dplyr Collapsing Zeiträume

Question

Ich habe einen data.frame unten und ich möchte die Zeiträume so "Chunk", dass es für jede company_id "die Perioden auf diejenigen, die durch 30 Tage getrennt sind" zusammenfasst.

company_id startDate endDate 
1  209952 2012-09-17 2012-10-17 
2  209952 2012-10-17 2012-11-17 
3  209952 2012-11-17 2012-12-17 
4  209952 2012-12-17 2013-01-17 
5  209952 2013-01-17 2013-02-17 
6  209952 2013-02-17 2013-03-17 
7  209952 2013-03-17 2013-04-17 
8  209952 2013-04-17 2013-05-17 
9  209952 2013-05-17 2013-06-17 
10  209952 2013-06-17 2013-07-17 
11  209952 2013-07-17 2013-08-17 
12  209952 2013-08-17 2013-09-17 
13  209952 2013-09-17 2013-10-17 
14  209952 2013-10-17 2013-11-17 
15  209952 2013-11-17 2013-12-17 
16  209952 2013-12-17 2014-01-17 
17  209952 2014-01-17 2014-02-17 
18  209952 2014-02-12 2014-03-12 
19  209952 2014-03-12 2014-04-12 
20  209952 2014-04-12 2014-05-12 
21  209952 2014-05-12 2014-06-12 
22  209952 2014-06-12 2014-07-12 
23  209952 2014-07-12 2014-08-12 
24  209952 2014-08-12 2014-09-12 
25  209952 2014-09-12 2014-10-12 
26  209952 2014-10-12 2014-11-12 
27  209952 2014-11-12 2014-12-12 
28  209952 2014-12-12 2015-01-12 
29  209952 2015-01-12 2015-02-12 
30  209952 2015-02-12 2015-03-12 
31  209952 2015-03-12 2015-04-12 
32  209952 2015-04-13 2015-05-13 
33  209952 2015-05-07 2016-05-07 
34  209952 2015-05-07 2015-06-07 
35  209952 2015-06-07 2015-07-07 
36  209952 2015-07-07 2015-08-07 
37  209952 2015-08-07 2015-09-07 
38  209952 2016-05-07 2017-10-23 
39 2802315 2012-10-19 2012-11-19 
40 2802315 2012-11-19 2012-12-19 
41 2802315 2012-12-19 2013-01-19 
42 2802315 2013-01-19 2013-02-19 
43 2802315 2013-02-19 2013-03-19 
44 2802315 2013-03-19 2013-04-19 
45 2802315 2013-04-19 2013-05-19 
46 2802315 2013-05-19 2013-06-19 
47 2802315 2013-06-19 2013-07-19 
48 2802315 2013-07-19 2013-08-19 
49 2802315 2013-08-19 2013-09-19 
50 2802315 2013-09-19 2013-10-19 
51 2802315 2013-10-19 2013-11-19 
52 2802315 2013-11-18 2013-12-18 
53 2802315 2013-12-18 2014-01-18 
54 2802315 2014-01-18 2014-02-18 
55 2802315 2014-02-18 2014-03-18 
56 2802315 2014-03-18 2014-04-18 
57 2802315 2014-04-18 2014-05-18 
58 2802315 2014-09-29 2014-10-29 
59 2802315 2014-10-29 2014-11-29 
60 2802315 2015-04-22 2015-05-22 
61 2802315 2015-05-21 2015-06-21 
62 2802315 2015-06-23 2015-09-23 
63 2802315 2015-07-23 2015-08-23 
64 2802315 2015-11-23 2016-05-23 

I haben die folgenden versucht:

test <- blocks %>% 
    filter(company_id %in% c(209952, 2802315)) %>% 
    arrange(company_id, startDate) %>% 
    group_by(company_id) %>% 
    mutate(
    week = cumsum(startDate - lag(endDate, default = 0) > 30) 
) %>% 
    group_by(company_id, week) %>% 
    summarize(
    startDate = min(startDate), 
    endDate = max(endDate) 
) 

