问题
I would like to thank for all the help already in advance, as the help until now has allowed me go get new ideas/hypotheses on how my data could be analysed.
Data
structure(list(record_id = c(110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101,
110101, 110101, 110101, 110101, 110101, 110101, 110101, 110101
), start = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59), stop = c(1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60), `treatment (type)` = c(1,
1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 0, 3, 3, 3,
0, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), vulnerable_period_30count = c(0,
0, 0, 1, 1, 1, 1, 2, 3, 4, 1, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 4,
1, 2, 3, 4, 1, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
30, 0, 0), vulnerable_period_yn = c(0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 0), vulnerable_period_30count_3days = c(0,
0, 0, 0, 0, 1, 1, 2, 3, 4, 5, 6, 1, 1, 1, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 0, 0), vulnerable_period_3days_yn = c(0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 11, 1, 1, 1, 1, 1, 1, 1, 0, 0)), row.names = c(NA, -60L
), class = c("tbl_df", "tbl", "data.frame"))
Explanation
Input
start and stop are the day-count. The daily treatment is listed in treatment, with 0 = no treatment, which is an interruption, and 1:3 are treatment A/B/C.
The hypothesis is that once a person has had a treatment interruption, he or she is vulnerable for the next 30 days.
So based on these inputs I would like to count/calculate the following outputs:
Output
vulnerable_period_30count: when a treatment interruption occurs (treatment (type) = 0), it start counting to 30. It automatically resets when a treatment interruption occurs.
vulnerable_period_30count_3days: only if a treatment interruption lasts for three days (thus three consecutive 0's) or longer, is the interruption relevant. Then, it will start counting to 30.
If the interruption last longer than the three days, it will reset the count again. NOTE: see day 6, the interruption lasts 4 days in total. The count resets when the 4 day interruption ended.
When the count has exceeded 30, it reverts back to 0 and will only start counting when a relevant interruption occurred again.
Here the attempt for the vulnerable_period_30count.
Attempt for vulnerable_period_30count
data$vulnerable_period_30count <- data %>%
group_by(record_id, idx = cumsum(`treatment (type)` == 0L)) %>%
mutate(counter = row_number()) %>%
ungroup %>%
select(-idx)
data$test[data$test >30] <- 0
The problem here that it start with 1, 2, 3 already. I hard-stop the count with the last line.
For how to only start count when three or more interruptions occured, i have absolutely no idea.
Hope you can help
BW
Comment OP on Answer 2 @Ekoam Thanks again. The code now runs which is good. Two errors I managed to find. I will use one case to illustrate:
structure(list(record_id = c(110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003), day_count = c(0, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242,
243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,
269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294,
295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,
308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320,
321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,
334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346,
347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,
360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,
373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398,
399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,
425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437,
438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450,
451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
464, 465, 466, 467, 468, 469, 470, 471, 472), day_count_stop = c(1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242,
243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,
269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294,
295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,
308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320,
321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,
334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346,
347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,
360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,
373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398,
399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,
425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437,
438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450,
451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
464, 465, 466, 467, 468, 469, 470, 471, 472, 473), oac_class = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0), vulnerable_period_30count = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L,
76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L,
89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L,
123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L,
134L, 135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L,
145L, 146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L,
156L, 157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L,
167L, 168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L,
178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L,
189L, 190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L,
200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L,
211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L,
222L, 223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L,
233L, 234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L,
244L, 245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L,
255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L,
266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L,
