R 扩展到1分钟，计算平均值
我试图操纵时间，以便在每分钟基础上重新分配平均空闲时间：R 扩展到1分钟，计算平均值,r,time,data.table,time-series,dplyr,R,Time,Data.table,Time Series,Dplyr,我试图操纵时间，以便在每分钟基础上重新分配平均空闲时间： ############################################################# ##Reproducible example 1 (n=10): ############################################################# df.in <- structure(list(id = c(31, 46, 60, 57, 44, 04, 18,
#############################################################
##Reproducible example 1 (n=10):
############################################################# df.in <- structure(list(id = c(31, 46, 60, 57, 44, 04, 18, 55,
22, 5), loc = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L), .Label = c("A", "B", "C"), class = "factor"), t.arrive = structure(c(1425197374,
1425197392, 1425197411, 1425198171, 1425198190, 1425196800, 1425197837,
1425198027, 1425197507, 1425198026), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), t.leave = structure(c(1425197409, 1425197531,
1425197555, 1425198171, 1425198296, 1425196992, 1425197865, 1425198028,
1425197512, 1425198026), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
idle = c(35, 139, 144, 0, 106, 192, 28, 1, 5, 0)), .Names = c("id",
"loc", "t.arrive", "t.leave", "idle"), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -10L))
#############################################################
##Reproducible example 2 (n=100):
#############################################################
> dput(df.in)
structure(list(id = c(78, 93, 107, 84, 104, 91, 71, 66, 189,
182, 92, 209, 96, 84, 50, 103, 182, 183, 74, 132, 101, 78, 88,
93, 48, 107, 82, 72, 182, 83, 66, 91, 104, 50, 71, 96, 103, 74,
182, 101, 132, 84, 78, 88, 93, 107, 83, 182, 48, 66, 96, 51,
75, 65, 102, 80, 106, 63, 156, 51, 75, 79, 67, 65, 85, 94, 89,
106, 69, 80, 79, 67, 69, 52, 105, 94, 73, 95, 100, 76, 55, 99,
60, 69, 53, 86, 52, 105, 90, 64, 95, 73, 63, 100, 76, 51, 99,
53, 75, 52), loc = structure(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, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("A",
"HPB", "HPS", "B", "OPP-B", "C"), class = "factor"), t.arrive = structure(c(1425197374,
1425197392, 1425197411, 1425197927, 1425198171, 1425198190, 1425198194,
1425198227, 1425198303, 1425198475, 1425198812, 1425198924, 1425199119,
1425199199, 1425199235, 1425199355, 1425199528, 1425199544, 1425199641,
1425199643, 1425199648, 1425199801, 1425199812, 1425200087, 1425200103,
1425200310, 1425200454, 1425200478, 1425200517, 1425200611, 1425200669,
1425201076, 1425201105, 1425201275, 1425201287, 1425201378, 1425201536,
1425201604, 1425201628, 1425201767, 1425201893, 1425202137, 1425202244,
1425202255, 1425202557, 1425202566, 1425202879, 1425202962, 1425203094,
1425203109, 1425203380, 1425196800, 1425196800, 1425197837, 1425198027,
1425198955, 1425199074, 1425199342, 1425199465, 1425199855, 1425199929,
1425199970, 1425200480, 1425200517, 1425200950, 1425201289, 1425201357,
1425201879, 1425202374, 1425202982, 1425202987, 1425203318, 1425197507,
1425198026, 1425198378, 1425198390, 1425198994, 1425199059, 1425199298,
1425199522, 1425199528, 1425199728, 1425200115, 1425200289, 1425200373,
1425200547, 1425200679, 1425200880, 1425200909, 1425201364, 1425201509,
1425201801, 1425201910, 1425202039, 1425202246, 1425202490, 1425202555,
1425202589, 1425203048, 1425203108), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), t.leave = structure(c(1425197409, 1425197531,
