > > > complete summary of many data types
> > > > > http://www.statmethods.net/input/datatypes.html. When you have
> > just
> > > > > created a new data set, or even when working with one that you have
> > not
> > > > > worked with in some time it is a good idea to do a str() and
> class()
> > > on the
> > > > > data object just to be sure that you are working with the data
> types
> > > you
> > > > > think you have. What looks like a column of numbers in a data.frame
> > may
> > > > > actually be a set of factors or a set of character (text) data and
> > > you're
> > > > > left wondering why multiplying it by some number is not working.
> > > > >
> > > > > Here is a short example to illustrate. Just copy and paste in the
> code
> > > > >  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
> > > > > dat1 # data looks identical on the screen
> > > > > 5*dat1[,"aa"]  # oops
> > > > > 5*dat1[, "bb"] # okay
> > > > > str(dat1)
> > > > >
> > > > >
> > > > > John Kane
> > > > > Kingston ON Canada
> > > > >
> > > > >
> > > > > > -----Original Message-----
> > > > > > From: [hidden email]
> > > > > > Sent: Thu, 22 May 2014 16:31:39 +0100
> > > > > > To: [hidden email], [hidden email]
> > > > > > Subject: Re: [R] subsetting to exclude different values for each
> > > subject
> > > > > > in study
> > > > > >
> > > > > > Hi,
> > > > > >
> > > > > > Sorry I'm fairly new to R and I don't really understand using
> dput(),
> > > > > > when
> > > > > > you say reproducible example do you mean the code with the
> > output?
> > > > > >
> > > > > > Best,
> > > > > >
> > > > > > Monaly.
> > > > > >
> > > > > >
> > > > > > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]>
> > > wrote:
> > > > > >
> > > > > >> Hi,
> > > > > >>
> > > > > >> It would be helpful if you provide a reproducible example using
> > > ?dput().
> > > > > >>
> > > > > >> A.K.
> > > > > >>
> > > > > >>
> > > > > >>
> > > > > >>
> > > > > >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
> > > > > >> <[hidden email]>
> > > > > >> wrote:
> > > > > >> Hi,
> > > > > >>
> > > > > >> I've written a code to determine the difference in score for a
> > > single
> > > > > >> subject and its non-neighbours
> > > > > >>
> > > > > >> o<-(ao[,c(13,5)]) ##this is the table with the relevant
> information
> > > > > >> o<-na.omit(o)  ##omitted data with NA
> > > > > >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed
> > > neighbours
> > > > > >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference
> > > between
> > > > > >> that
> > > > > >> individual and average non-neighbours scores
> > > > > >>
> > > > > >> Since each subject has a different number of non-neighbours I
> was
> > > > > >> wondering
> > > > > >> if there is an efficient way of writing the code, instead of
> > > writing the
> > > > > >> same code again and again (76 subjects) for each subject and its
> > > > > >> non-neighbours.
> > > > > >>
> > > > > >>
> > > > > >> Best,
> > > > > >>
> > > > > >> Monaly.
> > > > > >>
> > > > > >>     [[alternative HTML version deleted]]
> > > > > >>
> > > > > >> ______________________________________________
> > > > > >> [hidden email] 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.
> > > > > >>
> > > > > >>
> > > > > >
> > > > > >       [[alternative HTML version deleted]]
> > > > > >
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Re: subsetting to exclude different values for each subject in study

arun kirshna

Hi Monaly,
May be this helps:
b<- 77:99
ao1 <- ao[-b,]
##Your code:

XO<- matrix( 0,6, 76, byrow=TRUE);XO
abo<-ao$NestkastNummer[-b];abo  #removed values that were NA
rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
"adj_pop_avg", "ind_pop_dif")
colnames(XO) = abo
t <- ao$COR_LOC;t
i <- c(77:99)
ti <- t[-i];ti
XO[1,] = c(ti);XO
 

library(deldir)
library(spdep)
mat <- cbind(lat=ao1$lat_xm, long=ao1$long_ym)
library(spdep)
 coords <- coordinates(mat)
ind <- ao1$NestkastNummer

col.tri.nb <- tri2nb(coords, row.names=ind)
 lapply(col.tri.nb,function(x) ind[x])[1:5] ###
[[1]]
[1] 713 715 162 148 140 117

[[2]]
[1] 130 128 172  64 113 117

[[3]]
[1] 54 19 16 73 74

[[4]]
[1]   2  31 704  34 707

[[5]]
[1] 51 94 57 73 62

XO[2,] <- sapply(seq_along(col.tri.nb),function(i) mean(abs(ind[i]-ind[col.tri.nb[[i]]])))

A.K.



On Friday, May 23, 2014 7:17 AM, Monaly Mistry <[hidden email]> wrote:



Hi Arun and Frede,

So the dput() is below (it's the same data file as before), but below that is the code I used to make the tessellation.  Thanks for your help.

> dput(ao)
structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L, 
22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L, 
30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L, 
36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L, 
35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L, 
36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L, 
36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L, 
35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L, 
35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L, 
35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L, 
36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L, 
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 
49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L, 
65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L, 
83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L, 
32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L, 
91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371", 
"AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504", 
"AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491", 
"AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701", 
"AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965", 
"AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996", 
"AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387", 
"AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074", 
"AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067", 
"AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106", 
"AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178", 
"AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252", 
"AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275", 
"AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338", 
"AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520", 
"AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737", 
"AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873", 
"AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951", 
"AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009", 
"AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046", 
"AU...15049", "AU...15505"), class = "factor"), year_score_taken = c(2006L, 
2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L, 
2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L, 
2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L, 
2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L, 
2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L, 
2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L, 
2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404, 
13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718, 
16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945, 
12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718, 
18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588, 
34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234, 
22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616, 
23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718, 
40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718, 
7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188, 
28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718, 
39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L, 
344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L, 
352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L, 
377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L, 
378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L, 
378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L, 
378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L, 
377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L, 
377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L, 
373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L, 
377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L, 
400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L, 
356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L, 
378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L, 
400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L, 
1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L, 
13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L, 
10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L, 
12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L, 
21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L, 
11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L, 
11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L, 
5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L, 
55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L, 
55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L, 
55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L, 
55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L, 
55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L, 
55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L, 
55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L, 
55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L, 
55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L, 
55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L, 
55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L, 
55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L, 
55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L, 
55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L, 
55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L, 
55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L, 
55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L, 
55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L, 
55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L, 
55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L, 
55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L, 
55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L, 
55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L, 
55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L, 
55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L, 
19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L, 
93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L, 
25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L, 
64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L, 
127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L, 
101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L, 
183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L, 
17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm = c(729.2669944, 
1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688, 
198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345, 
388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924, 
308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656, 
267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672, 
1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712, 
952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424, 
456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678, 
1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113, 
135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177, 
237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193, 
770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334, 
437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095, 
177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528, 
1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674, 
325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545, 
423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216, 
709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308, 
659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721, 
722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022, 
744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671, 
1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164, 
887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293, 
1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291, 
1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801, 
636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835, 
485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869, 
887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864, 
783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057, 
731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549, 
1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063, 
480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401, 
1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502, 
816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948, 
415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577, 
1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479, 
197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715, 
597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634, 
433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272, 
796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan", 
"year_score_taken", "COR_LOC", "IndividuID", "BroedJaar", "ManipulatieOuders", 
"LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID", "NestkastNummer", 
"lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names = c(NA, 
-99L))


#Code for  tessellation
library(deldir)

ao= read.table("C:/Users/Monaly/Desktop/2012_malenest.txt", header=TRUE)

a29= deldir(ao$lat_xm, ao$long_ym)

a30=tile.list(a29)

plot(a30, close=TRUE, main="2012 Male Nest", xlab="latitude (m)", ylab="longitude (m)", wpoints="real", verbose=FALSE,num=TRUE, rw=c(0, 1200, 0, 2000))

text(ao$lat_xm, ao$long_ym,col=c(2,1,4),labels=round(ao$NestkastNummer, 3), pos=2, offset=0.2, cex=0.7)  #this was to identify the points




On Fri, May 23, 2014 at 11:58 AM, Frede Aakmann Tøgersen <[hidden email]> wrote:

