The correct code is:
for (i in 1:length(SeparatedGroupsofmealsCombs)) { ...
I had mentioned that this is untested, but the error is so obvious ...
B.
> On Nov 18, 2017, at 4:40 PM, Allaisone 1 <[email protected]> wrote:
>
>
> The loop :
>
> AllMAFs <- list()
>
> for (i in length(SeparatedGroupsofmealsCombs) {
> AllMAFs[[i]] <- apply( SeparatedGroupsofmealsCombs[[i]], 2, function(x)maf(
> tabulate( x+1) ))
> }
>
> gives these errors (I tried this many times and I'm sure I copied it
> entirely) :-
> Error in apply(SeparatedGroupsofmealsCombs[[i]], 2, function(x)
> maf(tabulate(x + :
> object 'i' not found
> > }
> Error: unexpected '}' in " }"
>
>
> The lapply function :
> results<-lapply(SeparatedGroupsofmealsCombs , function(x)maf(tabulate(x+1)))
> gives this error :-
> Error in FUN(left, right) : non-numeric argument to binary operator
>
> I have been trying since yesterday but but until now I'm not able to identify
> the correct syntax.
>
>
>
>
> From: David Winsemius <[email protected]>
> Sent: 18 November 2017 20:06:56
> To: Allaisone 1
> Cc: Boris Steipe; R-help
> Subject: Re: [R] Complicated analysis for huge databases
>
>
> > On Nov 18, 2017, at 1:52 AM, Allaisone 1 <[email protected]> wrote:
> >
> > Although the loop seems to be formulated correctly I wonder why
> > it gives me these errors :
> >
> > -object 'i' not found
> > - unexpected '}' in "}"
>
> You probably did not copy the entire code offered. But we cannot know since
> you did not "show your code", not=r did you post complete error messages.
> Both of these practices are strongly recommended by the Posting Guide. Please
> read it (again?).
>
> --
> David.
> >
> >
> > the desired output is expected to be very large as for each dataframe in
> > the list of dataframes I expect to see maf value for each of the 600
> > columns! and this is only for
> >
> > for one dataframe in the list .. I have around 150-200 dataframes.. not
> > sure how R will store these results.. but first I need the analysis to be
> > done correctly. The final output has to be something like this :-
> >
> >
> >> mafsforeachcolumns(I,II,...600)foreachcombination
> >
> > MealsCombinations Cust.ID I II III
> > IV ...... 600
> > 1 33-55 1 0.124 0.10
> > 0.65 0.467
> > 3
> > 5
> >
> > 2 44-66 7 0.134 0.43
> > 0.64 0.479
> > 4
> > 9
> >
> > .
> >
> > .
> >
> > ~180 dataframes
> >
> >
> > ________________________________
> > From: Boris Steipe <[email protected]>
> > Sent: 18 November 2017 00:35:16
> > To: Allaisone 1; R-help
> > Subject: Re: [R] Complicated analysis for huge databases
> >
> > Something like the following?
> >
> > AllMAFs <- list()
> >
> > for (i in length(SeparatedGroupsofmealsCombs) {
> > AllMAFs[[i]] <- apply( SeparatedGroupsofmealsCombs[[i]], 2,
> > function(x)maf( tabulate( x+1) ))
> > }
> >
> >
> > (untested, of course)
> > Also the solution is a bit generic since I don't know what the output of
> > maf() looks like in your case, and I don't understand why you use tabulate
> > because I would have assumed that's what maf() does - but that's not for me
> > to worry about :-)
> >
> >
> >
> > B.
> >
> >
> >
> >> On Nov 17, 2017, at 7:15 PM, Allaisone 1 <[email protected]> wrote:
> >>
> >>
> >> Thanks Boris , this was very helpful but I'm struggling with the last part.
> >>
> >> 1) I combined the first 2 columns :-
> >>
> >>
> >> library(tidyr)
> >> SingleMealsCode <-unite(MyData, MealsCombinations, c(MealA, MealB),
> >> remove=FALSE)
> >> SingleMealsCode <- SingleMealsCode[,-2]
> >>
> >> 2) I separated this dataframe into different dataframes based on
> >> "MealsCombination"
> >> column so R will recognize each meal combination separately :
> >>
> >> SeparatedGroupsofmealsCombs <-
> >> split(SingleMealCode,SingleMealCode$MealsCombinations)
> >>
> >> after investigating the structure of "SeparatedGroupsofmealsCombs" , I can
> >> see
> >> a list of different databases, each of which represents a different Meal
> >> combinations which is great.