1425197555, 1425197927, 1425198171, 1425198296, 1425198194, 1425198315,
1425198411, 1425198553, 1425198818, 1425198924, 1425199119, 1425199219,
1425199235, 1425199359, 1425199528, 1425199558, 1425199652, 1425199734,
1425199648, 1425199801, 1425200028, 1425200198, 1425200240, 1425200364,
1425200492, 1425200619, 1425200610, 1425200910, 1425200859, 1425201100,
1425201302, 1425201275, 1425201467, 1425201393, 1425201569, 1425201704,
1425201805, 1425201951, 1425202057, 1425202262, 1425202370, 1425202255,
1425202667, 1425202840, 1425202913, 1425202990, 1425203094, 1425203109,
1425203380, 1425196992, 1425196800, 1425197865, 1425198028, 1425198984,
1425199149, 1425199356, 1425199466, 1425199902, 1425200051, 1425200286,
1425200783, 1425200845, 1425201125, 1425201586, 1425201640, 1425201879,
1425202377, 1425202986, 1425202987, 1425203318, 1425197512, 1425198026,
1425198378, 1425198486, 1425199021, 1425199078, 1425199325, 1425199558,
1425199810, 1425199939, 1425200118, 1425200305, 1425200485, 1425200782,
1425200894, 1425201065, 1425201111, 1425201364, 1425201623, 1425201857,
1425202015, 1425202039, 1425202404, 1425202671, 1425202651, 1425202834,
1425203105, 1425203198), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
idle = c(35, 139, 144, 0, 0, 106, 0, 88, 108, 78, 6, 0, 0,
20, 0, 4, 0, 14, 11, 91, 0, 0, 216, 111, 137, 54, 38, 141,
93, 299, 190, 24, 197, 0, 180, 15, 33, 100, 177, 184, 164,
125, 126, 0, 110, 274, 34, 28, 0, 0, 0, 192, 0, 28, 1, 29,
75, 14, 1, 47, 122, 316, 303, 328, 175, 297, 283, 0, 3, 4,
0, 0, 5, 0, 0, 96, 27, 19, 27, 36, 282, 211, 3, 16, 112,
235, 215, 185, 202, 0, 114, 56, 105, 0, 158, 181, 96, 245,
57, 90)), class = "data.frame", .Names = c("id", "loc", "t.arrive",
"t.leave", "idle"), row.names = c(NA, -100L))
## Expand time into 1-min intervals
df.min <- df.in %>%
rownames_to_column() %>%
group_by(rowname) %>%
do(data.frame(min = seq(.$t.arrive, .$t.leave, by = "1 min"),
id = first(.$id),
loc = first(.$loc),
idle.mean = as.numeric(mean(.$idle))
))
## Round Off to 0 seconds to make it more tractable:
df.min$min <- as.POSIXct(round(df.min$min, "mins"))
## Calculate within each minute
df.min <- df.min %>%
group_by(min, loc) %>%
summarise(units.count = n(),
cum.queue.min = sum(idle.mean)/60
)
## Take 1 min average idle time per id
df.min <- as.data.frame(df.min)
df.min <- df.min %>%
mutate(queue.tmean = cum.queue.min / units.count) %>%
select(-units.count, -cum.queue.min) %>%
arrange(min, loc)
#############################################################
##可再现示例1（n=10）：
#############################################################df.in dput（df.in）
结构（列表id=c（78,93,107,84,104,91,71,66,189，
182, 92, 209, 96, 84, 50, 103, 182, 183, 74, 132, 101, 78, 88,
93, 48, 107, 82, 72, 182, 83, 66, 91, 104, 50, 71, 96, 103, 74,
182, 101, 132, 84, 78, 88, 93, 107, 83, 182, 48, 66, 96, 51,
75, 65, 102, 80, 106, 63, 156, 51, 75, 79, 67, 65, 85, 94, 89,
106, 69, 80, 79, 67, 69, 52, 105, 94, 73, 95, 100, 76, 55, 99,
60, 69, 53, 86, 52, 105, 90, 64, 95, 73, 63, 100, 76, 51, 99,
53,75,52），loc=结构（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，1L，1L，1L，1L，1L，1L，1L，1L，
1L，1L，1L，1L，1L，1L，1L，1L，1L，1L，4L，4L，4L，4L，4L，
4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，
6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，
6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L，6L），标签=c（“A”，
“HPB”、“HPS”、“B”、“OPP-B”、“C”）、class=“factor”），t.到达=结构（C（1425197374，
1425197392, 1425197411, 1425197927, 1425198171, 1425198190, 1425198194,