Hi Monaly

>
>I guess that if you made the neighborhood data available (using dput()) then Arun will easily show you how to automatically with only  a couple of code lines instead of those many lines you had to make by hand.
>
>Have a nice day.
>
>Yours sincerely / Med venlig hilsen
>
>
>Frede Aakmann Tøgersen
>Specialist, M.Sc., Ph.D.
>Plant Performance & Modeling
>
>Technology & Service Solutions
>T +45 9730 5135
>M +45 2547 6050
>[hidden email]
>http://www.vestas.com
>
>Company reg. name: Vestas Wind Systems A/S
>This e-mail is subject to our e-mail disclaimer statement.
>Please refer to www.vestas.com/legal/notice
>If you have received this e-mail in error please contact the sender.
>
>
>
>> -----Original Message-----
>> From: [hidden email] [mailto:[hidden email]]
>> On Behalf Of Monaly Mistry
>> Sent: 23. maj 2014 12:34
>> To: arun; [hidden email]
>> Subject: Re: [R] subsetting to exclude different values for each subject in
>> study
>>
>> Hi,
>>
>
>> I did use the library deldir, I didn't put that code in since I  wasn't
>> sure if it was really relevant to the question as I just made the
>> tesselations identifying which tessellation belonged to which individual.
>> Following that I by hand recorded which individuals were sharing a boundary
>> with each other.
>>
>> Best,
>>
>> Monaly.
>>
>>
>> On Fri, May 23, 2014 at 11:25 AM, arun <[hidden email]> wrote:
>>
>> > Hi,
>> >
>> > I am not sure how you did that.  May be using library(deldir).  I didn't
>> > find that codes in your previous email.
>> >
>> > A.K.
>> >
>> > On Friday, May 23, 2014 12:42 AM, Monaly Mistry
>> <[hidden email]>
>> > wrote:
>> >
>> >
>> >
>> > Hi,
>> > Neighbours in this case were selected if they shared a boundary in the
>> > voroni tesellation.
>> >
>> > Best,
>> > Monaly
>> > On May 23, 2014 3:19 AM, "arun" <[hidden email]> wrote:
>> > >
>> > >
>> > >
>> > > HI Monaly,
>> > > Thanks for the code and dput.  But, I have a doubt about how you are
>> > selecting the neigbours.  Is there another dataset with the information?
>> > Sorry, if I have missed something
>> > > For e.g.
>> > > ### average difference b/n neighbours for each individual
>> > > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
>> > >
>> > >
>> > > A.K.
>> > >
>> > >
>> > > On Thursday, May 22, 2014 5:21 PM, Monaly Mistry <
>> > [hidden email]> wrote:
>> > > Hi Everyone,
>> > >
>> > > I hope I did this correctly (I called my data frame ao) and Thank you
>> > very
>> > > much for the info about using dput(), I'm starting to understand all the
>> > > different things that can be done in R and I appreciate all the advice.
>> > I
>> > > must appologize in advance since my coding is quite long but hopefully it
>> > > makes sense. and there is a efficient way to do this.
>> > >
>> > > structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
>> > > 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
>> > > 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
>> > > 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
>> > > 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
>> > > 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
>> > > 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
>> > > 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
>> > > 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
>> > > 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
>> > > 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>> > > NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan =
>> structure(c(1L,
>> > > 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
>> > > 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
>> > > 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
>> > > 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
>> > > 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
>> > > 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
>> > > 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
>> > > 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
>> > > "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
>> > > "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
>> > > "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
>> > > "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
>> > > "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
>> > > "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
>> > > "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
>> > > "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
>> > > "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
>> > > "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
>> > > "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
>> > > "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
>> > > "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
>> > > "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
>> > > "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
>> > > "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
>> > > "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
>> > > "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
>> > > "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
>> > > "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
>> > c(2006L,
>> > > 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
>> > > 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
>> > > 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
>> > > 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
>> > > 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
>> > > 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
>> > > 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
>> > > 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
>> > > 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>> > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC =
>> > c(15.13404,
>> > > 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
>> > > 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
>> > > 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
>> > > 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
>> > > 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
>> > > 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
>> > > 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
>> > > 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
>> > > 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
>> > > 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
>> > > 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
>> > > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
>> > > NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
>> > > 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
>> > > 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
>> > > 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
>> > > 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
>> > > 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
>> > > 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
>> > > 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
>> > > 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
>> > > 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
>> > > 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
>> > > 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
>> > > 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
>> > > 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
>> > > 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
>> > > 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
>> > c(0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
>> > > 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
>> > > 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
>> > > 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
>> > > 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
>> > > 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
>> > > 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
>> > > 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
>> > > 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
>> > > 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
>> > > 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
>> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
>> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
>> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
>> > > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
>> > > 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
>> > > 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
>> > > 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
>> > > 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
>> > > 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
>> > > 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
>> > > 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
