search for: svdat

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2016 Apr 20
0
Data reshaping with conditions
Hi sri, As your problem involves a few logical steps, I found it easier to approach it in a stepwise way. Perhaps there are more elegant ways to accomplish this. svdat<-read.table(text="Count id name type 117 335 sally A 19 335 sally A 167 335 sally B 18 340 susan A 56 340 susan A 22 340 susan B 53 340 susan B 135 351 lee A 114 351 lee A 84 351 lee A 80 351 lee A 19 351 lee A 8 351 lee A 21 351 lee A 88 351 lee B 111 351 lee B 46 351 lee B 108 351 lee B&q...
2016 Apr 21
2
Data reshaping with conditions
...variable. Regards, Sri On Thu, Apr 21, 2016 at 4:52 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi sri, > As your problem involves a few logical steps, I found it easier to > approach it in a stepwise way. Perhaps there are more elegant ways to > accomplish this. > > svdat<-read.table(text="Count id name type > 117 335 sally A > 19 335 sally A > 167 335 sally B > 18 340 susan A > 56 340 susan A > 22 340 susan B > 53 340 susan B > 135 351 lee A > 114 351 lee A > 84 351 lee A > 80 351 lee A > 19 351 lee A > 8 351 lee A &gt...
2016 Apr 21
0
Data reshaping with conditions
...igher_than_max of (Count type B). I took to mean that you wanted a logical value for x and y. Looking more closely at your initial message, I see that you wanted _all_ values of A with respect to maxB in x and y. The error with maximum values was due to a typo. Perhaps this will do what you want: svdat<-read.table(text="Count id name type 117 335 sally A 19 335 sally A 167 335 sally B 18 340 susan A 56 340 susan A 22 340 susan B 53 340 susan B 135 351 lee A 114 351 lee A 84 351 lee A 80 351 lee A 19 351 lee A 8 351 lee A 21 351 lee A 88 351 lee B 111 351 lee B 46 351 lee B 108 351 lee B&q...
2016 Apr 20
2
Data reshaping with conditions
Dear All, I am trying to reshape the data with some conditions. A small part of the data looks like below. Like this there will be more data with repeating ID. Count id name type 117 335 sally A 19 335 sally A 167 335 sally B 18 340 susan A 56 340 susan A 22 340 susan B 53 340 susan B 135 351 lee A 114 351 lee A 84 351 lee A 80 351 lee A 19 351 lee A 8 351 lee A 21 351 lee A 88 351 lee B 111 351