By the way, do not post the same topic twice, especially with different
subject lines! It's counter-productive
--
Ivan Calandra, ATER
University of Reims Champagne-Ardenne
GEGENAA - EA 3795
CREA - 2 esplanade Roland Garros
51100 Reims, France
+33(0)3 26 77 36 89
ivan.calandra at univ-reims.fr
https://www.researchgate.net/profile/Ivan_Calandra
Le 25/02/15 10:14, conglan a ?crit :> HI,
>
> I have the following large data set:
> the sample is as the following:
> Country Product Brand Year_Month DisplaySize OperationSystem
> AE 1 20 201204 1 1
> AE 5 20 201204 1 1
> AE 2 28 201204 3 2
> AE 3 27 201204 1 1
> AE 1 20 201205 1 1
> AE 2 28 201205 3 2
> AE 4 20 201205 1 2
>
> I want to calculate the extra three columns as the following:
> Let's say "20" is own brand
> #The sums of display size produced by competing brand
> #The sums of display size of other products produced by own brand
> #The sums of display size of products of competing firm in the same
> operating system
>
> I build up the following codes, apprently two loops takes too long time:
>
> dd<-unique(cl$Year_Month)
>
> fldata<-data.frame(NULL)
>
> for ( i in 1:length(dd)){
>
> cls<-cl[cl$Year_Month==dd[i],]
> ws<-numeric(0) #The sums of characteristics of products produced by
> competing firms
> ws1<-numeric(0) #The sums of characteristics of other products
produced
> by firm
> ws2<-numeric(0) #The sums of characteristics of products of competing
firm
> in the same operating system
>
>
> for (j in 1:dim(cls)[1]){
>
> wd<-sum(cls[cls$Brand!=cls$Brand[j],]$DISPLAY_SIZE_INCH)
> ws[j]<-wd
> wc<-sum(cls[cls$Brand==cls$Brand[j] &
cls$Item_Id!=cls$Item_Id[j]
> ,]$DISPLAY_SIZE_INCH)
> ws1[j]<-wc
> wf<-sum(cls[cls$Brand!=cls$Brand[j] &
> cls$OPERATING_SYST==cls$OPERATING_SYST[j] ,]$DISPLAY_SIZE_INCH)
> ws2[j]<-wf
> }
>
> cls<-cbind(cls,SumPCf=ws,SumOPf=ws1,SumPCfOp=ws2)
> fldata<-rbind(fldata,cls)
>
> }
>
>
> how can I avoid two loops and improve the speed of my code??????? any other
> solution?
>
>
> Thanks.
>
> Tammy
>
>
>
>
> --
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