>> > > 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
>> > > 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
>> > > 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
>> > > 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
>> > > 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
>> > > 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
>> > > 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
>> > > 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
>> > > 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
>> > > 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
>> > > 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
>> > > 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
>> > > 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
>> > > 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
>> > > 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
>> > > 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
>> > > 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
>> > > 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
>> > > 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
>> > > 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
>> > > 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
>> > > 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
>> > > 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
>> > > 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
>> > > 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
>> > > 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
>> > > 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
>> > > c(729.2669944,
>> > > 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
>> > > 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
>> > > 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
>> > > 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
>> > > 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
>> > > 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
>> > > 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
>> > > 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
>> > > 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
>> > > 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
>> > > 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
>> > > 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
>> > > 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
>> > > 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
>> > > 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
>> > > 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
>> > > 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
>> > > 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
>> > > 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
>> > > 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
>> > > 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
>> > > 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
>> > > 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
>> > > 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
>> > > 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
>> > > 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
>> > > 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
>> > > 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
>> > > 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
>> > > 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
>> > > 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
>> > > 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
>> > > 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
>> > > 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
>> > > 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
>> > > 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
>> > > 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
>> > > 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
>> > > 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
>> > > 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
>> > > 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK",
>> > "RingNummerMan",
>> > > "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
>> > > "ManipulatieOuders",
>> > > "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
>> > > "NestkastNummer",
>> > > "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
>> > c(NA,
>> > > -99L))
>> > >
>> > >
>> > > #Below is the code I made to run my analyses
>> > > XO<- matrix( 0,6, 76, byrow=TRUE);XO  #I first made a matrix to store my
>> > > results in
>> > > names(ao)
>> > > ao$NestkastNummer
>> > > b<-c(77:99)
>> > > abo<-ao$NestkastNummer[-b];abo   #removed values that were NA
>> > > rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
>> > > "adj_pop_avg", "ind_pop_dif")
>> > > colnames(XO) = c((abo))
>> > > ncol(XO)
>> > > names(ao)
>> > > t <- ao$COR_LOC;t
>> > > i <- c(77:99)
>> > > ti <- t[-i];ti
>> > > XO[1,] = c(ti);XO  #assigned values from data frame to the matrix
>> > >
>> > > ### average difference b/n neighbours for each individual
>> > > XO["avg", "176"]<- mean(abs((XO[1,"176"])-XO[1,c("140","162","713")]))
>> > > XO["avg", "124"]<-
>> > > mean(abs((XO[1,"124"])-XO[1,c("113","64","128","172","130","117")]))
>> > > XO["avg", "51"]<- mean(abs((XO[1,"51"])-XO[1,c("74")]))
>> > > XO["avg", "717"]<-
>> > mean(abs((XO[1,"717"])-XO[1,c("34","707","704","718")]))
>> > > XO["avg", "54"]<- mean(abs((XO[1,"54"])-XO[1,c("73","94")]))
>> > > XO["avg", "19"]<- mean(abs((XO[1,"19"])-XO[1,c("15","14")]))
>> > > XO["avg", "11"]<- mean(abs((XO[1,"11"])-XO[1,c("22","23","9")]))
>> > > XO["avg", "42"]<-
>> > > mean(abs((XO[1,"42"])-XO[1,c("23","79","80","39","25","9")]))
>> > > XO["avg", "90"]<-
>> > mean(abs((XO[1,"90"])-XO[1,c("91","97","109","88","84")]))
>> > > XO["avg", "9"]<- mean(abs((XO[1,"9"])-XO[1,c("11","23","42","25","8")]))
>> > > XO["avg", "713"]<-
>> > mean(abs((XO[1,"713"])-XO[1,c("715","719","710","176")]))
>> > > XO["avg", "82"]<- mean(abs((XO[1,"82"])-XO[1,c("81","91","84","86")]))
>> > > XO["avg", "709"]<-
>> > > mean(abs((XO[1,"709"])-XO[1,c("36","86","88","710","718","707","35")]))
>> > > XO["avg", "2"]<- mean(abs((XO[1,"2"])-XO[1,c("3","31")]))
>> > > XO["avg", "39"]<-
>> > > mean(abs((XO[1,"39"])-XO[1,c("25","42","80","81","86","38","28","6")]))
>> > > XO["avg", "86"]<-
>> > > mean(abs((XO[1,"86"])-
>> XO[1,c("38","39","81","82","84","88","709","36")]))
>> > > XO["avg", "16"]<- mean(abs((XO[1,"16"])-XO[1,c("15")]))
>> > > XO["avg", "710"]<-
>> > > mean(abs((XO[1,"710"])-XO[1,c("709","88","713","719","718")]))
>> > > XO["avg", "93"]<-
>> > > mean(abs((XO[1,"93"])-XO[1,c("185","94","95","111","97","91")]))
>> > > XO["avg", "94"]<-
>> > mean(abs((XO[1,"94"])-XO[1,c("73","54","95","93","185")]))
>> > > XO["avg", "163"]<-
>> > mean(abs((XO[1,"163"])-XO[1,c("133","164","168","162")]))
>> > > XO["avg", "14"]<- mean(abs((XO[1,"14"])-XO[1,c("15","19")]))
>> > > XO["avg", "170"]<-
>> > mean(abs((XO[1,"170"])-XO[1,c("130","164","169","168")]))
>> > > XO["avg", "718"]<-
>> > > mean(abs((XO[1,"718"])-XO[1,c("707","709","710","719","704")]))
>> > > XO["avg", "79"]<-
>> > > mean(abs((XO[1,"79"])-XO[1,c("23","22","185","81","80","42")]))
>> > > XO["avg", "715"]<- mean(abs((XO[1,"715"])-XO[1,c("716","713")]))
>> > > XO["avg", "130"]<-
>> > > mean(abs((XO[1,"130"])-XO[1,c("124","172","170","164","133","117")]))
>> > > XO["avg", "133"]<-
>> > > mean(abs((XO[1,"133"])-XO[1,c("117","130","164","163","162","140")]))
>> > > XO["avg", "57"]<- mean(abs((XO[1,"57"])-XO[1,c("95","111")]))
>> > > XO["avg", "25"]<- mean(abs((XO[1,"25"])-
>> XO[1,c("8","9","42","80","39")]))
>> > > XO["avg", "128"]<-
>> > mean(abs((XO[1,"128"])-XO[1,c("124","64","127","172")]))
>> > > XO["avg", "164"]<-
>> > > mean(abs((XO[1,"164"])-XO[1,c("130","170","169","168","163","133")]))
>> > > XO["avg", "162"]<-
>> > mean(abs((XO[1,"162"])-XO[1,c("176","140","133","163")]))
>> > > XO["avg", "15"]<- mean(abs((XO[1,"15"])-XO[1,c("16","19","14")]))
>> > > XO["avg", "60"]<- mean(abs((XO[1,"60"])-XO[1,c("62","68","113")]))
>> > > XO["avg", "172"]<-
>> > mean(abs((XO[1,"172"])-XO[1,c("124","128","127","130")]))
>> > > XO["avg", "91"]<-
>> > > mean(abs((XO[1,"91"])-XO[1,c("185","93","97","90","84","82","81")]))
>> > > XO["avg", "31"]<- mean(abs((XO[1,"31"])-
>> XO[1,c("2","3","36","35","34")]))
>> > > XO["avg", "73"]<- mean(abs((XO[1,"73"])-XO[1,c("74","54","94","185")]))
>> > > XO["avg", "97"]<-
>> > > mean(abs((XO[1,"97"])-XO[1,c("91","93","111","109","90")]))
>> > > XO["avg", "111"]<-
>> > > mean(abs((XO[1,"111"])-XO[1,c("95","57","68","101","109","97","93")]))
>> > > XO["avg", "64"]<- mean(abs((XO[1,"64"])-
>> XO[1,c("113","62","128","124")]))
>> > > XO["avg", "74"]<- mean(abs((XO[1,"74"])-XO[1,c("51","73","185")]))
>> > > XO["avg", "95"]<- mean(abs((XO[1,"95"])-XO[1,c("94","57","111","93")]))
>> > > XO["avg", "704"]<-
>> > mean(abs((XO[1,"704"])-XO[1,c("719","718","707","717")]))
