Hi:
Here's a Q D solution that could be improved. It uses the plyr
package. Starting from your data1 data frame,
library('plyr')
dseq - seq(as.Date('2010-01-01'), as.Date('2011-06-05'), by = '30 days')
# Use the cut() function to create a factor whose levels are demarcated
# by the dates in dseq:
# See ?cut for labeling options
data1[['tf']] - cut(data1$Date, dseq)
ddply(subset(data1, event == 1L), .(tf), summarise, Date.min = min(Date))
tf Date.min
1 2010-01-01 2010-01-01
2 2010-01-31 2010-02-12
3 2010-05-01 2010-05-03
4 2011-03-27 2011-04-21
The value of tf is the left endpoint of the time interval.
This isn't your desired output in two respects: (1) summarise won't
carry along extra variables, so ID gets dropped; (2) you have
2010-03-01 as the first date of a 30-day period, but according to the
way I defined the 30-day intervals, Mar. 1 is the last day of an
interval, so that's why it's not included [2010-2-12 precedes it]. You
can always change the definitions. If you group by months instead, you
get the output you expected.
Hope this is enough to get you started..
Dennis
On Fri, Nov 18, 2011 at 3:22 PM, Chris Conner connerpha...@yahoo.com wrote:
I large datset that includes subjects(ID), Dates and events that need to be
counted. Not every date includes an event, and I need to only count one
event per 30days, per subject. So in essence, I need to create a 30-day
black out period during which time an event cannot be counted for each
subject. The reason is that a rule has been set up, whereby a subject can
only be counted once per 30 day period (the 30 day window includes the day
the event of interest is counted).
The solution should count only the following events per subject(per the
30-day blackout rule):
ID Date
auto1 1/1/2010
auto2 2/12/2010
auto2 4/21/2011
auto3 3/1/2010
auto3 5/3/2010
I have created a multistep process to do this, but it is extremely clumsy
(detailed below). I have to believe that one of you has a much more elegant
solution. Thank you all in advance for any help
## example data
data1 - structure(list(ID = structure(c(2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,3L,
4L, 4L, 4L, 4L, 4L), .Label = c(, auto1, auto2, auto3), class =
factor), Date = structure(c(14610, 14610, 14627,14680, 14652, 14660, 14725,
15085, 15086, 14642, 14669, 14732,14747, 14749), class = Date), event =
c(1L, 1L, 1L, 0L, 1L,1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L)), .Names = c(ID,
Date,event), class = data.frame, row.names = c(NA, 14L))
## remove non events
data2 - data1[data1$event==1,]
library(doBy)
## create a table of first events
step1 - summaryBy(Date~ID, data = data2, FUN=min)
step1$Date30 - step1$Date.min+30
step2 - merge(data2, step1, by.x=ID, by.y=ID)
## use an ifelse to essentially remove any events that shouldn't be
counted
step2$event - ifelse(as.numeric(step2$Date) = step2$Date.min
as.numeric(step2$Date) = step2$Date30, 0, step2$event)
## basically repeat steps above until I get an error (no more events)
data3 - step2[step2$event==1,]
data3- data3[,1:3]
step3 - summaryBy(Date~ID, data = data3, FUN=min)
step3$Date30 - step3$Date.min+30
step4 - merge(data3, step3, by.x=ID, by.y=ID)
step4$event - ifelse(as.numeric(step4$Date) = step4$Date.min
as.numeric(step4$Date) = step4$Date30, 0, step4$event)
## then I rbind the keepers
## in this case steps 1 and 3 above
final - rbind(step1,step3)
## then reformat
final - final[,1:2]
final$Date.min - as.Date(final$Date.min,origin=1970-01-01)
## again, extremely clumsy, but it works... HELP! :)
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