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.
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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