>> > > XO["avg", "148"]<- mean(abs((XO[1,"148"])-XO[1,c("150")]))
>> > > XO["avg", "36"]<-
>> > > mean(abs((XO[1,"36"])-XO[1,c("28","38","86","709","707","35","3")]))
>> > > XO["avg", "80"]<-
>> > mean(abs((XO[1,"80"])-XO[1,c("42","79","81","39","25")]))
>> > > XO["avg", "8"]<- mean(abs((XO[1,"8"])-XO[1,c("9","25")]))
>> > > XO["avg", "68"]<-
>> > > mean(abs((XO[1,"68"])-XO[1,c("111","60","113","117","101")]))
>> > > XO["avg", "105"]<-
>> > mean(abs((XO[1,"105"])-XO[1,c("88","109","101","716")]))
>> > > XO["avg", "22"]<- mean(abs((XO[1,"22"])-XO[1,c("11","79","23")]))
>> > > XO["avg", "716"]<- mean(abs((XO[1,"716"])-XO[1,c("88","105","715")]))
>> > > XO["avg", "127"]<- mean(abs((XO[1,"127"])-XO[1,c("128","172")]))
>> > > XO["avg", "88"]<-
>> > >
>> > mean(abs((XO[1,"88"])-
>> XO[1,c("86","84","90","109","105","716","710","709")]))
>> > > XO["avg", "81"]<-
>> > > mean(abs((XO[1,"81"])-XO[1,c("80","79","185","91","82","86","39")]))
>> > > XO["avg", "140"]<-
>> > mean(abs((XO[1,"140"])-XO[1,c("117","133","162","176")]))
>> > > XO["avg", "169"]<- mean(abs((XO[1,"169"])-XO[1,c("164","170","168")]))
>> > > XO["avg", "109"]<-
>> > > mean(abs((XO[1,"109"])-XO[1,c("90","97","111","101","105","88")]))
>> > > XO["avg", "719"]<-
>> > mean(abs((XO[1,"719"])-XO[1,c("718","710","713","704")]))
>> > > XO["avg", "35"]<-
>> > > mean(abs((XO[1,"35"])-XO[1,c("36","709","707","34","31","3")]))
>> > > XO["avg", "185"]<-
>> > > mean(abs((XO[1,"185"])-XO[1,c("79","74","73","94","93","91","81")]))
>> > > XO["avg", "6"]<- mean(abs((XO[1,"6"])-XO[1,c("39","28","3")]))
>> > > XO["avg", "34"]<- mean(abs((XO[1,"34"])-
>> XO[1,c("31","35","707","717")]))
>> > > XO["avg", "707"]<-
>> > > mean(abs((XO[1,"707"])-
>> XO[1,c("34","35","36","709","718","717","704")]))
>> > > XO["avg", "101"]<-
>> > > mean(abs((XO[1,"101"])-XO[1,c("105","109","111","68","113","117")]))
>> > > XO["avg", "38"]<- mean(abs((XO[1,"38"])-XO[1,c("39","86","36","28")]))
>> > > XO["avg", "28"]<- mean(abs((XO[1,"28"])-
>> XO[1,c("6","39","38","36","3")]))
>> > > XO["avg", "84"]<-
>> > mean(abs((XO[1,"84"])-XO[1,c("82","91","90","88","86")]))
>> > > XO["avg", "113"]<-
>> > > mean(abs((XO[1,"113"])-XO[1,c("68","60","62","64","124","117","101")]))
>> > > XO["avg", "62"]<- mean(abs((XO[1,"62"])-XO[1,c("60","64","113")]))
>> > > XO["avg", "168"]<-
>> > mean(abs((XO[1,"168"])-XO[1,c("170","169","164","163")]))
>> > > XO["avg", "23"]<-
>> > mean(abs((XO[1,"23"])-XO[1,c("9","11","22","79","42")]))
>> > > XO["avg", "3"]<-
>> > mean(abs((XO[1,"3"])-XO[1,c("6","28","36","35","31","2")]))
>> > > XO["avg", "117"]<-
>> > >
>> > mean(abs((XO[1,"117"])-
>> XO[1,c("101","113","124","130","133","140","68")]))
>> > > XO["avg", "150"]<- mean(abs((XO[1,"150"])-XO[1,c("148")]))
>> > > XO["pop_size",] <- 76
>> > > XO["pop_avg_score",]<- mean(XO["EB_score",])
>> > > for (i in XO){
>> > >   XO["adj_pop_avg",] <-
>> > >
>> > ((XO["pop_avg_score",])*(XO["pop_size",])-
>> (XO["EB_score",]))/((XO["pop_size",]-1))
>> > >   #here I ran a loop to get info
>> > >   XO["ind_pop_dif",] <-abs((XO["EB_score",]-XO["adj_pop_avg",]))}
>> > > t.test(XO["avg",], XO["ind_pop_dif",], paired=TRUE)
>> > > XO
>> > > XO<-rbind(XO,0)
>> > > rownames(XO)<-c("EB_score","avg","pop_size","pop_avg_score",
>> > "adj_pop_avg",
>> > > "ind_pop_dif", "non_nei")
>> > > XO["non_nei",]<-0
>> > > rowMeans(XO[,1:76])
>> > >
>> > > #This is the average observed discrepancy from individuals to neighbours
>> > > #IOW on average how different is a focal bird in this year different from
>> > > its neighbours
>> > > obso=mean(XO["avg",])
>> > > print(paste("Observed=", obso))
>> > > XY[15,1]<-round(obso, digits=4)
>> > >
>> > >
>> > > #This is the code I previously posted to find the difference in scores
>> > > between a single subject and its non-neighbours
>> > > o<-(ao[,c(13,5)])
>> > > o<-na.omit(o)
>> > > o<-o[!o$NestkastNummer %in% c(176,140,162,713),]
>> > > XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))
>> > >
>> > >
>> > > Best,
>> > >
>> > > Monaly.
>> > >
>> > >
>> > > On Thu, May 22, 2014 at 5:08 PM, John Kane <[hidden email]>
>> wrote:
>> > >
>> > > > Re dput() etc
>> > > > https://github.com/hadley/devtools/wiki/Reproducibility
>> > > >
>> > > >
>> > http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-
>> reproducible-example
>> > > >
>> > > > What dput() does is take your data and ouput it in an ascii format that
>> > > > let's the reader here create an exact duplicate of your database.
>> > > >
>> > > > R is not WYSIWYG. Often what you see on the screen does not tell the
>> > whole
>> > > > tale. R supports a number of different data types: vectors, matrices,
>> > > > data.frames, lists, arrays and others. This site gives a useful though
>> > not
>> > > > complete summary of many data types
>> > > > http://www.statmethods.net/input/datatypes.html. When you have
>> just
>> > > > created a new data set, or even when working with one that you have
>> not
>> > > > worked with in some time it is a good idea to do a str() and class()
>> > on the
>> > > > data object just to be sure that you are working with the data types
>> > you
>> > > > think you have. What looks like a column of numbers in a data.frame
>> may
>> > > > actually be a set of factors or a set of character (text) data and
>> > you're
>> > > > left wondering why multiplying it by some number is not working.
>> > > >
>> > > > Here is a short example to illustrate. Just copy and paste in the code
>> > > >  dat1  <- data.frame(aa = as.factor(1:5), bb = 1:5)
>> > > > dat1 # data looks identical on the screen
>> > > > 5*dat1[,"aa"]  # oops
>> > > > 5*dat1[, "bb"] # okay
>> > > > str(dat1)
>> > > >
>> > > >
>> > > > John Kane
>> > > > Kingston ON Canada
>> > > >
>> > > >
>> > > > > -----Original Message-----
>> > > > > From: [hidden email]
>> > > > > Sent: Thu, 22 May 2014 16:31:39 +0100
>> > > > > To: [hidden email], [hidden email]
>> > > > > Subject: Re: [R] subsetting to exclude different values for each
>> > subject
>> > > > > in study
>> > > > >
>> > > > > Hi,
>> > > > >
>> > > > > Sorry I'm fairly new to R and I don't really understand using dput(),
>> > > > > when
>> > > > > you say reproducible example do you mean the code with the
>> output?
>> > > > >
>> > > > > Best,
>> > > > >
>> > > > > Monaly.
>> > > > >
>> > > > >
>> > > > > On Thu, May 22, 2014 at 4:03 PM, arun <[hidden email]>
>> > wrote:
>> > > > >
>> > > > >> Hi,
>> > > > >>
>> > > > >> It would be helpful if you provide a reproducible example using
>> > ?dput().
>> > > > >>
>> > > > >> A.K.
>> > > > >>
>> > > > >>
>> > > > >>
>> > > > >>
>> > > > >> On Thursday, May 22, 2014 10:15 AM, Monaly Mistry
>> > > > >> <[hidden email]>
>> > > > >> wrote:
>> > > > >> Hi,
>> > > > >>
>> > > > >> I've written a code to determine the difference in score for a
>> > single
>> > > > >> subject and its non-neighbours
>> > > > >>
>> > > > >> o<-(ao[,c(13,5)]) ##this is the table with the relevant information
>> > > > >> o<-na.omit(o)  ##omitted data with NA
>> > > > >> o<-o[!o$NestkastNummer %in% c(176,140,162,713),] ##removed
>> > neighbours
>> > > > >> XO[7,1]<-abs((XO[1,"176"]-(mean(o[,"COR_LOC"]))))  #difference
>> > between
>> > > > >> that
>> > > > >> individual and average non-neighbours scores
>> > > > >>
>> > > > >> Since each subject has a different number of non-neighbours I was
>> > > > >> wondering
>> > > > >> if there is an efficient way of writing the code, instead of
>> > writing the
>> > > > >> same code again and again (76 subjects) for each subject and its
>> > > > >> non-neighbours.
>> > > > >>
>> > > > >>
>> > > > >> Best,
>> > > > >>
>> > > > >> Monaly.
>> > > > >>
>> > > > >>     [[alternative HTML version deleted]]
>> > > > >>
>> > > > >> ______________________________________________
>> > > > >> [hidden email] 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.
>> > > > >>
>> > > > >>
>> > > > >
>> > > > >       [[alternative HTML version deleted]]
>> > > > >
>> > > > > ______________________________________________
>> > > > > [hidden email] 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.
>> > > >
>> > > >
>> __________________________________________________________
>> __
>> > > > FREE ONLINE PHOTOSHARING - Share your photos online with your
>> friends
>> > and
>> > > > family!
>> > > > Visit http://www.inbox.com/photosharing to find out more!
>> > > >
>> > > >
>> > > >
>> > >
>> > >     [[alternative HTML version deleted]]
>> > >
>> > > ______________________________________________
>> > > [hidden email] 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.
>> >
>>
>>       [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> [hidden email] 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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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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Re: subsetting to exclude different values for each subject in study

