r - 根据因子对重叠时间间隔进行分类
问题描述
我正在查看来自制造工厂的生产数据，需要根据一条生产线生产的瓶子大小对所有生产线的当天生产进行分类。基本设置是 FL 5S 生产线仅生产 5l 罐，FL 25 生产 25l 罐，FL 5 可生产 1l、3l 和 5l 罐。我需要一个名为“Recipe”的新列，它应该反映在 FL 5 上生产的所有订单与在 FL 5 上生产的订单同时生产的所有订单在 FL 5 上生产的大小。例如，如果Size == "001"在时间间隔内，2020-04-28 08:46:56 UTC--2020-04-29 07:49:09 UTC则Recipe应该001为所有生产的订单在其他线上，其时间Started与Ended1l 顺序的这些时间重叠。
数据：
library(tidyverse)
library(lubridate)
Prod_data_ex<-structure(list(Order = c(27380, 27388, 27395, 27381, 27389, 27382,
27396, 27397, 27393, 27392, 27383, 27384, 27385, 27386, 27398,
27409, 27410, 27411, 27412, 27416, 27420, 27421, 27417, 27418,
27432, 27433, 27419, 27413, 27399, 27414, 27415, 27428, 27424,
27429, 27431),
Prod_line = c("FL 5S", "FL 5", "FL 25", "FL 5S",
"FL 5", "FL 5S", "FL 25", "FL 5", "FL 5", "FL 5", "FL 5S", "FL 5S",
"FL 5S", "FL 5S", "FL 5", "FL 5S", "FL 5S", "FL 5S", "FL 5S",
"FL 25", "FL 5S", "FL 5S", "FL 25", "FL 25", "FL 5S", "FL 5S",
"FL 25", "FL 5S", "FL 5", "FL 5S", "FL 5S", "FL 5S", "FL 25",
"FL 5S", "FL 5"),
Produced = c(5400, 6373, 1440, 6372, 864, 5400,
288, 1080, 864, 5402, 3240, 864, 5293, 2700, 11547, 4427, 3672,
864, 2651, 96, 648, 1620, 96, 480, 2160, 1363, 480, 4320, 11528,
4320, 864, 12745, 600, 3782, 6373),
Size = c("005", "005", "025","005", "005", "005", "025", "005", "005", "005", "005", "005",
"005", "005", "001", "005", "005", "005", "005", "025", "005",
"005", "025", "025", "005", "005", "025", "005", "001", "005",
"005", "005", "025", "005", "005"),
Started = structure(c(1587969512, 1587970869, 1587972010, 1587984935, 1587998820, 1587999421, 1588003819,
1588004233, 1588008484, 1588013628, 1588014019, 1588022405, 1588024038,
1588062901, 1588063616, 1588070291, 1588076547, 1588083066, 1588085684,
1588090216, 1588093080, 1588094491, 1588094881, 1588100218, 1588101215,
1588105405, 1588105729, 1588110352, 1588146549, 1588147618, 1588155560,
1588157595, 1588172423, 1588179841, 1588180603), tzone = "UTC", class = c("POSIXct",
"POSIXt")), Ended = structure(c(1587984935, 1587998820, 1588003819,
1587999421, 1588004233, 1588014019, 1588090216, 1588008484, 1588013628,
1588063616, 1588022405, 1588024038, 1588062901, 1588070291, 1588146549,
1588076547, 1588083066, 1588085684, 1588093080, 1588094881, 1588094491,
1588101215, 1588100218, 1588105729, 1588105405, 1588110352, 1588172423,
1588147618, 1588180603, 1588155560, 1588157595, 1588179841, 1588188834,
1588191295, 1588241708), tzone = "UTC", class = c("POSIXct",
"POSIXt"))), row.names = c(NA, -35L), class = c("tbl_df", "tbl", "data.frame"))
这是我最接近工作代码的地方，但仍然不适用于其他行的订单。
Prod_data_recpies<- Prod_data_ex%>%
mutate(interval= interval(Started, Ended))%>%
mutate(recipe= ifelse(Size=="001", map_int(interval,~ any(int_overlaps(.x, interval[Size=="001"]))),
ifelse(Size=="003", map_int(interval,~ any(int_overlaps(.x, interval[Size=="003"]))), "005")))
谢谢！
解决方案
我不确定您要如何处理多个FL 5订单的重叠和不存在的重叠，但您可以修改以下方法以满足您的需求。我foverlaps()从data.table包中使用来识别重叠。type如果您想指定间隔重叠（例如完全在、开始或结束重叠），您可以使用该参数。
library(data.table)
dt <- as.data.table(Prod_data_ex)
dt[, Ended := Ended - 1] # prevent overlaps of consecutive time intervals
setkeyv(dt, c("Started", "Ended"))
dt[, Recipe := foverlaps(dt, dt[Prod_line == "FL 5"], which = TRUE)[,
