根据因素对重叠时间间隔进行分类
Classifying overlapping time intervals based on factor
我正在查看制造工厂的生产数据，需要根据一条生产线上生产的瓶子大小对所有生产线当天的生产进行分类。基本设置是，生产线 FL 5S 仅生产 5 升水罐，FL 25 生产 25 升水罐，而 FL 5 可以生产 1 升、3 升和 5 升水罐。我需要一个名为“配方”的新列，它应该反映在 FL 5 上生产的水壶的大小，用于与在 FL 5 上生产的订单同时生产的任何生产线上的所有订单。例如，如果 Size == "001" 在时间间隔 2020-04-28 08:46:56 UTC--2020-04-29 07:49:09 UTC 然后 Recipe 应该是 001 对于在其他生产线上生产的所有订单，其 Started 和 Ended 时间与 1l 订单的这些时间重叠。
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"))
这是我最接近工作的代码，但仍然不适用于其他线路上的订单。
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！
我不确定您想如何处理多个 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要。
我使用 data.table 包中的 foverlaps() 来识别重叠。如果要指定间隔重叠（例如完全在范围内、开始或结束重叠），可以使用 type 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 package (v0.3.0)
于 2020-12-18 创建
我正在查看制造工厂的生产数据，需要根据一条生产线上生产的瓶子大小对所有生产线当天的生产进行分类。基本设置是，生产线 FL 5S 仅生产 5 升水罐，FL 25 生产 25 升水罐，而 FL 5 可以生产 1 升、3 升和 5 升水罐。我需要一个名为“配方”的新列，它应该反映在 FL 5 上生产的水壶的大小，用于与在 FL 5 上生产的订单同时生产的任何生产线上的所有订单。例如，如果 Size == "001" 在时间间隔 2020-04-28 08:46:56 UTC--2020-04-29 07:49:09 UTC 然后 Recipe 应该是 001 对于在其他生产线上生产的所有订单，其 Started 和 Ended 时间与 1l 订单的这些时间重叠。
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"))
这是我最接近工作的代码，但仍然不适用于其他线路上的订单。
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！
我不确定您想如何处理多个 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要。
我使用 data.table 包中的 foverlaps() 来识别重叠。如果要指定间隔重叠（例如完全在范围内、开始或结束重叠），可以使用 type 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 package (v0.3.0)
于 2020-12-18 创建