arun kirshna
Hi,
Sorry, there is a mistake. XO[2,] should be:
XO[2,] <-  sapply(seq_along(col.tri.nb), function(i){ind1 <- as.character(ind[i]); ind2 <- as.character(ind[col.tri.nb[[i]]]); mean(abs(XO[1,ind1]-XO[1,ind2]))} )
A.K.






On Friday, May 23, 2014 12:56 PM, arun <[hidden email]> wrote:

Hi Monaly,
May be this helps:
b<- 77:99
ao1 <- ao[-b,]
##Your code:

XO<- matrix( 0,6, 76, byrow=TRUE);XO
abo<-ao$NestkastNummer[-b];abo  #removed values that were NA
rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
"adj_pop_avg", "ind_pop_dif")
colnames(XO) = abo
t <- ao$COR_LOC;t
i <- c(77:99)
ti <- t[-i];ti
XO[1,] = c(ti);XO
 

library(deldir)
library(spdep)
mat <- cbind(lat=ao1$lat_xm, long=ao1$long_ym)
library(spdep)
 coords <- coordinates(mat)
ind <- ao1$NestkastNummer

col.tri.nb <- tri2nb(coords, row.names=ind)
 lapply(col.tri.nb,function(x) ind[x])[1:5] ###
[[1]]
[1] 713 715 162 148 140 117

[[2]]
[1] 130 128 172  64 113 117

[[3]]
[1] 54 19 16 73 74

[[4]]
[1]   2  31 704  34 707

[[5]]
[1] 51 94 57 73 62

XO[2,] <- sapply(seq_along(col.tri.nb),function(i) mean(abs(ind[i]-ind[col.tri.nb[[i]]])))

A.K.



On Friday, May 23, 2014 7:17 AM, Monaly Mistry <[hidden email]> wrote:



Hi Arun and Frede,

So the dput() is below (it's the same data file as before), but below that is the code I used to make the tessellation.  Thanks for your help.

> dput(ao)
structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L, 
22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L, 
30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L, 
36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L, 
35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L, 
36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L, 
36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L, 
35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L, 
35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L, 
35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L, 
36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L, 
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 
49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L, 
65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L, 
83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L, 
32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L, 
91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371", 
"AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504", 
"AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491", 
"AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701", 
"AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965", 
"AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996", 
"AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387", 
"AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074", 
"AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067", 
"AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106", 
"AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178", 
"AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252", 
"AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275", 
"AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338", 
"AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520", 
"AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737", 
"AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873", 
"AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951", 
"AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009", 
"AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046", 
"AU...15049", "AU...15505"), class = "factor"), year_score_taken = c(2006L, 
2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L, 
2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L, 
2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L, 
2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L, 
2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L, 
2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L, 
2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404, 
13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718, 
16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945, 
12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718, 
18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588, 
34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234, 
22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616, 
23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718, 
40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718, 
7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188, 
28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718, 
39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L, 
344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L, 
352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L, 
377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L, 
378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L, 
378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L, 
378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L, 
377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L, 
377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L, 
373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L, 
377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L, 
400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L, 
356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L, 
378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L, 
400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L, 
1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L, 
13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L, 
10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L, 
12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L, 
21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L, 
11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L, 
11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L, 
5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L, 
55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L, 
55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L, 
55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L, 
55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L, 
55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L, 
55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L, 
55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L, 
55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L, 
55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L, 
55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L, 
55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L, 
55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L, 
55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L, 
55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L, 
55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L, 
55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L, 
55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L, 
55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L, 
55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L, 
55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L, 
55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L, 
55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L, 
55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L, 
55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L, 
55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L, 
19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L, 
93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L, 
25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L, 
64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L, 
127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L, 
101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L, 
183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L, 
17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm = c(729.2669944, 
1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688, 
198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345, 
388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924, 
308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656, 
267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672, 
1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712, 
952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424, 
456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678, 
1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113, 
135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177, 
237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193, 
770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334, 
437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095, 
177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528, 
1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674, 
325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545, 
423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216, 
709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308, 
659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721, 
722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022, 
744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671, 
1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164, 
887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293, 
1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291, 
1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801, 
636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835, 
485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869, 
887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864, 
783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057, 
731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549, 
1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063, 
480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401, 
1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502, 
816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948, 
415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577, 
1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479, 
197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715, 
597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634, 
433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272, 
796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan", 
"year_score_taken", "COR_LOC", "IndividuID", "BroedJaar", "ManipulatieOuders", 
"LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID", "NestkastNummer", 
"lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names = c(NA, 
-99L))


#Code for  tessellation
library(deldir)

ao= read.table("C:/Users/Monaly/Desktop/2012_malenest.txt", header=TRUE)

a29= deldir(ao$lat_xm, ao$long_ym)

a30=tile.list(a29)

plot(a30, close=TRUE, main="2012 Male Nest", xlab="latitude (m)", ylab="longitude (m)", wpoints="real", verbose=FALSE,num=TRUE, rw=c(0, 1200, 0, 2000))

text(ao$lat_xm, ao$long_ym,col=c(2,1,4),labels=round(ao$NestkastNummer, 3), pos=2, offset=0.2, cex=0.7)  #this was to identify the points

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Re: subsetting to exclude different values for each subject in study

Monaly Mistry
Hi Arun,

Thank you for your help,  I have a few questions though if you don't mind.
I'm a bit confused about the following 2 lines of code:
col.tri.nb <- tri2nb(coords, row.names=ind)
 lapply(col.tri.nb,function(x) ind[x])[1:5]
## from what I understand in the first line determines the neighbouring
individuals, while in the second line it calls for the output of neighbours
for the first 5 individuals.
Also for the last line of code that you resent I don't really understand
what it is running?

Best,

Monaly.


On Sat, May 24, 2014 at 2:41 AM, arun <[hidden email]> wrote:

