Hi everyone,
My dataset includes information on when individuals tap/log in. I would like to work with a subset of my dataset, based on exactly when these individuals are captured tapping in. The time range of interest is 05:45- 09:00 am.
I am using Stata 16.1
I have tried following William's suggestion in this post https://www.statalist.org/forums/for...ates-and-times.
For some reason, the following code spits out the variable "keepthis" that is full of 0's, no "1"s:
I can't figure out why! Any insights are appreciated.
Dataset:
My dataset includes information on when individuals tap/log in. I would like to work with a subset of my dataset, based on exactly when these individuals are captured tapping in. The time range of interest is 05:45- 09:00 am.
I am using Stata 16.1
I have tried following William's suggestion in this post https://www.statalist.org/forums/for...ates-and-times.
For some reason, the following code spits out the variable "keepthis" that is full of 0's, no "1"s:
Code:
gen entry_time = entry_datetime format entry_time %tcHH:MM:SS.sss // time works generate keepthis = inrange(entry_time, hms(05,45,00), hms(09,00,00))
Dataset:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float id double entry_datetime float entry_date byte trnspt_mode_cd 1 1885557758000 21823 1 2 1885562330000 21823 1 3 1885577749000 21823 1 4 1885574952000 21823 1 5 1885539516000 21823 2 6 1885548725000 21823 1 7 1885532387000 21823 2 8 1885569476000 21823 1 9 1885569271000 21823 1 10 1885554651000 21823 1 11 1885532271000 21823 2 12 1885585196000 21823 2 13 1885532278000 21823 2 14 1885555809000 21823 1 15 1885530208000 21823 1 16 1885549637000 21823 1 17 1885555058000 21823 1 18 1885555320000 21823 1 19 1.8855547e+12 21823 2 20 1885573609000 21823 1 21 1885549879000 21823 1 22 1885531646000 21823 1 23 1885569754000 21823 1 24 1885544420000 21823 1 25 1885530270000 21823 1 26 1885548842000 21823 1 27 1885556195000 21823 2 28 1885590028000 21823 1 29 1885544083000 21823 1 30 1885565447000 21823 1 31 1885566394000 21823 1 32 1885540579000 21823 1 33 1885564446000 21823 1 34 1885532403000 21823 1 35 1885530818000 21823 1 36 1885561773000 21823 2 37 1885582243000 21823 2 38 1885571226000 21823 1 39 1885560217000 21823 2 40 1885531861000 21823 1 41 1885567292000 21823 1 42 1885531512000 21823 1 43 1885547268000 21823 1 44 1885563392000 21823 2 45 1885555799000 21823 1 46 1885563568000 21823 1 47 1885532636000 21823 1 48 1885534353000 21823 1 49 1885531753000 21823 1 50 1885572935000 21823 2 51 1885568129000 21823 1 52 1885539278000 21823 1 53 1885560787000 21823 1 54 1885569176000 21823 1 55 1885554294000 21823 1 56 1885533165000 21823 2 57 1885564383000 21823 1 58 1885579774000 21823 2 59 1885571127000 21823 2 60 1885555232000 21823 1 61 1885549333000 21823 1 62 1885566967000 21823 1 63 1885581575000 21823 1 64 1885563490000 21823 2 65 1885533439000 21823 1 66 1885576905000 21823 2 67 1885553424000 21823 1 68 1885561123000 21823 2 69 1885531762000 21823 1 70 1885573248000 21823 2 71 1885566690000 21823 1 72 1885544266000 21823 2 73 1885571062000 21823 1 74 1885553014000 21823 1 75 1885568505000 21823 2 76 1885585260000 21823 2 77 1885582032000 21823 1 78 1885555847000 21823 1 79 1885567347000 21823 2 80 1885558674000 21823 1 81 1885545412000 21823 1 82 1885568668000 21823 2 83 1885567224000 21823 1 84 1885531774000 21823 2 85 1885573059000 21823 2 86 1885551918000 21823 1 87 1885530128000 21823 1 88 1885544421000 21823 1 89 1885564805000 21823 1 90 1885550584000 21823 2 91 1885529413000 21823 1 92 1885529952000 21823 2 93 1885532724000 21823 1 94 1885546429000 21823 2 95 1885533333000 21823 1 96 1885535146000 21823 1 97 1885532395000 21823 1 98 1885533379000 21823 2 99 1885580630000 21823 2 100 1885559297000 21823 1 end format %tcDay_Mon_DD_CCYY_HH:MM:SS entry_datetime format %td entry_date
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