Displaying 10 results from an estimated 10 matches for "separatedgroupsofmealscombs".
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]], 2, function(x) maf(tabulate(x + :
object 'i' n...
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) :-
>
> Error in apply(SeparatedGroupsofmealsCombs[[i]], 2, function(x) maf(ta...
2017 Nov 18
0
Complicated analysis for huge databases
...__________
> 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 don't understand why you...
2017 Nov 19
1
Complicated analysis for huge databases
...h <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) :-
>
> Error in apply(SeparatedGroupsofmealsCombs[[i]], 2, function(x) maf(tabul...
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 don't understand why you use tabulate because I would hav...
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) {...
2017 Nov 18
2
Complicated analysis for huge databases
...y(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, ho...
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 don't understand why you use tabulate because I would hav...
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
2017 Nov 17
3
Complicated analysis for huge databases
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