search for: allmafs

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2017 Nov 18
3
Complicated analysis for huge databases
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]...
2017 Nov 18
0
Complicated analysis for huge databases
On 18/11/2017 4:40 PM, Allaisone 1 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) :- > >...
2017 Nov 19
1
Complicated analysis for huge databases
...______________________________ From: Duncan Murdoch <murdoch.duncan at gmail.com> Sent: 18 November 2017 23:15:15 To: Allaisone 1; David Winsemius Cc: R-help Subject: Re: [R] Complicated analysis for huge databases On 18/11/2017 4:40 PM, Allaisone 1 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) :- > > Err...
2017 Nov 18
0
Complicated analysis for huge databases
...ataframes > > > ________________________________ > From: Boris Steipe <boris.steipe at 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...
2017 Nov 18
2
Complicated analysis for huge databases
...9 . . ~180 dataframes ________________________________ From: Boris Steipe <boris.steipe at 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 do...
2017 Nov 18
0
Complicated analysis for huge databases
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 <allaisone1 at hotmail.com> 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 app...
2017 Nov 18
0
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 do...
2017 Nov 18
2
Complicated analysis for huge databases
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
2017 Nov 17
0
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 <allaisone1 at hotmail.com> wrote: > > > Hi all .., > > > I have a large dataset of around 600,000 rows and 600