Das Problem ist, dass die Intervalle auf der Leitung (1) und (2) sollte in einer so startdate = 2012-09-17 und endDate kombinierbar = 2017-10-23 weil es weniger als 30 Tage dazwischen gibt.

company_id week startDate endDate 
     <dbl> <int>  <date>  <date> 
1  209952  1 2012-09-17 2016-05-07 
2  209952  2 2016-05-07 2017-10-23 
3 2802315  1 2012-10-19 2014-05-18 
4 2802315  2 2014-09-29 2014-11-29 
5 2802315  3 2015-04-22 2015-09-23 
6 2802315  4 2015-11-23 2016-05-23 

Die Ausgabe, die ich suche ist

1  209952  1 2012-09-17 2016-10-23 
2 2802315  1 2012-10-19 2014-05-18 
3 2802315  2 2014-09-29 2014-11-29 
4 2802315  3 2015-04-22 2015-09-23 
5 2802315  4 2015-11-23 2016-05-23 

Answer 1

Wie wäre mutate + summarize zweimal anrufen:

chunk = function(DF){ 
    DF %>% 
    mutate(
     week = cumsum(startDate - lag(endDate, default = 0) > 30) 
    ) %>% 
    group_by(company_id, week) %>% 
    summarize(
     startDate = min(startDate), 
     endDate = max(endDate) 
    ) 
} 

blocks %>% 
    arrange(company_id, startDate) %>% 
    group_by(company_id) %>% 
    chunk() %>% 
    chunk() 

Ergebnis:

# A tibble: 5 x 4 
# Groups: company_id [?] 
    company_id week startDate endDate 
     <int> <int>  <date>  <date> 
1  209952  1 2012-09-17 2017-10-23 
2 2802315  1 2012-10-19 2014-05-18 
3 2802315  2 2014-09-29 2014-11-29 
4 2802315  3 2015-04-22 2015-09-23 
5 2802315  4 2015-11-23 2016-05-23 

Daten:

blocks = structure(list(company_id = c(209952L, 209952L, 209952L, 209952L, 
209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 
209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 
209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 
209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 
209952L, 209952L, 209952L, 209952L, 209952L, 209952L, 2802315L, 
2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 
2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 
2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 2802315L, 
2802315L, 2802315L, 2802315L, 2802315L), startDate = structure(c(15600, 
15630, 15661, 15691, 15722, 15753, 15781, 15812, 15842, 15873, 
15903, 15934, 15965, 15995, 16026, 16056, 16087, 16113, 16141, 
16172, 16202, 16233, 16263, 16294, 16325, 16355, 16386, 16416, 
16447, 16478, 16506, 16538, 16562, 16562, 16593, 16623, 16654, 
16928, 15632, 15663, 15693, 15724, 15755, 15783, 15814, 15844, 
15875, 15905, 15936, 15967, 15997, 16027, 16057, 16088, 16119, 
16147, 16178, 16342, 16372, 16547, 16576, 16609, 16639, 16762 
), class = "Date"), endDate = structure(c(15630, 15661, 15691, 
15722, 15753, 15781, 15812, 15842, 15873, 15903, 15934, 15965, 
15995, 16026, 16056, 16087, 16118, 16141, 16172, 16202, 16233, 
16263, 16294, 16325, 16355, 16386, 16416, 16447, 16478, 16506, 
16537, 16568, 16928, 16593, 16623, 16654, 16685, 17462, 15663, 
15693, 15724, 15755, 15783, 15814, 15844, 15875, 15905, 15936, 
15967, 15997, 16028, 16057, 16088, 16119, 16147, 16178, 16208, 
16372, 16403, 16577, 16607, 16701, 16670, 16944), class = "Date")), class = "data.frame", .Names = c("company_id", 
"startDate", "endDate"), row.names = c(NA, -64L)) 

library(lubridate) 
blocks = blocks %>% 
    mutate_if(is.character, ymd)