list(list(unique(yid[yid != xid]))), by = xid][, -1]]
dt$Recipe <- sapply(dt$Recipe, function(x) {
paste(unique(dt[Prod_line == "FL 5"][x]$Size), collapse = ",")
})
dt[, Ended := Ended + 1][] # add back the second
结果：
#> Order Prod_line Produced Size Started Ended
#> 1: 27380 FL 5S 5400 005 2020-04-27 06:38:32 2020-04-27 10:55:35
#> 2: 27388 FL 5 6373 005 2020-04-27 07:01:09 2020-04-27 14:47:00
#> 3: 27395 FL 25 1440 025 2020-04-27 07:20:10 2020-04-27 16:10:19
#> 4: 27381 FL 5S 6372 005 2020-04-27 10:55:35 2020-04-27 14:57:01
#> 5: 27389 FL 5 864 005 2020-04-27 14:47:00 2020-04-27 16:17:13
#> 6: 27382 FL 5S 5400 005 2020-04-27 14:57:01 2020-04-27 19:00:19
#> 7: 27396 FL 25 288 025 2020-04-27 16:10:19 2020-04-28 16:10:16
#> 8: 27397 FL 5 1080 005 2020-04-27 16:17:13 2020-04-27 17:28:04
#> 9: 27393 FL 5 864 005 2020-04-27 17:28:04 2020-04-27 18:53:48
#> 10: 27392 FL 5 5402 005 2020-04-27 18:53:48 2020-04-28 08:46:56
#> 11: 27383 FL 5S 3240 005 2020-04-27 19:00:19 2020-04-27 21:20:05
#> 12: 27384 FL 5S 864 005 2020-04-27 21:20:05 2020-04-27 21:47:18
#> 13: 27385 FL 5S 5293 005 2020-04-27 21:47:18 2020-04-28 08:35:01
#> 14: 27386 FL 5S 2700 005 2020-04-28 08:35:01 2020-04-28 10:38:11
#> 15: 27398 FL 5 11547 001 2020-04-28 08:46:56 2020-04-29 07:49:09
#> 16: 27409 FL 5S 4427 005 2020-04-28 10:38:11 2020-04-28 12:22:27
#> 17: 27410 FL 5S 3672 005 2020-04-28 12:22:27 2020-04-28 14:11:06
#> 18: 27411 FL 5S 864 005 2020-04-28 14:11:06 2020-04-28 14:54:44
#> 19: 27412 FL 5S 2651 005 2020-04-28 14:54:44 2020-04-28 16:58:00
#> 20: 27416 FL 25 96 025 2020-04-28 16:10:16 2020-04-28 17:28:01
#> 21: 27420 FL 5S 648 005 2020-04-28 16:58:00 2020-04-28 17:21:31
#> 22: 27421 FL 5S 1620 005 2020-04-28 17:21:31 2020-04-28 19:13:35
#> 23: 27417 FL 25 96 025 2020-04-28 17:28:01 2020-04-28 18:56:58
#> 24: 27418 FL 25 480 025 2020-04-28 18:56:58 2020-04-28 20:28:49
#> 25: 27432 FL 5S 2160 005 2020-04-28 19:13:35 2020-04-28 20:23:25
#> 26: 27433 FL 5S 1363 005 2020-04-28 20:23:25 2020-04-28 21:45:52
#> 27: 27419 FL 25 480 025 2020-04-28 20:28:49 2020-04-29 15:00:23
#> 28: 27413 FL 5S 4320 005 2020-04-28 21:45:52 2020-04-29 08:06:58
#> 29: 27399 FL 5 11528 001 2020-04-29 07:49:09 2020-04-29 17:16:43
#> 30: 27414 FL 5S 4320 005 2020-04-29 08:06:58 2020-04-29 10:19:20
#> 31: 27415 FL 5S 864 005 2020-04-29 10:19:20 2020-04-29 10:53:15
#> 32: 27428 FL 5S 12745 005 2020-04-29 10:53:15 2020-04-29 17:04:01
#> 33: 27424 FL 25 600 025 2020-04-29 15:00:23 2020-04-29 19:33:54
#> 34: 27429 FL 5S 3782 005 2020-04-29 17:04:01 2020-04-29 20:14:55
#> 35: 27431 FL 5 6373 005 2020-04-29 17:16:43 2020-04-30 10:15:08
#> Order Prod_line Produced Size Started Ended
#> Recipe
#> 1:
#> 2: 005
#> 3: 005
#> 4: 005
#> 5: 005
#> 6: 005
#> 7: 005,001
#> 8: 005
#> 9: 005
#> 10: 005
#> 11: 005
#> 12: 005
#> 13: 005
#> 14: 005,001
#> 15: 001
#> 16: 001
#> 17: 001
#> 18: 001
#> 19: 001
#> 20: 001
#> 21: 001
#> 22: 001
#> 23: 001
#> 24: 001
#> 25: 001
#> 26: 001
#> 27: 001
#> 28: 001
#> 29: 001
#> 30: 001
#> 31: 001
#> 32: 001
#> 33: 001,005
#> 34: 001,005
#> 35: 005
#> Recipe
由reprex 包（v0.3.0）于 2020 年 12 月 18 日创建
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