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.