277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L,
288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L,
299L, 300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L,
310L, 311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L,
321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L,
332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L,
343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L,
354L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L,
365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L,
376L, 377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L,
387L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L,
398L, 399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L,
409L, 410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L,
420L, 421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L,
431L, 432L, 433L, 434L, 1L), vulnerable_period_30count_3days = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L,
64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L,
77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 101L,
102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L, 112L,
113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L,
124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L,
135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L,
146L, 147L, 148L, 149L, 150L, 151L, 152L, 153L, 154L, 155L, 156L,
157L, 158L, 159L, 160L, 161L, 162L, 163L, 164L, 165L, 166L, 167L,
168L, 169L, 170L, 171L, 172L, 173L, 174L, 175L, 176L, 177L, 178L,
179L, 180L, 181L, 182L, 183L, 184L, 185L, 186L, 187L, 188L, 189L,
190L, 191L, 192L, 193L, 194L, 195L, 196L, 197L, 198L, 199L, 200L,
201L, 202L, 203L, 204L, 205L, 206L, 207L, 208L, 209L, 210L, 211L,
212L, 213L, 214L, 215L, 216L, 217L, 218L, 219L, 220L, 221L, 222L,
223L, 224L, 225L, 226L, 227L, 228L, 229L, 230L, 231L, 232L, 233L,
234L, 235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L,
245L, 246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L,
256L, 257L, 258L, 259L, 260L, 261L, 262L, 263L, 264L, 265L, 266L,
267L, 268L, 269L, 270L, 271L, 272L, 273L, 274L, 275L, 276L, 277L,
278L, 279L, 280L, 281L, 282L, 283L, 284L, 285L, 286L, 287L, 288L,
289L, 290L, 291L, 292L, 293L, 294L, 295L, 296L, 297L, 298L, 299L,
300L, 301L, 302L, 303L, 304L, 305L, 306L, 307L, 308L, 309L, 310L,
311L, 312L, 313L, 314L, 315L, 316L, 317L, 318L, 319L, 320L, 321L,
322L, 323L, 324L, 325L, 326L, 327L, 328L, 329L, 330L, 331L, 332L,
333L, 334L, 335L, 336L, 337L, 338L, 339L, 340L, 341L, 342L, 343L,
344L, 345L, 346L, 347L, 348L, 349L, 350L, 351L, 352L, 353L, 354L,
355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L, 365L,
366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 375L, 376L,
377L, 378L, 379L, 380L, 381L, 382L, 383L, 384L, 385L, 386L, 387L,
388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L, 398L,
399L, 400L, 401L, 402L, 403L, 404L, 405L, 406L, 407L, 408L, 409L,
410L, 411L, 412L, 413L, 414L, 415L, 416L, 417L, 418L, 419L, 420L,
421L, 422L, 423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L,
432L, 433L, 434L, 435L, 1L)), row.names = c(NA, -473L), groups = structure(list(
record_id = 110003, .rows = structure(list(1:473), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = c(NA, -1L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
At day 66 or day 67 the day count does not stop, and continues counting. This occurs for several other record_id's as wlel.
At day 36 and 38, the vulnerable_day_30_count starts counting too soon.
At the final day, the vulnerable_day_30_count start counting already, although there has not been an interruption of 3 days.
回答1:
Consider a function like this:
# 1. Determine the positions of zeros (p0) in `x`.
# 2. Test whether p0 - 1, p0 - 2, ..., p0 - n + 1 are zeros;
# reduce p0 to those that satisfy all conditions.
# 3. Find the upper bounds (ub). For example, if p0 = c(1,2,3),
# then upper bounds will be c(2,3,?) (i.e. lead p0).
# ? is then 3 + 30 (the max you set) if it is still within
# the length of `x`; otherwise is the length of `x` plus 1.
# 4. Compute the sequence between each pair of p0 and ub and
# then unlist the result to a vector.
# 5. Assign the vector back to its correct position.
conditional_count <- function(x, n, max = 30L) {
len <- length(x); out <- integer(len)
p0 <- which(x == 0L)
if (n > 1L)
p0 <- Reduce(function(idx, i) idx[x[(idx - i + 1L)] == 0L], seq_len(n)[-1L], p0)
ub <- c(tail(p0, -1L), min(len + 1L, tail(p0, 1L) + max))
res <- unlist(lapply(ub - p0, seq_len))
`[<-`(out, p0[[1L]] + seq.int(0L, length(res) - 1L), res)
}
Then you can just
df %>%
mutate(
vulnerable_period_30count = conditional_count(`treatment (type)`, 1L),
vulnerable_period_30count_3days = conditional_count(`treatment (type)`, 3L)
)
Output (the df I used only has four variables: record_id, start, stop, treatment (type). Other variables were removed.)
record_id start stop treatment (type) vulnerable_period_30count vulnerable_period_30count_3days
1 110101 0 1 1 0 0
2 110101 1 2 1 0 0
3 110101 2 3 1 0 0
4 110101 3 4 0 1 0
5 110101 4 5 0 1 0
6 110101 5 6 0 1 1
7 110101 6 7 0 1 1
8 110101 7 8 2 2 2
9 110101 8 9 2 3 3
10 110101 9 10 2 4 4
11 110101 10 11 0 1 5
12 110101 11 12 0 1 6
13 110101 12 13 0 1 1
14 110101 13 14 0 1 1
15 110101 14 15 0 1 1
16 110101 15 16 0 1 1
17 110101 16 17 3 2 2
18 110101 17 18 3 3 3
19 110101 18 19 0 1 4
20 110101 19 20 3 2 5
21 110101 20 21 3 3 6
22 110101 21 22 3 4 7
23 110101 22 23 0 1 8
24 110101 23 24 2 2 9
25 110101 24 25 2 3 10
26 110101 25 26 2 4 11
27 110101 26 27 0 1 12
28 110101 27 28 0 1 13
29 110101 28 29 0 1 1
30 110101 29 30 1 2 2
31 110101 30 31 1 3 3
32 110101 31 32 1 4 4
33 110101 32 33 1 5 5
34 110101 33 34 1 6 6
35 110101 34 35 1 7 7
36 110101 35 36 1 8 8
37 110101 36 37 1 9 9
38 110101 37 38 1 10 10
39 110101 38 39 1 11 11
40 110101 39 40 1 12 12
41 110101 40 41 1 13 13
42 110101 41 42 1 14 14
43 110101 42 43 1 15 15
44 110101 43 44 1 16 16
45 110101 44 45 1 17 17
46 110101 45 46 1 18 18
47 110101 46 47 1 19 19
