Hello all, Updating the description of the code I am hoping to get help with: I have a dataset where I am hoping to indicator variables that help me better organize a matched sample from my data. I have data on counties observed each week over the course of a year, some of them experienced a focal event (i.e., if P =1) and some did not and will serve as controls. Additionally, some counties experienced the focal event more than once. I want to add an indicator variable that identifies before and after these focal events at different durations for both the treatments and the non-treated counties. I need at 1 week, 2 weeks, 3 weeks, 4 weeks, and 5 weeks, but only illustrated up to 2 weeks in the example below. In addition, would be ideal if once a county experienced a focal event they can no longer be in the control set from there forward, but it’s okay if they stay in the dataset and experience a focal event again. I believe I need some variables such as an eventweek variable that identifies the week of the focal event, a before/after variable (0/1 if before after the focal event), a duration variable that identifies the number of weeks before and after (i.e., at 1, 2, 3, 4, and 5), and a variable (treated) that indicates that a county has been treated (after it has been treated since a county should be able to stay in the pool of controls to serve as a control until it gets treated if at all). Essentially, I think I need to duplicate sets of observations to create windows of treated and non-treated counties around the focal events I am interested in examining. I also like to add a few other variables: (1) a variable that indicates which treatment number this is for a county (i.e., first treatment, second treatment, …) (2) a variable that indicates whether a county was treated within a short time window of a previous time it was treated (=1 if treated within <=5 time periods, 0 otherwise) and (3) a variable that indicates the degree of treatment (I want focal event variable to be binary in the wantdata but in the have data it is a actually >1 sometimes since it is a count variable), so if focal event was 2, for example, this variable would = 2. See below for example. Also, I should note that is a focal occurred before week 6, the durations for those observations may not be able to get up to the full 5 weeks before and after. For example, if a focal event occurred in a county in week 2, I'll only have the 1 week duration for that focal event. Thanks! Have: Time ID County FocalEvent 1 222 ABC 0 2 222 ABC 0 3 222 ABC 0 4 222 ABC 0 5 222 ABC 1 6 222 ABC 0 7 222 ABC 0 8 222 ABC 0 9 222 ABC 0 10 222 ABC 0 11 222 ABC 0 12 222 ABC 0 13 222 ABC 0 1 333 BBB 0 2 333 BBB 0 3 333 BBB 0 4 333 BBB 0 5 333 BBB 0 6 333 BBB 0 7 333 BBB 0 8 333 BBB 0 9 333 BBB 0 10 333 BBB 0 11 333 BBB 0 12 333 BBB 0 13 333 BBB 0 1 444 CCC 0 2 444 CCC 0 3 444 CCC 0 4 444 CCC 0 5 444 CCC 1 6 444 CCC 0 7 444 CCC 0 8 444 CCC 0 9 444 CCC 0 10 444 CCC 1 11 444 CCC 0 12 444 CCC 0 13 444 CCC 0 1 555 DDD 0 2 555 DDD 0 3 555 DDD 0 4 555 DDD 0 5 555 DDD 0 6 555 DDD 0 7 555 DDD 0 Want: Time ID County FocalEvent EventWeek Duration BeforeAfter Treated Tnumber Trecent Tdegree 4 222 ABC 0 5 1 0 1 1 0 0 5 222 ABC 1 5 1 1 1 1 0 0 4 333 BBB 0 5 1 0 0 0 0 0 5 333 BBB 0 5 1 1 0 0 0 0 4 444 CCC 0 5 1 0 1 1 0 0 5 444 CCC 1 5 1 1 1 1 0 0 4 555 DDD 0 5 1 0 0 0 0 0 5 555 DDD 0 5 1 1 0 0 0 0 9 444 CCC 0 10 1 0 1 2 0 0 10 444 CCC 1 10 1 1 1 2 0 0 9 333 BBB 0 10 1 0 0 0 0 0 10 333 BBB 0 10 1 1 0 0 0 0 3 222 ABC 0 5 2 0 1 1 0 0 4 222 ABC 0 5 2 0 1 1 0 0 5 222 ABC 1 5 2 1 1 1 0 0 6 222 ABC 0 5 2 1 1 1 0 0 3 333 BBB 0 5 2 0 0 0 0 0 4 333 BBB 0 5 2 0 0 0 0 0 5 333 BBB 0 5 2 1 0 0 0 0 6 333 BBB 0 5 2 1 0 0 0 0 3 444 CCC 0 5 2 0 1 1 0 0 4 444 CCC 0 5 2 0 1 1 0 0 5 444 CCC 1 5 2 1 1 1 0 0 6 444 CCC 0 5 2 1 1 1 0 0 3 555 DDD 0 5 2 0 0 0 0 0 4 555 DDD 0 5 2 0 0 0 0 0 5 555 DDD 0 5 2 1 0 0 0 0 6 555 DDD 0 5 2 1 0 0 0 0 8 444 CCC 0 10 1 0 1 2 0 0 9 444 CCC 0 10 1 0 1 2 0 0 10 444 CCC 1 10 1 1 1 2 0 0 11 444 CCC 1 10 1 1 1 2 0 0 8 333 BBB 0 10 1 0 0 0 0 0 9 333 BBB 0 10 1 0 0 0 0 0 10 333 BBB 0 10 1 1 0 0 0 0 11 333 BBB 0 10 1 1 0 0 0 0
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