> On Nov 18, 2017, at 1:52 AM, Allaisone 1 <allaiso...@hotmail.com> 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 <boris.ste...@utoronto.ca>
> 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 <allaiso...@hotmail.com> 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 <boris.ste...@utoronto.ca>
>> 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 <allaiso...@hotmail.com> 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]]
>>>
>>> ______________________________________________
>>> 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.
>
>
> [[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.
David Winsemius
Alameda, CA, USA
'Any technology distinguishable from magic is insufficiently advanced.'
-Gehm's Corollary to Clarke's Third Law
______________________________________________
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.