48 110101 47 48 1 20 20
49 110101 48 49 1 21 21
50 110101 49 50 1 22 22
51 110101 50 51 1 23 23
52 110101 51 52 1 24 24
53 110101 52 53 1 25 25
54 110101 53 54 1 26 26
55 110101 54 55 1 27 27
56 110101 55 56 1 28 28
57 110101 56 57 1 29 29
58 110101 57 58 1 30 30
59 110101 58 59 1 0 0
60 110101 59 60 1 0 0
Update
conditional_count <- function(x, n, max = 30L) {
len <- length(x); out <- integer(len)
p0 <- which(x == 0L)
if (n > 1L)
p0 <- Reduce(function(idx, i) idx[x[(idx - i + 1L)] == 0L], seq_len(n)[-1L], p0)
if (length(p0) < 1L)
return(out)
ub <- c(tail(p0, -1L), min(len + 1L, tail(p0, 1L) + max))
res <- unlist(lapply(ub - p0, seq_len))
`[<-`(out, p0[[1L]] + seq.int(0L, length(res) - 1L), res)
}
This error is likely due to no such zero satisfying your conditions stated above. Hence, p0 is of length 0. We can add one more if-else statement to handle this boundary condition. Try with the code shown above. If it's still not working, please consider updating your problem with something like dput(df[record_id == *, ]), where * represents the erratic group (i.e. 110005 in this case).
Update 2
conditional_count <- function(x, n, max = 30L) {
len <- length(x); out <- integer(len)
p0 <- which(x == 0L)
if (n > 1L)
p0 <- Reduce(function(idx, i) {
lidx <- idx - i + 1L; lidx <- lidx[lidx > 0L]
idx[x[lidx] == 0L]
}, seq_len(n)[-1L], p0)
if (length(p0) < 1L)
return(out)
ub <- c(tail(p0, -1L), min(len + 1L, tail(p0, 1L) + max))
res <- unlist(lapply(ub - p0, seq_len))
`[<-`(out, p0[[1L]] + seq.int(0L, length(res) - 1L), res)
}
I managed to reproduce your problem using some simulated data. The error is also caused by a boundary condition. When the code tries to verify the sequence p0 - 1, p0 - 2, ..., it may go into negative if there is a zero at the beginning of the vector treatment (type). I have corrected that part. Try with the updated code above.
Update 3
conditional_count <- function(x, n, max = 30L) {
len <- length(x); out <- integer(len)
p0 <- which(x == 0L)
if (n > 1L)
p0 <- Reduce(function(idx, i) {
lidx <- idx - i + 1L
idx <- idx[lidx > 0L]; lidx <- lidx[lidx > 0L]
idx[x[lidx] == 0L]
}, seq_len(n)[-1L], p0)
if (length(p0) < 1L)
return(out)
ub <- pmin(c(tail(p0, -1L), len), p0 + max - 1L)
rl <- ub - p0 + 1L
res <- unlist(lapply(rl, seq_len))
pos <- inverse.rle(list(lengths = rl, values = p0)) + res - 1L
`[<-`(out, pos, res)
}
Try with this new one. I reworked some logics. I previously assumed that only the count for the last valid zero can go beyond 30 days. That's why the count never stops for in-between valid zeros. Furthermore, the resultant vector was not assigned to the correct position. This bug also creates some anomalies you have observed. I have corrected both. The result now looks like this
structure(list(record_id = c(110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003, 110003, 110003, 110003,
110003, 110003, 110003, 110003, 110003), day_count = c(0, 1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242,
243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,
269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294,
295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,
308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320,
321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,
334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346,
347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,
360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,
373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398,
399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,
425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437,
438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450,
451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
464, 465, 466, 467, 468, 469, 470, 471, 472), day_count_stop = c(1,
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,
52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83,
84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99,
100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,
113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,
139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,
152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,
191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203,
204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242,
243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255,
256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268,
269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294,
295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307,
308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320,
321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333,
334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346,
347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359,
360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372,
373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385,
386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398,
399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,
425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437,
438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450,
451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,
464, 465, 466, 467, 468, 469, 470, 471, 472, 473), oac_class = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0), vulnerable_period_30count = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L), vulnerable_period_30count_3days = c(0L,
0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), row.names = c(NA,
-473L), groups = structure(list(record_id = 110003, .rows = structure(list(
1:473), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr",
"list"))), row.names = 1L, class = c("tbl_df", "tbl", "data.frame"
), .drop = TRUE), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
))
来源：https://stackoverflow.com/questions/64621711/r-starting-and-resetting-a-counting-column-in-r-based-on-condition