1425198227, 1425198303, 1425198475, 1425198812, 1425198924, 1425199119,
1425199199, 1425199235, 1425199355, 1425199528, 1425199544, 1425199641,
1425199643, 1425199648, 1425199801, 1425199812, 1425200087, 1425200103,
1425200310, 1425200454, 1425200478, 1425200517, 1425200611, 1425200669,
1425201076, 1425201105, 1425201275, 1425201287, 1425201378, 1425201536,
1425201604, 1425201628, 1425201767, 1425201893, 1425202137, 1425202244,
1425202255, 1425202557, 1425202566, 1425202879, 1425202962, 1425203094,
1425203109, 1425203380, 1425196800, 1425196800, 1425197837, 1425198027,
1425198955, 1425199074, 1425199342, 1425199465, 1425199855, 1425199929,
1425199970, 1425200480, 1425200517, 1425200950, 1425201289, 1425201357,
1425201879, 1425202374, 1425202982, 1425202987, 1425203318, 1425197507,
1425198026, 1425198378, 1425198390, 1425198994, 1425199059, 1425199298,
1425199522, 1425199528, 1425199728, 1425200115, 1425200289, 1425200373,
1425200547, 1425200679, 1425200880, 1425200909, 1425201364, 1425201509,
1425201801, 1425201910, 1425202039, 1425202246, 1425202490, 1425202555,
142520258914252030481425203108），类别=c（“POSIXct”，“POSIXt”
)，tzone=“UTC”），t.leave=结构（c，
1425197555, 1425197927, 1425198171, 1425198296, 1425198194, 1425198315,
1425198411, 1425198553, 1425198818, 1425198924, 1425199119, 1425199219,
1425199235, 1425199359, 1425199528, 1425199558, 1425199652, 1425199734,
1425199648, 1425199801, 1425200028, 1425200198, 1425200240, 1425200364,
1425200492, 1425200619, 1425200610, 1425200910, 1425200859, 1425201100,
1425201302, 1425201275, 1425201467, 1425201393, 1425201569, 1425201704,
1425201805, 1425201951, 1425202057, 1425202262, 1425202370, 1425202255,
1425202667, 1425202840, 1425202913, 1425202990, 1425203094, 1425203109,
1425203380, 1425196992, 1425196800, 1425197865, 1425198028, 1425198984,
1425199149, 1425199356, 1425199466, 1425199902, 1425200051, 1425200286,
1425200783, 1425200845, 1425201125, 1425201586, 1425201640, 1425201879,
1425202377, 1425202986, 1425202987, 1425203318, 1425197512, 1425198026,
1425198378, 1425198486, 1425199021, 1425199078, 1425199325, 1425199558,
1425199810, 1425199939, 1425200118, 1425200305, 1425200485, 1425200782,
1425200894, 1425201065, 1425201111, 1425201364, 1425201623, 1425201857,
1425202015, 1425202039, 1425202404, 1425202671, 1425202651, 1425202834,
14252031051425203198），class=c（“POSIXct”，“POSIXt”），tzone=“UTC”），
怠速=c（35、139、144、0、0、106、0、88、108、78、6、0、0、，
20, 0, 4, 0, 14, 11, 91, 0, 0, 216, 111, 137, 54, 38, 141,
93, 299, 190, 24, 197, 0, 180, 15, 33, 100, 177, 184, 164,
125, 126, 0, 110, 274, 34, 28, 0, 0, 0, 192, 0, 28, 1, 29,
75, 14, 1, 47, 122, 316, 303, 328, 175, 297, 283, 0, 3, 4,
0, 0, 5, 0, 0, 96, 27, 19, 27, 36, 282, 211, 3, 16, 112,
235, 215, 185, 202, 0, 114, 56, 105, 0, 158, 181, 96, 245,
57,90），class=“data.frame”，.Names=c（“id”、“loc”、“t.arrival”，
“t.离开”，“空闲”），row.names=c（NA，-100L））
#############################################################
##Reproducible example 1 (n=10):
############################################################# df.in <- structure(list(id = c(31, 46, 60, 57, 44, 04, 18, 55,
22, 5), loc = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L), .Label = c("A", "B", "C"), class = "factor"), t.arrive = structure(c(1425197374,
1425197392, 1425197411, 1425198171, 1425198190, 1425196800, 1425197837,
1425198027, 1425197507, 1425198026), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), t.leave = structure(c(1425197409, 1425197531,