> Hi,
> Sorry, there is a mistake. XO[2,] should be:
> XO[2,] <-  sapply(seq_along(col.tri.nb), function(i){ind1 <-
> as.character(ind[i]); ind2 <- as.character(ind[col.tri.nb[[i]]]);
> mean(abs(XO[1,ind1]-XO[1,ind2]))} )
> A.K.
>
>
>
>
>
>
> On Friday, May 23, 2014 12:56 PM, arun <[hidden email]> wrote:
>
> Hi Monaly,
> May be this helps:
> b<- 77:99
> ao1 <- ao[-b,]
> ##Your code:
>
> XO<- matrix( 0,6, 76, byrow=TRUE);XO
> abo<-ao$NestkastNummer[-b];abo  #removed values that were NA
> rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
> "adj_pop_avg", "ind_pop_dif")
> colnames(XO) = abo
> t <- ao$COR_LOC;t
> i <- c(77:99)
> ti <- t[-i];ti
> XO[1,] = c(ti);XO
>
>
> library(deldir)
> library(spdep)
> mat <- cbind(lat=ao1$lat_xm, long=ao1$long_ym)
> library(spdep)
>  coords <- coordinates(mat)
> ind <- ao1$NestkastNummer
>
> col.tri.nb <- tri2nb(coords, row.names=ind)
>  lapply(col.tri.nb,function(x) ind[x])[1:5] ###
> [[1]]
> [1] 713 715 162 148 140 117
>
> [[2]]
> [1] 130 128 172  64 113 117
>
> [[3]]
> [1] 54 19 16 73 74
>
> [[4]]
> [1]   2  31 704  34 707
>
> [[5]]
> [1] 51 94 57 73 62
>
> XO[2,] <- sapply(seq_along(col.tri.nb),function(i)
> mean(abs(ind[i]-ind[col.tri.nb[[i]]])))
>
> A.K.
>
>
>
> On Friday, May 23, 2014 7:17 AM, Monaly Mistry <[hidden email]>
> wrote:
>
>
>
> Hi Arun and Frede,
>
> So the dput() is below (it's the same data file as before), but below that
> is the code I used to make the tessellation.  Thanks for your help.
>
> > dput(ao)
> structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L,
> 22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L,
> 30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L,
> 36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L,
> 35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L,
> 36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L,
> 36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L,
> 35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L,
> 35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L,
> 35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L,
> 36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L,
> 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
> 16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L,
> 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L,
> 49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L,
> 65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L,
> 83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L,
> 32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L,
> 91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371",
> "AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504",
> "AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491",
> "AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701",
> "AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965",
> "AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996",
> "AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387",
> "AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074",
> "AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067",
> "AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106",
> "AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178",
> "AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252",
> "AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275",
> "AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338",
> "AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520",
> "AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737",
> "AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873",
> "AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951",
> "AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009",
> "AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046",
> "AU...15049", "AU...15505"), class = "factor"), year_score_taken =
> c(2006L,
> 2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L,
> 2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L,
> 2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L,
> 2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L,
> 2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L,
> 2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
> 2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L,
> 2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L,
> 2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404,
> 13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718,
> 16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945,
> 12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718,
> 18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588,
> 34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234,
> 22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616,
> 23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718,
> 40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718,
> 7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188,
> 28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718,
> 39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L,
> 344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L,
> 352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L,
> 377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L,
> 378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L,
> 378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L,
> 378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L,
> 377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L,
> 377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L,
> 373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L,
> 377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L,
> 400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L,
> 356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L,
> 378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L,
> 400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
> 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders =
> c(0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L,
> 1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L,
> 13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L,
> 10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L,
> 12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L,
> 21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L,
> 11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L,
> 11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L,
> 5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L,
> 4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L,
> 55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L,
> 55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L,
> 55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L,
> 55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L,
> 55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L,
> 55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L,
> 55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L,
> 55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L,
> 55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L,
> 55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L,
> 55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L,
> 55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L,
> 55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L,
> 55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L,
> 55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L,
> 55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L,
> 55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L,
> 55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L,
> 55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L,
> 55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L,
> 55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L,
> 55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L,
> 55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L,
> 55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L,
> 55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L,
> 19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L,
> 93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L,
> 25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L,
> 64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L,
> 127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L,
> 101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L,
> 183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L,
> 17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm =
> c(729.2669944,
> 1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688,
> 198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345,
> 388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924,
> 308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656,
> 267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672,
> 1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712,
> 952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424,
> 456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678,
> 1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113,
> 135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177,
> 237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193,
> 770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334,
> 437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095,
> 177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528,
> 1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674,
> 325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545,
> 423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216,
> 709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308,
> 659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721,
> 722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022,
> 744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671,
> 1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164,
> 887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293,
> 1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291,
> 1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801,
> 636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835,
> 485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869,
> 887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864,
> 783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057,
> 731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549,
> 1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063,
> 480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401,
> 1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502,
> 816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948,
> 415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577,
> 1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479,
> 197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715,
> 597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634,
> 433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272,
> 796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359,
> 23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan",
> "year_score_taken", "COR_LOC", "IndividuID", "BroedJaar",
> "ManipulatieOuders",
> "LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID",
> "NestkastNummer",
> "lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names =
> c(NA,
> -99L))
>
>
> #Code for  tessellation
> library(deldir)
>
> ao= read.table("C:/Users/Monaly/Desktop/2012_malenest.txt", header=TRUE)
>
> a29= deldir(ao$lat_xm, ao$long_ym)
>
> a30=tile.list(a29)
>
> plot(a30, close=TRUE, main="2012 Male Nest", xlab="latitude (m)",
> ylab="longitude (m)", wpoints="real", verbose=FALSE,num=TRUE, rw=c(0, 1200,
> 0, 2000))
>
> text(ao$lat_xm, ao$long_ym,col=c(2,1,4),labels=round(ao$NestkastNummer,
> 3), pos=2, offset=0.2, cex=0.7)  #this was to identify the points
>

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Re: subsetting to exclude different values for each subject in study

arun kirshna
Hi Monaly,

According to the description of ?tri2nb
The function uses the ‘deldir’ package to convert a matrix of
     two-dimensional coordinates into a neighbours list of class ‘nb’
     with a list of integer vectors containing neighbour region number
     ids.


So, col.tri.nb is a list of length 76.

 str(col.tri.nb)
List of 76
 $ : int [1:6] 11 26 33 46 57 75
 $ : int [1:6] 27 31 36 42 70 75
---------------------------

 lst1 <- lapply(col.tri.nb,function(x) ind[x])
 lst1[1:5] #returns 1st five elements
##Regarding the last piece of code:
#Check the output of

res <- sapply(seq_along(col.tri.nb), function(i) {
    ind1 <- as.character(ind[i])
    ind2 <- as.character(ind[col.tri.nb[[i]]])
    XOind1 <- XO[1, ind1]
    XOind2 <- XO[1, ind2]
    cat(" list element=", i, "\n", " NestkastNummer=", ind1, "\n", " Neighbouring NestkastNummer=",
        ind2, "\n", " EBScore=", XOind1, "\n", " EBScore Neighbour element=", XOind2,
        "\n", " avg=", mean(abs(XOind1 - XOind2)), sep = " ", "\n")
    mean(abs(XOind1 - XOind2))
})



A.K.




On Tuesday, May 27, 2014 7:20 AM, Monaly Mistry <[hidden email]> wrote:



Hi Arun,

Thank you for your help,  I have a few questions though if you don't mind. I'm a bit confused about the following 2 lines of code:
col.tri.nb <- tri2nb(coords, row.names=ind)
 lapply(col.tri.nb,function(x) ind[x])[1:5]

## from what I understand in the first line determines the neighbouring individuals, while in the second line it calls for the output of neighbours for the first 5 individuals.
Also for the last line of code that you resent I don't really understand what it is running?

Best,

Monaly.



On Sat, May 24, 2014 at 2:41 AM, arun <[hidden email]> wrote:

Hi,

>Sorry, there is a mistake. XO[2,] should be:
>XO[2,] <-  sapply(seq_along(col.tri.nb), function(i){ind1 <- as.character(ind[i]); ind2 <- as.character(ind[col.tri.nb[[i]]]); mean(abs(XO[1,ind1]-XO[1,ind2]))} )
>A.K.
>
>
>
>
>
>
>
>On Friday, May 23, 2014 12:56 PM, arun <[hidden email]> wrote:
>
>Hi Monaly,
>May be this helps:
>b<- 77:99
>ao1 <- ao[-b,]
>##Your code:
>
>XO<- matrix( 0,6, 76, byrow=TRUE);XO
>abo<-ao$NestkastNummer[-b];abo  #removed values that were NA
>rownames(XO) = c("EB_score","avg","pop_size","pop_avg_score",
>"adj_pop_avg", "ind_pop_dif")
>colnames(XO) = abo
>t <- ao$COR_LOC;t
>i <- c(77:99)
>ti <- t[-i];ti
>XO[1,] = c(ti);XO

