I have a data frame that has a set of observed dwell times at a set of
locations. The metadata for the locations includes things that have varying
degrees of specificity. I'm interested in tracking the number of people
present at a given time in a given store, type of store, or zip code.
Here's an example of some sample data (here st=start_time, and et=end_time):
data.frame(st=seq(1483360938,by=1700,length=10),et=seq(1483362938,by=1700,length=10),store=c(rep("gap",5),rep("starbucks",5)),zip=c(94000,94000,94100,94100,94200,94000,94000,94100,94100,94200),store_id=seq(50,59))
st et store zip store_id
1 1483360938 1483362938 gap 94000 50
2 1483362638 1483364638 gap 94000 51
3 1483364338 1483366338 gap 94100 52
4 1483366038 1483368038 gap 94100 53
5 1483367738 1483369738 gap 94200 54
6 1483369438 1483371438 starbucks 94000 55
7 1483371138 1483373138 starbucks 94000 56
8 1483372838 1483374838 starbucks 94100 57
9 1483374538 1483376538 starbucks 94100 58
10 1483376238 1483378238 starbucks 94200 59
I'd like to be able to:
a) create aggretages of the number of people present in each store_id at a
given time
b) create aggregates of the number of people present - grouped by zip or
store
I expect to be rolling up to hour or half hour buckets, but I don't think I
should have to decide this up front and be able to do something clever to
be able to use ggplot + some other library to plot the time evolution of
this information, rolled up the way I want.
Any clever solutions? I've trolled stackoverflow and this email list.. to
no avail - but I'm willing to acknowledge I may have missed something.
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