Fernando Henrique Ferraz P. da Rosa writes:
> cust <- c(111,111,112)
> cc <- data.frame(t(sapply(unique(cust),function(level,vec) {
> c(custid=level,freq=sum(vec==level)) },cust)))
> cc[order(cc$freq,decreasing=T),]
An even simpler solution:
cc <- data.frame(table(cust))
Matthew Wilson writes:
> (...)
> Step 3:
>
> I print a list of number of sales per customer ID, ranking the customer
> IDs from most to least. I use a SAS proc freq step for this:
>
> proc freq data=d2 order=freq;
> tables custid;
> run;
>
> and the output would look like this:
I copied your data into Excel and saved it as *.txt. From within
R, the following commands produced for me the result cited in your email
below:
salesData <- read.table("R-qn.txt", header=TRUE,
sep="\t", as.is=TRUE)
sapply(salesData, class)# check class of columns o
Hi,
I'd like to get away from SAS, but I don't really know R well enough at
this point to know if it would be good for this project. I tried to
describe the essence of the project below without getting bogged down in
details.
It starts when I receive a data flat file. There's lots of columns,
