In case anyone is still interested in my query, note that if there are
n total items to be split into g groups as evenly as possible, if we
define this as at most two different size groups whose size differs by
1, then:
if n = k*g + r, where 0 <= r < g,
then n = k*(g - r) + (k + 1)*r .
i.e. g-r groups of size k and r groups of size k+1
So using R's modular arithmetic operators, which are handy to know
about, we have:
r = n %% g and k = n %/% g .
(and note that you should disregard my previous stupid remark about
numerical analysis).
Cheers,
Bert
On Sat, Sep 4, 2021 at 3:34 PM Bert Gunter <bgunter.4...@gmail.com> wrote:
>
> I have a more general problem for you.
>
> Given n items and 2 <=g <<n , how do you divide the n items into g
> groups that are as "equal as possible."
>
> First, operationally define "as equal as possible."
> Second, define the algorithm to carry out the definition. Hint: Note
> that sum{m[i]} for i <=g must sum to n, where m[i] is the number of
> items in the ith group.
> Third, write R code for the algorithm. Exercise for the reader.
>
> I may be wrong, but I think numerical analysts might also have a
> little fun here.
>
> Randomization, of course, is trivial.
>
> Cheers,
> Bert
>
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
>
> On Sat, Sep 4, 2021 at 2:13 PM AbouEl-Makarim Aboueissa
> <abouelmakarim1...@gmail.com> wrote:
> >
> > Dear Thomas:
> >
> >
> > Thank you very much for your input in this matter.
> >
> >
> > The core part of this R code(s) (please see below) was written by *Richard
> > O'Keefe*. I had three examples with different sample sizes.
> >
> >
> >
> > *First sample of size n1 = 204* divided randomly into three groups of sizes
> > 68. *No problems with this one*.
> >
> >
> >
> > *The second sample of size n2 = 112* divided randomly into three groups of
> > sizes 37, 37, and 38. BUT this R code generated three groups of equal sizes
> > (37, 37, and 37). *How to fix the code to make sure that the output will be
> > three groups of sizes 37, 37, and 38*.
> >
> >
> >
> > *The third sample of size n3 = 284* divided randomly into three groups of
> > sizes 94, 95, and 95. BUT this R code generated three groups of equal sizes
> > (94, 94, and 94). *Again*, h*ow to fix the code to make sure that the
> > output will be three groups of sizes 94, 95, and 95*.
> >
> >
> > With many thanks
> >
> > abou
> >
> >
> > ########### ------------------------ #############
> >
> >
> > N1 <- 485
> > population1.IDs <- seq(1, N1, by = 1)
> > #### population1.IDs
> >
> > n1<-204 ##### in this case the size
> > of each group of the three groups = 68
> > sample1.IDs <- sample(population1.IDs,n1)
> > #### sample1.IDs
> >
> > #### n1 <- length(sample1.IDs)
> >
> > m1 <- n1 %/% 3
> > s1 <- sample(1:n1, n1)
> > group1.IDs <- sample1.IDs[s1[1:m1]]
> > group2.IDs <- sample1.IDs[s1[(m1+1):(2*m1)]]
> > group3.IDs <- sample1.IDs[s1[(m1*2+1):(3*m1)]]
> >
> > groups.IDs <-cbind(group1.IDs,group2.IDs,group3.IDs)
> >
> > groups.IDs
> >
> >
> > ####### --------------------------
> >
> >
> > N2 <- 266
> > population2.IDs <- seq(1, N2, by = 1)
> > #### population2.IDs
> >
> > n2<-112 ##### in this case the sizes of the three
> > groups are(37, 37, and 38)
> > ##### BUT this codes generate
> > three groups of equal sizes (37, 37, and 37)
> > sample2.IDs <- sample(population2.IDs,n2)
> > #### sample2.IDs
> >
> > #### n2 <- length(sample2.IDs)
> >
> > m2 <- n2 %/% 3
> > s2 <- sample(1:n2, n2)
> > group1.IDs <- sample2.IDs[s2[1:m2]]
> > group2.IDs <- sample2.IDs[s2[(m2+1):(2*m2)]]
> > group3.IDs <- sample2.IDs[s2[(m2*2+1):(3*m2)]]
> >
> > groups.IDs <-cbind(group1.IDs,group2.IDs,group3.IDs)
> >
> > groups.IDs
> >
> >
> > ####### --------------------------
> >
> >
> >
> > N3 <- 674
> > population3.IDs <- seq(1, N3, by = 1)
> > #### population3.IDs
> >
> > n3<-284 ##### in this case the sizes of the three
> > groups are(94, 95, and 95)
> > ##### BUT this codes generate
> > three groups of equal sizes (94, 94, and 94)
> > sample2.IDs <- sample(population2.IDs,n2)
> > sample3.IDs <- sample(population3.IDs,n3)
> > #### sample3.IDs
> >
> > #### n3 <- length(sample2.IDs)
> >
> > m3 <- n3 %/% 3
> > s3 <- sample(1:n3, n3)
> > group1.IDs <- sample3.IDs[s3[1:m3]]
> > group2.IDs <- sample3.IDs[s3[(m3+1):(2*m3)]]
> > group3.IDs <- sample3.IDs[s3[(m3*2+1):(3*m3)]]
> >
> > groups.IDs <-cbind(group1.IDs,group2.IDs,group3.IDs)
> >
> > groups.IDs
> >
> > ______________________
> >
> >
> > *AbouEl-Makarim Aboueissa, PhD*
> >
> > *Professor, Statistics and Data Science*
> > *Graduate Coordinator*
> >
> > *Department of Mathematics and Statistics*
> > *University of Southern Maine*
> >
> >
> >
> > On Sat, Sep 4, 2021 at 11:54 AM Thomas Subia <tgs...@yahoo.com> wrote:
> >
> > > Abou,
> > >
> > >
> > >
> > > I’ve been following your question on how to split a data column randomly
> > > into 3 groups using R.
> > >
> > >
> > >
> > > My method may not be amenable for a large set of data but it surely worth
> > > considering since it makes sense intuitively.
> > >
> > >
> > >
> > > mydata <- LETTERS[1:11]
> > >
> > > > mydata
> > >
> > > [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K"
> > >
> > >
> > >
> > > # Let’s choose a random sample of size 4 from mydata
> > >
> > > > random_grp1
> > >
> > > [1] "J" "H" "D" "A"
> > >
> > >
> > >
> > > Now my next random selection of data is defined by
> > >
> > > data_wo_random <- setdiff(mydata,random_grp1)
> > >
> > > # this makes sense because I need to choose random data from a set which
> > > is defined by the difference of the sets mydata and random_grp1
> > >
> > >
> > >
> > > > data_wo_random
> > >
> > > [1] "B" "C" "E" "F" "G" "I" "K"
> > >
> > >
> > >
> > > This is great! So now I can randomly select data of any size from this
> > > set.
> > >
> > > Repeating this process can easily generate subgroups of your original
> > > dataset of any size you want.
> > >
> > >
> > >
> > > Surely this method could be improved so that this could be done
> > > automatically.
> > >
> > > Nevertheless, this is an intuitive method which I believe is easier to
> > > understand than some of the other methods posted.
> > >
> > >
> > >
> > > Hope this helps!
> > >
> > >
> > >
> > > Thomas Subia
> > >
> > > Statistician
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > 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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