> >>
> >> No, I'm struggling with the last part, how can I run the maf code for all
> >> dataframes?
> >>
> >> when I run this code as before :-
> >>
> >> maf <- apply(SeparatedGroupsofmealsCombs, 2, function(x)maf(tabulate(x+1)))
> >>
> >> an error message says : dim(X) must have a positive length . I'm not sure
> >> which length
> >> I need to specify.. any suggestions to correct this syntax ?
> >>
> >> Regards
> >> Allaisone
> >> From: Boris Steipe <[email protected]>
> >> Sent: 17 November 2017 21:12:06
> >> To: Allaisone 1
> >> Cc: R-help
> >> Subject: Re: [R] Complicated analysis for huge databases
> >>
> >> Combine columns 1 and 2 into a column with a single ID like "33.55",
> >> "44.66" and use split() on these IDs to break up your dataset. Iterate
> >> over the list of data frames split() returns.
> >>
> >>
> >> B.
> >>
> >>> On Nov 17, 2017, at 12:59 PM, Allaisone 1 <[email protected]> wrote:
> >>>
> >>>
> >>> Hi all ..,
> >>>
> >>>
> >>> I have a large dataset of around 600,000 rows and 600 columns. The first
> >>> col is codes for Meal A, the second columns is codes for Meal B. The
> >>> third column is customers IDs where each customer had a combination of
> >>> meals. Each column of the rest columns contains values 0,1,or 2. The
> >>> dataset is organised in a way so that the first group of customers had
> >>> similar meals combinations, this is followed by another group of
> >>> customers with similar meals combinations but different from the first
> >>> group and so on. The dataset looks like this :-
> >>>
> >>>
> >>>> MyData
> >>>
> >>> Meal A Meal B Cust.ID I II III IV
> >>> ...... 600
> >>>
> >>> 1 33 55 1 0 1
> >>> 2 0
> >>>
> >>> 2 33 55 3 1 0
> >>> 2 2
> >>>
> >>> 3 33 55 5 2 1
> >>> 1 2
> >>>
> >>> 4 44 66 7 0 2
> >>> 2 2
> >>>
> >>> 5 44 66 4 1 1
> >>> 0 1
> >>>
> >>> 6 44 66 9 2 0
> >>> 1 2
> >>>
> >>> .
> >>>
> >>> .
> >>>
> >>> 600,000
> >>>
> >>>
> >>>
> >>> I wanted to find maf() for each column(from 4 to 600) after calculating
> >>> the frequency of the 3 values (0,1,2) but this should be done group by
> >>> group (i.e. group(33-55) : rows 1:3 then group(44-66) :rows 4:6 and so
> >>> on).
> >>>
> >>>
> >>> I can do the analysis for the entire column but not group by group like
> >>> this :
> >>>
> >>>
> >>> MAF <- apply(MyData[,4:600], 2, function(x)maf(tabulate(x+1)))
> >>>
> >>> How can I modify this code to tell R to do the analysis group by group
> >>> for each column so I get maf value for 33-55 group of clolumn I, then maf
> >>> value for group 44-66 in the same column I,then the rest of groups in
> >>> this column and do the same for the remaining columns.
> >>>
> >>> In fact, I'm interested in doing this analysis for only 300 columns but
> >>> all of the 600 columns.
> >>> I have another sheet contains names of columns of interest like this :
> >>>
> >>>> ColOfinterest
> >>>
> >>> Col
> >>> I
> >>> IV
> >>> V
> >>> .
> >>> .
> >>> 300
> >>>
> >>> Any one would help with the best combination of syntax to perform this
> >>> complex analysis?
> >>>
> >>> Regards
> >>> Allaisone
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>>
> >>> [[alternative HTML version deleted]]
> >>>
> >>> ______________________________________________
> >>> [email protected] 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.
> >
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > [email protected] 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.
>
> David Winsemius
> Alameda, CA, USA
>
> 'Any technology distinguishable from magic is insufficiently advanced.'
> -Gehm's Corollary to Clarke's Third Law
______________________________________________
[email protected] mailing list -- To UNSUBSCRIBE and more, see
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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.