>
>library(deldir)
>library(spdep)
>mat <- cbind(lat=ao1$lat_xm, long=ao1$long_ym)
>library(spdep)
> coords <- coordinates(mat)
>ind <- ao1$NestkastNummer
>
>col.tri.nb <- tri2nb(coords, row.names=ind)
> lapply(col.tri.nb,function(x) ind[x])[1:5] ###
>[[1]]
>[1] 713 715 162 148 140 117
>
>[[2]]
>[1] 130 128 172  64 113 117
>
>[[3]]
>[1] 54 19 16 73 74
>
>[[4]]
>[1]   2  31 704  34 707
>
>[[5]]
>[1] 51 94 57 73 62
>
>XO[2,] <- sapply(seq_along(col.tri.nb),function(i) mean(abs(ind[i]-ind[col.tri.nb[[i]]])))
>
>A.K.
>
>
>
>On Friday, May 23, 2014 7:17 AM, Monaly Mistry <[hidden email]> wrote:
>
>
>
>Hi Arun and Frede,
>
>So the dput() is below (it's the same data file as before), but below that is the code I used to make the tessellation.  Thanks for your help.
>
>> dput(ao)
>structure(list(num = 1:99, FORM_CHK = c(20870L, 22018L, 30737L, 
>22010L, 22028L, 36059L, 36063L, 36066L, 30587L, 30612L, 36056L, 
>30376L, 35153L, 30435L, 30536L, 30486L, 30475L, 36053L, 36048L, 
>36076L, 36045L, 36065L, 35772L, 36949L, 35702L, 36894L, 36080L, 
>35542L, 35457L, 35533L, 36042L, 36925L, 36827L, 36008L, 35817L, 
>36350L, 35985L, 35973L, 35801L, 36639L, 35810L, 35812L, 35807L, 
>36351L, 35967L, 35944L, 37006L, 36345L, 36062L, 36077L, 35802L, 
>35984L, 36043L, 35769L, 36360L, 36082L, 36071L, 36354L, 35771L, 
>35754L, 36295L, 35746L, 36064L, 35779L, 35751L, 35752L, 35785L, 
>35792L, 37011L, 36003L, 36040L, 36831L, 36031L, 36652L, 36992L, 
>36965L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
>NA, NA, NA, NA, NA, NA, NA, NA, NA), RingNummerMan = structure(c(1L, 
>2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
>16L, 17L, 19L, 22L, 23L, 24L, 25L, 26L, 27L, 29L, 30L, 31L, 34L, 
>35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 
>49L, 50L, 51L, 52L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 63L, 
>65L, 67L, 69L, 70L, 73L, 74L, 75L, 76L, 78L, 79L, 80L, 81L, 82L, 
>83L, 85L, 86L, 87L, 88L, 89L, 93L, 96L, 97L, 18L, 20L, 21L, 28L, 
>32L, 33L, 45L, 53L, 62L, 64L, 66L, 68L, 71L, 72L, 77L, 84L, 90L, 
>91L, 92L, 94L, 95L, 98L, 99L), .Label = c("AJ...75425", "AL...62371", 
>"AR...11060", "AR...29297", "AR...29307", "AR...29502", "AR...29504", 
>"AR...29507", "AR...30039", "AR...30085", "AR...30165", "AR...30491", 
>"AR...30563", "AR...30616", "AR...30652", "AR...30687", "AR...30701", 
>"AR...30927", "AR...30959", "AR...30963", "AR...30964", "AR...30965", 
>"AR...30966", "AR...30985", "AR...30988", "AR...40917", "AR...40996", 
>"AR...45735", "AR...45904", "AR...45928", "AR...47609", "AR...65387", 
>"AR...65479", "AR...65550", "AR...65629", "AR...65948", "AR...86074", 
>"AR...86521", "AR...86527", "AR...90061", "AR...90064", "AR...90067", 
>"AR...90077", "AR...90081", "AR...90098", "AR...90101", "AR...90106", 
>"AR...90112", "AR...90133", "AR...90155", "AR...90176", "AR...90178", 
>"AR...90180", "AR...90187", "AR...90212", "AR...90247", "AR...90252", 
>"AR...90256", "AR...90258", "AR...90269", "AR...90272", "AR...90275", 
>"AR...90294", "AR...90298", "AR...90300", "AR...90337", "AR...90338", 
>"AR...90367", "AR...90397", "AR...90410", "AR...90463", "AR...90520", 
>"AR...90544", "AR...90556", "AR...90678", "AR...90712", "AR...90737", 
>"AR...90744", "AR...90829", "AR...90862", "AR...90863", "AR...90873", 
>"AR...90880", "AR...90892", "AR...90898", "AR...90945", "AR...90951", 
>"AR...90965", "AR...90970", "AR...90972", "AU...15008", "AU...15009", 
>"AU...15027", "AU...15032", "AU...15036", "AU...15038", "AU...15046", 
>"AU...15049", "AU...15505"), class = "factor"), year_score_taken = c(2006L, 
>2008L, 2009L, 2008L, 2008L, 2011L, 2011L, 2011L, 2009L, 2009L, 
>2011L, 2009L, 2010L, 2009L, 2009L, 2009L, 2009L, 2011L, 2011L, 
>2011L, 2011L, 2011L, 2011L, 2012L, 2011L, 2012L, 2011L, 2010L, 
>2010L, 2010L, 2011L, 2012L, 2012L, 2011L, 2011L, 2012L, 2011L, 
>2011L, 2011L, 2012L, 2011L, 2011L, 2011L, 2012L, 2011L, 2011L, 
>2013L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L, 
>2012L, 2011L, 2012L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
>2011L, 2011L, 2011L, 2011L, 2013L, 2011L, 2011L, 2012L, 2011L, 
>2012L, 2012L, 2012L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
>NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), COR_LOC = c(15.13404, 
>13.88054, 30.0969, 19.09152, 16.88054, 14.15718, 39.15718, 16.15718, 
>16.13566, 23.07538, 39.15718, 24.56838, 12.13942, 21.4123, 19.06945, 
>12.33264, 32.48872, 30.15718, 37.15718, 37.15718, 49.15718, 22.15718, 
>18.50272, 23.69432, 24.9322, 47.29712, 41.15718, 21.47903, 38.6588, 
>34.99572, 28.15718, 13.08614, 16.71908, 22.68894, 19.2616, 15.96234, 
>22.83964, 13.89992, 14.2616, 18.17118, 24.2616, 22.2616, 13.2616, 
>23.96234, 24.89992, 24.05062, 10.20884, 6.96234, 13.15718, 17.15718, 
>40.2616, 21.83964, 20.15718, 39.50272, 26.81164, 20.3843, 14.15718, 
>7.96234, 19.50272, 40.74384, 5.7675, 42.95482, 29.15718, 18.32188, 
>28.74384, 37.74384, 22.32188, 25.32188, 18.20884, 14.68894, 22.15718, 
>39.71908, 18.2067, 15.1109, 15.61466, 47.4532, NA, NA, NA, NA, 
>NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
>NA, NA, NA), IndividuID = c(11394L, 15676L, 342518L, 344902L, 
>344909L, 377497L, 377499L, 377504L, 352003L, 351986L, 352260L, 
>352392L, 353800L, 353892L, 353949L, 354060L, 354074L, 377487L, 
>377490L, 377511L, 377513L, 377495L, 377297L, 357796L, 366326L, 
>378446L, 377518L, 358157L, 358730L, 366215L, 377519L, 378407L, 
>378453L, 377443L, 377358L, 377726L, 377422L, 377402L, 377341L, 
>378354L, 377350L, 377352L, 377347L, 378408L, 377396L, 377374L, 
>377774L, 377743L, 377500L, 377510L, 377342L, 377421L, 377786L, 
>377294L, 377836L, 378291L, 377508L, 378199L, 377296L, 377280L, 
>373000L, 373020L, 377496L, 377306L, 373025L, 377278L, 377310L, 
>377317L, 377337L, 377439L, 377450L, 377464L, 377478L, 400290L, 
>400361L, 400260L, 357889L, 377477L, 377298L, 400370L, 356930L, 
>356939L, 378115L, 377562L, 378018L, 377834L, 378290L, 378228L, 
>378268L, 378052L, 378103L, 377332L, 377514L, 400356L, 400357L, 
>400372L, 400259L, 400256L, 400354L), BroedJaar = c(2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 
>2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L), ManipulatieOuders = c(0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
>0L, 0L), LegBeginDag = c(11L, 15L, 15L, 13L, 8L, 26L, 15L, 16L, 
>1L, 3L, 4L, 9L, 13L, 20L, 11L, 2L, 9L, 13L, 31L, 1L, 12L, 8L, 
>13L, 7L, 10L, 11L, 17L, 10L, 11L, 19L, 20L, 13L, 14L, 24L, 17L, 
>10L, 8L, 29L, 7L, 26L, 10L, 15L, 2L, 6L, 8L, 13L, 1L, 5L, 12L, 