1425197555, 1425198171, 1425198296, 1425196992, 1425197865, 1425198028,
1425197512, 1425198026), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
idle = c(35, 139, 144, 0, 106, 192, 28, 1, 5, 0)), .Names = c("id",
"loc", "t.arrive", "t.leave", "idle"), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -10L))
#############################################################
##Reproducible example 2 (n=100):
#############################################################
> dput(df.in)
structure(list(id = c(78, 93, 107, 84, 104, 91, 71, 66, 189,
182, 92, 209, 96, 84, 50, 103, 182, 183, 74, 132, 101, 78, 88,
93, 48, 107, 82, 72, 182, 83, 66, 91, 104, 50, 71, 96, 103, 74,
182, 101, 132, 84, 78, 88, 93, 107, 83, 182, 48, 66, 96, 51,
75, 65, 102, 80, 106, 63, 156, 51, 75, 79, 67, 65, 85, 94, 89,
106, 69, 80, 79, 67, 69, 52, 105, 94, 73, 95, 100, 76, 55, 99,
60, 69, 53, 86, 52, 105, 90, 64, 95, 73, 63, 100, 76, 51, 99,
53, 75, 52), loc = structure(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, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("A",
"HPB", "HPS", "B", "OPP-B", "C"), class = "factor"), t.arrive = structure(c(1425197374,
1425197392, 1425197411, 1425197927, 1425198171, 1425198190, 1425198194,
1425198227, 1425198303, 1425198475, 1425198812, 1425198924, 1425199119,
1425199199, 1425199235, 1425199355, 1425199528, 1425199544, 1425199641,
1425199643, 1425199648, 1425199801, 1425199812, 1425200087, 1425200103,
1425200310, 1425200454, 1425200478, 1425200517, 1425200611, 1425200669,
1425201076, 1425201105, 1425201275, 1425201287, 1425201378, 1425201536,
1425201604, 1425201628, 1425201767, 1425201893, 1425202137, 1425202244,
1425202255, 1425202557, 1425202566, 1425202879, 1425202962, 1425203094,
1425203109, 1425203380, 1425196800, 1425196800, 1425197837, 1425198027,
1425198955, 1425199074, 1425199342, 1425199465, 1425199855, 1425199929,
1425199970, 1425200480, 1425200517, 1425200950, 1425201289, 1425201357,
1425201879, 1425202374, 1425202982, 1425202987, 1425203318, 1425197507,
1425198026, 1425198378, 1425198390, 1425198994, 1425199059, 1425199298,
1425199522, 1425199528, 1425199728, 1425200115, 1425200289, 1425200373,
1425200547, 1425200679, 1425200880, 1425200909, 1425201364, 1425201509,
1425201801, 1425201910, 1425202039, 1425202246, 1425202490, 1425202555,
1425202589, 1425203048, 1425203108), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), t.leave = structure(c(1425197409, 1425197531,
1425197555, 1425197927, 1425198171, 1425198296, 1425198194, 1425198315,
1425198411, 1425198553, 1425198818, 1425198924, 1425199119, 1425199219,
1425199235, 1425199359, 1425199528, 1425199558, 1425199652, 1425199734,
1425199648, 1425199801, 1425200028, 1425200198, 1425200240, 1425200364,
1425200492, 1425200619, 1425200610, 1425200910, 1425200859, 1425201100,
1425201302, 1425201275, 1425201467, 1425201393, 1425201569, 1425201704,
1425201805, 1425201951, 1425202057, 1425202262, 1425202370, 1425202255,
1425202667, 1425202840, 1425202913, 1425202990, 1425203094, 1425203109,
1425203380, 1425196992, 1425196800, 1425197865, 1425198028, 1425198984,
1425199149, 1425199356, 1425199466, 1425199902, 1425200051, 1425200286,
1425200783, 1425200845, 1425201125, 1425201586, 1425201640, 1425201879,
1425202377, 1425202986, 1425202987, 1425203318, 1425197512, 1425198026,
1425198378, 1425198486, 1425199021, 1425199078, 1425199325, 1425199558,