>12L, 15L, 19L, 10L, 1L, 5L, 13L, 6L, 5L, 16L, 2L, 2L, 30L, 10L, 
>21L, 8L, 19L, 8L, 27L, 3L, 8L, 14L, 18L, 17L, 7L, 4L, 10L, 13L, 
>11L, 31L, 25L, 23L, 7L, 7L, 7L, 8L, 3L, 14L, 14L, 15L, 5L, 10L, 
>11L, 18L, 1L, 31L, 3L, 8L, 20L, 14L), LegBeginMaand = c(4L, 4L, 
>5L, 4L, 5L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
>3L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 
>4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 
>4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 4L, 4L, 4L, 4L, 
>4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
>4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
>4L), broodinfo = c(55334L, 55325L, 55317L, 55349L, 55366L, 55303L, 
>55461L, 55528L, 55296L, 55297L, 55630L, 55567L, 55345L, 55444L, 
>55526L, 55571L, 55462L, 55346L, 55576L, 55577L, 55601L, 55300L, 
>55607L, 55634L, 55558L, 55633L, 55590L, 55594L, 55537L, 55466L, 
>55327L, 55603L, 55600L, 55302L, 55319L, 55609L, 55574L, 55310L, 
>55554L, 55582L, 55561L, 55320L, 55555L, 55578L, 55343L, 55331L, 
>55314L, 55560L, 55460L, 55551L, 55322L, 55306L, 55348L, 55589L, 
>55572L, 55565L, 55595L, 55606L, 55323L, 55635L, 55568L, 55614L, 
>55447L, 55312L, 55344L, 55321L, 55569L, 55309L, 55570L, 55562L, 
>55550L, 55605L, 55465L, 55445L, 55587L, 55332L, 55629L, 55613L, 
>55448L, 55632L, 55636L, 55531L, 55329L, 55597L, 55298L, 55596L, 
>55318L, 55608L, 55463L, 55532L, 55557L, 55536L, 55333L, 55533L, 
>55538L, 55637L, 55330L, 55326L, 55525L), BroedselID = c(55334L, 
>55325L, 55317L, 55349L, 55366L, 55303L, 55461L, 55528L, 55296L, 
>55297L, 55630L, 55567L, 55345L, 55444L, 55526L, 55571L, 55462L, 
>55346L, 55576L, 55577L, 55601L, 55300L, 55607L, 55634L, 55558L, 
>55633L, 55590L, 55594L, 55537L, 55466L, 55327L, 55603L, 55600L, 
>55302L, 55319L, 55609L, 55574L, 55310L, 55554L, 55582L, 55561L, 
>55320L, 55555L, 55578L, 55343L, 55331L, 55314L, 55560L, 55460L, 
>55551L, 55322L, 55306L, 55348L, 55589L, 55572L, 55565L, 55595L, 
>55606L, 55323L, 55635L, 55568L, 55614L, 55447L, 55312L, 55344L, 
>55321L, 55569L, 55309L, 55570L, 55562L, 55550L, 55605L, 55465L, 
>55445L, 55587L, 55332L, 55629L, 55613L, 55448L, 55632L, 55636L, 
>55531L, 55329L, 55597L, 55298L, 55596L, 55318L, 55608L, 55463L, 
>55532L, 55557L, 55536L, 55333L, 55533L, 55538L, 55637L, 55330L, 
>55326L, 55525L), NestkastNummer = c(176L, 124L, 51L, 717L, 54L, 
>19L, 11L, 42L, 90L, 9L, 713L, 82L, 709L, 2L, 39L, 86L, 16L, 710L, 
>93L, 94L, 163L, 14L, 170L, 718L, 79L, 715L, 130L, 133L, 57L, 
>25L, 128L, 164L, 162L, 15L, 60L, 172L, 91L, 31L, 73L, 97L, 111L, 
>64L, 74L, 95L, 704L, 148L, 36L, 80L, 8L, 68L, 105L, 22L, 716L, 
>127L, 88L, 81L, 140L, 169L, 109L, 719L, 35L, 185L, 6L, 34L, 707L, 
>101L, 38L, 28L, 84L, 113L, 62L, 168L, 23L, 3L, 117L, 150L, 705L, 
>183L, 7L, 714L, 720L, 49L, 144L, 153L, 12L, 143L, 56L, 171L, 
>17L, 50L, 77L, 55L, 175L, 52L, 58L, 722L, 145L, 125L, 32L), lat_xm = c(729.2669944, 
>1001.809576, 501.4865527, 105.2662516, 622.0842564, 313.4718688, 
>198.828763, 248.3819471, 466.4434076, 155.709257, 433.2482345, 
>388.4860969, 306.5590574, 14.98895776, 191.9843836, 309.4336924, 
>308.6123573, 351.526526, 606.8213156, 601.8249333, 912.0799656, 
>267.5461811, 1084.557939, 264.26089, 359.6713191, 488.4822672, 
>1018.578266, 915.707476, 773.276261, 171.4513083, 1084.831712, 
>952.5985963, 878.4741353, 288.3530553, 913.9963847, 1071.827424, 
>456.313756, 51.12730755, 582.6607182, 592.1059359, 740.3548678, 
>1042.875765, 476.8468377, 654.0474325, 276.375404, 877.6528113, 
>135.7921596, 300.9466765, 145.6480126, 829.1262723, 601.4827177, 
>237.6363065, 500.3230173, 1129.730741, 398.06821, 340.8493193, 
>770.4016222, 1051.63655, 571.7097287, 314.4300781, 117.5861334, 
>437.9708453, 95.41039954, 105.7453938, 235.5829892, 627.9704095, 
>177.0636713, 99.17481232, 396.6993402, 973.4739067, 1034.662528, 
>1046.77705, 221.278275, 27.24031031, 724.0652756, 942.6742674, 
>325.9970589, 261.933799, 116.7648206, 464.0478832, 532.6968545, 
>423.9399058, 656.8536222, 979.9076146, 221.2098377, 701.5473216, 
>709.8290013, 1120.559295, 345.5719307, 463.4318862, 429.6207308, 
>659.112262, 717.7684649, 533.3812884, 819.3388243, 600.9351721, 
>722.4910753, 1126.719223, 26.8297633), long_ym = c(385.4016022, 
>744.3388344, 1278.519267, 582.1054392, 1183.781188, 1313.545671, 
>1155.204087, 1008.093201, 812.6125238, 1045.899477, 474.135164, 
>887.4467064, 626.9169985, 700.9728169, 849.3068501, 799.1579293, 
>1418.180093, 598.1175046, 928.3664402, 1111.83807, 367.2768291, 
>1318.32705, 501.4891137, 542.5200518, 1095.7148, 552.6387801, 
>636.2573659, 479.9172936, 1057.018971, 980.7392501, 739.0014835, 
>485.8106446, 371.9470232, 1365.91848, 942.3769994, 664.2784869, 
>887.335514, 669.5046549, 1156.983212, 893.8960158, 933.9261864, 
>783.4794517, 1191.342439, 975.8466709, 453.8976828, 55.70866057, 
>731.2178331, 973.6227733, 1002.199869, 920.5827929, 678.1778549, 
>1141.415921, 578.9919757, 710.2019861, 738.8902861, 936.706063, 
>480.8068625, 454.8984371, 771.1368166, 510.940689, 680.7353401, 
>1087.041598, 895.6751282, 641.8171157, 573.7658194, 651.9358502, 
>816.2819528, 819.6178023, 828.7357905, 801.8266126, 856.9792948, 
>415.0906484, 1086.374437, 737.4447458, 559.866446, 0, 423.6526577, 
>1166.990753, 957.8330951, 562.8687158, 564.7590286, 1339.676479, 
>197.5933584, 132.099559, 1205.686591, 246.6303384, 1106.500715, 
>597.3391415, 1389.380609, 1312.878499, 1155.760068, 1152.090634, 
>433.6602223, 1252.833235, 1028.88666, 522.3937678, 151.7810272, 
>796.3780665, 631.3647851), avg_pop_eb = c(23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359, 23.57103359, 23.57103359, 23.57103359, 
>23.57103359, 23.57103359)), .Names = c("num", "FORM_CHK", "RingNummerMan", 
>"year_score_taken", "COR_LOC", "IndividuID", "BroedJaar", "ManipulatieOuders", 
>"LegBeginDag", "LegBeginMaand", "broodinfo", "BroedselID", "NestkastNummer", 
>"lat_xm", "long_ym", "avg_pop_eb"), class = "data.frame", row.names = c(NA, 
>-99L))
>
>
>#Code for  tessellation
>library(deldir)
>
>ao= read.table("C:/Users/Monaly/Desktop/2012_malenest.txt", header=TRUE)
>
>a29= deldir(ao$lat_xm, ao$long_ym)
>
>a30=tile.list(a29)
>
>plot(a30, close=TRUE, main="2012 Male Nest", xlab="latitude (m)", ylab="longitude (m)", wpoints="real", verbose=FALSE,num=TRUE, rw=c(0, 1200, 0, 2000))
>
>text(ao$lat_xm, ao$long_ym,col=c(2,1,4),labels=round(ao$NestkastNummer, 3), pos=2, offset=0.2, cex=0.7)  #this was to identify the points
>

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