1425199810, 1425199939, 1425200118, 1425200305, 1425200485, 1425200782,
1425200894, 1425201065, 1425201111, 1425201364, 1425201623, 1425201857,
1425202015, 1425202039, 1425202404, 1425202671, 1425202651, 1425202834,
1425203105, 1425203198), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
idle = c(35, 139, 144, 0, 0, 106, 0, 88, 108, 78, 6, 0, 0,
20, 0, 4, 0, 14, 11, 91, 0, 0, 216, 111, 137, 54, 38, 141,
93, 299, 190, 24, 197, 0, 180, 15, 33, 100, 177, 184, 164,
125, 126, 0, 110, 274, 34, 28, 0, 0, 0, 192, 0, 28, 1, 29,
75, 14, 1, 47, 122, 316, 303, 328, 175, 297, 283, 0, 3, 4,
0, 0, 5, 0, 0, 96, 27, 19, 27, 36, 282, 211, 3, 16, 112,
235, 215, 185, 202, 0, 114, 56, 105, 0, 158, 181, 96, 245,
57, 90)), class = "data.frame", .Names = c("id", "loc", "t.arrive",
"t.leave", "idle"), row.names = c(NA, -100L))
## Expand time into 1-min intervals
df.min <- df.in %>%
rownames_to_column() %>%
group_by(rowname) %>%
do(data.frame(min = seq(.$t.arrive, .$t.leave, by = "1 min"),
id = first(.$id),
loc = first(.$loc),
idle.mean = as.numeric(mean(.$idle))
))
## Round Off to 0 seconds to make it more tractable:
df.min$min <- as.POSIXct(round(df.min$min, "mins"))
## Calculate within each minute
df.min <- df.min %>%
group_by(min, loc) %>%
summarise(units.count = n(),
cum.queue.min = sum(idle.mean)/60
)
## Take 1 min average idle time per id
df.min <- as.data.frame(df.min)
df.min <- df.min %>%
mutate(queue.tmean = cum.queue.min / units.count) %>%
select(-units.count, -cum.queue.min) %>%
arrange(min, loc)
##将时间扩展为1分钟的间隔
df.min%
行名称到列（）%>%
分组依据（行名称）%>%
do（数据帧（最小值=序号（.$t.到达，.$t.离开，按=“1分钟”），
id=第一个（.$id），
loc=第一个（.$loc），
idle.mean=作为数值（平均值（.$idle））
))
##四舍五入到0秒，使其更易于处理：
df.min$min%
汇总（units.count=n（），
cum.queue.min=总和（空闲平均值）/60
)
##每个id的平均空闲时间为1分钟
df.min%
选择（-units.count，-cum.queue.min）%>%
安排（最小，loc）
# Create sequence of datetimes by second from t.arrive to t.leave for each observation
df <- NULL
for (i in 1:nrow(df.in)) {
df <- bind_rows(
df,
slice(data_frame( # slice cuts off last second entry
t.present = seq(df.in[[i, 't.arrive']], df.in[[i, 't.leave']], by = 'sec'),
id = df.in[[i, 'id']],
loc = df.in[[i, 'loc']]), -n()))
}
# Calculate target metric
df$t.present.min <- as.POSIXct(trunc(df$t.present, 'mins'))
result <- df %>%
group_by(id, loc, t.present.min) %>%
summarise(secs.present = n()) %>%
group_by(loc, t.present.min) %>%
summarise(avg.secs = mean(secs.present))
result
#为每次观察按秒从t.Arrival到t.leave创建日期时间序列
df%
总结（平均秒数=平均数（秒数）
结果
loc t.present.min avg.secs
<fctr> <dttm> <dbl>
1 A 2015-03-01 08:09:00 17.00000
2 A 2015-03-01 08:10:00 39.33333
3 A 2015-03-01 08:11:00 60.00000
4 A 2015-03-01 08:12:00 23.00000
5 A 2015-03-01 08:23:00 50.00000
6 A 2015-03-01 08:24:00 56.00000
7 B 2015-03-01 08:00:00 60.00000
8 B 2015-03-01 08:01:00 60.00000
9 B 2015-03-01 08:02:00 60.00000
10 B 2015-03-01 08:03:00 12.00000
11 B 2015-03-01 08:17:00 28.00000
12 B 2015-03-01 08:20:00 1.00000
13 C 2015-03-01 08:11:00 5.00000
loct.present.min平均秒
1A 2015-03-01 08:09:00 17.00000
2A 2015-03-01 08:10:00 39.33333
3A 2015-03-01 08:11:00 60.00000
4A 2015-03-01 08:12:00 23.00000
5A 2015-03-01 08:23:00 50.00000
6 A 2015-03-01 08:24:00 56.00000
7b 2015-03-01 08:00:00 60.00000
8b 2015-03-0108:01:0060.00000
9 B 20
library(dplyr)
out <- do.call(rbind, mapply(make_obs,
df.in$t.arrive, df.in$idle, df.in$id, df.in$loc,
SIMPLIFY = FALSE))
group_by(out, loc, time) %>%
summarise(idle = mean(secs))
Source: local data frame [13 x 3]
Groups: loc [?]
loc time idle
<fctr> <dttm> <dbl>
1 A 2015-03-01 08:10:00 51.66667
2 A 2015-03-01 08:11:00 60.00000
3 A 2015-03-01 08:12:00 21.50000
4 A 2015-03-01 08:23:00 30.00000
...
library(lubridate)
library(data.table)
setDT(dt.in)
dt.in[, arrive_min := round_date(t.arrive, "mins")]
dt2 <- dt.in[, .(mins = arrive_min + (0:floor(idle/60))*60) , by = .(id, loc, arrive_min)]
dt.in[, mins:=arrive_min, ]
dt_full <- dt.in[dt2, on = c("id", "loc", "mins")]
dt_full[, .(mins = mins, idle=c(rep(60, idle[1]/60), idle[1]%%60)), by = .(id, loc, i.arrive_min)
][, .(ave_idle=mean(idle)), by = .(mins, loc)]
# min1 loc ave_idle
# 1: 2015-03-01 08:10:00 A 51.66667
# 2: 2015-03-01 08:11:00 A 60.00000
# 3: 2015-03-01 08:12:00 A 21.50000
# 4: 2015-03-01 08:23:00 A 30.00000
# 5: 2015-03-01 08:24:00 A 46.00000
# 6: 2015-03-01 08:00:00 B 60.00000
# 7: 2015-03-01 08:01:00 B 60.00000
# 8: 2015-03-01 08:02:00 B 60.00000
# 9: 2015-03-01 08:03:00 B 12.00000
#10: 2015-03-01 08:17:00 B 28.00000
#11: 2015-03-01 08:20:00 B 1.00000
#12: 2015-03-01 08:12:00 C 5.00000
#13: 2015-03-01 08:20:00 C 0.00000
id loc t.arrive t.leave idle
1 78 A 2015-03-01 08:09:36 2015-03-01 08:09:58 22
2 78 A 2015-03-01 08:09:34 2015-03-01 08:10:09 35
df.in <- df.in %>%
mutate(arrive_min=round_date(t.arrive, "mins"))
df2 <- df.in %>%
group_by(id, loc, arrive_min) %>%
do(data.frame(id=.$id, loc=.$loc, mins = .$arrive_min + (0:floor(.$idle/60))*60))
df.in$mins <- df.in$arrive_min
left_join(df2, df.in, by=c("id", "loc", "mins")) %>%
group_by(id, loc, arrive_min.x) %>%
do(data.frame(min1=.$mins, idle=c(rep(60, .$idle[1]/60), .$idle[1]%%60))) %>%
group_by(min1, loc) %>%
summarise(ave_idle=mean(idle))
# min1 loc.x ave_idle
# <dttm> <fctr> <dbl>
#1 2015-03-01 08:00:00 B 60.00000
#2 2015-03-01 08:01:00 B 60.00000
#3 2015-03-01 08:02:00 B 60.00000
#4 2015-03-01 08:03:00 B 12.00000
#5 2015-03-01 08:10:00 A 51.66667
#6 2015-03-01 08:11:00 A 60.00000
#7 2015-03-01 08:12:00 A 21.50000
#8 2015-03-01 08:12:00 C 5.00000
#9 2015-03-01 08:17:00 B 28.00000
#10 2015-03-01 08:20:00 B 1.00000
#11 2015-03-01 08:20:00 C 0.00000
#12 2015-03-01 08:23:00 A 30.00000
#13 2015-03-01 08:24:00 A 46.00000