使用 ggplot 在 POSIXct 截距处添加垂直线
Add vertical lines at POSIXct intercepts using ggplot
我正在绘制车站自行车可用性的时间序列图。到目前为止，我能够按 10 分钟的时间间隔绘制可用性。请注意，created_at.x 是 POSIXct 格式。
> head(Pershing, n =20)
id status available_bike_count available_dock_count created_at.x station_summary_id citibike_station_id latitude longitude
41473 293 Active 2 57 2015-10-01 05:20:08 82792 519 40.75188 -73.9777
41474 293 Active 3 56 2015-10-01 09:20:10 82816 519 40.75188 -73.9777
41475 293 Active 4 52 2015-10-01 12:00:06 82832 519 40.75188 -73.9777
41476 293 Active 3 56 2015-10-01 04:50:06 82789 519 40.75188 -73.9777
41477 293 Active 3 56 2015-10-01 06:30:08 82799 519 40.75188 -73.9777
41478 293 Active 2 57 2015-10-01 05:10:07 82791 519 40.75188 -73.9777
41479 293 Active 9 47 2015-10-01 12:40:06 82836 519 40.75188 -73.9777
41480 293 Active 2 57 2015-10-01 07:10:06 82803 519 40.75188 -73.9777
41481 293 Active 1 57 2015-10-01 00:00:12 82760 519 40.75188 -73.9777
41482 293 Active 3 56 2015-10-01 05:00:07 82790 519 40.75188 -73.9777
41483 293 Active 1 56 2015-10-01 02:50:07 82777 519 40.75188 -73.9777
41484 293 Active 3 56 2015-10-01 08:40:06 82812 519 40.75188 -73.9777
41485 293 Active 49 7 2015-10-01 22:50:07 82897 519 40.75188 -73.9777
41486 293 Active 11 44 2015-10-01 16:40:07 82860 519 40.75188 -73.9777
41487 293 Active 3 54 2015-10-01 01:40:06 82770 519 40.75188 -73.9777
41488 293 Active 5 52 2015-10-01 11:10:07 82827 519 40.75188 -73.9777
41489 293 Active 9 47 2015-10-01 18:40:13 82872 519 40.75188 -73.9777
41490 293 Active 8 48 2015-10-01 12:20:08 82834 519 40.75188 -73.9777
41491 293 Active 2 56 2015-10-01 00:40:06 82764 519 40.75188 -73.9777
41492 293 Active 4 52 2015-10-01 12:50:06 82837 519 40.75188 -73.9777
ggplot(data=Pershing,aes(x= created_at.x, y=available_bike_count)) +
geom_point(colour="red") +
ylab("Available Bikes") +
xlab("time")
我想添加显示时间范围的垂直线（实际上是柱状图）。时间范围应从 Pershing_trucks$stoptime 开始并在 Pershing_trucks$starttime
结束
> Pershing_trucks
X bikeid end.station.id start.station.id diff.time
10 2785 19911 359 519 930
12 3052 23301 359 519 1406
14 3496 23818 359 519 1164
15 3778 23185 359 519 1515
17 3795 21630 359 519 1563
21 5046 22144 359 519 884
22 5063 19132 359 519 1396
24 5461 22994 359 519 1574
25 5861 22176 359 519 1394
27 5956 14716 359 519 1932
32 6341 18099 359 519 1267
33 6415 19432 359 519 1143
34 6763 17121 359 519 827
35 6800 22616 359 519 1693
38 7694 20754 359 519 1690
39 8224 15323 359 519 2465
40 8626 23472 359 519 1046
43 8903 19348 359 519 1724
45 8952 24236 359 519 1289
49 9664 24246 359 519 2723
50 9729 18330 359 519 2030
51 9730 18330 359 519 2438
55 10460 22067 359 519 1069
56 10461 22067 359 519 1635
59 11906 22430 359 519 617
62 12310 17727 359 519 1749
64 12557 15174 359 519 1280
65 12568 24123 359 519 2843
72 13572 20687 359 519 1541
77 15530 22913 359 519 1052
80 15863 16252 359 519 893
82 16901 24265 359 519 3316
85 17731 23792 359 519 751
87 19130 17144 359 519 2162
89 21064 15553 359 519 1817
91 21572 22077 359 519 719
92 21789 19456 359 519 3313
95 22822 19639 359 519 1837
98 24345 20860 359 519 1682
102 25520 17632 359 519 1553
112 28679 20514 359 519 3297
120 30827 19110 359 519 3118
121 31258 16744 359 519 3081
stoptime starttime midtime
10 2015-10-01 08:30:16 2015-10-01 08:45:46 2015-10-01 08:38:01
12 2015-10-01 08:20:30 2015-10-01 08:43:56 2015-10-01 08:32:13
14 2015-10-01 06:23:14 2015-10-01 06:42:38 2015-10-01 06:32:56
15 2015-10-01 07:04:10 2015-10-01 07:29:25 2015-10-01 07:16:47
17 2015-10-01 07:40:59 2015-10-01 08:07:02 2015-10-01 07:54:00
21 2015-10-01 06:51:46 2015-10-01 07:06:30 2015-10-01 06:59:08
22 2015-10-01 07:43:37 2015-10-01 08:06:53 2015-10-01 07:55:15
24 2015-10-01 06:20:39 2015-10-01 06:46:53 2015-10-01 06:33:46
25 2015-10-01 06:46:05 2015-10-01 07:09:19 2015-10-01 06:57:42
27 2015-10-01 08:12:16 2015-10-01 08:44:28 2015-10-01 08:28:22
32 2015-10-01 06:23:19 2015-10-01 06:44:26 2015-10-01 06:33:52
33 2015-10-01 06:25:58 2015-10-01 06:45:01 2015-10-01 06:35:29
34 2015-10-01 06:33:12 2015-10-01 06:46:59 2015-10-01 06:40:05
35 2015-10-01 07:23:30 2015-10-01 07:51:43 2015-10-01 07:37:36
38 2015-10-01 06:41:24 2015-10-01 07:09:34 2015-10-01 06:55:29
39 2015-10-01 06:52:20 2015-10-01 07:33:25 2015-10-01 07:12:52
40 2015-10-01 06:47:52 2015-10-01 07:05:18 2015-10-01 06:56:35
43 2015-10-01 07:37:51 2015-10-01 08:06:35 2015-10-01 07:52:13
45 2015-10-01 06:48:08 2015-10-01 07:09:37 2015-10-01 06:58:52
49 2015-10-01 07:01:33 2015-10-01 07:46:56 2015-10-01 07:24:14
50 2015-10-01 07:00:07 2015-10-01 07:33:57 2015-10-01 07:17:02
51 2015-10-01 07:55:18 2015-10-01 08:35:56 2015-10-01 08:15:37
55 2015-10-01 07:30:05 2015-10-01 07:47:54 2015-10-01 07:38:59
56 2015-10-01 08:16:32 2015-10-01 08:43:47 2015-10-01 08:30:09
59 2015-10-01 07:17:48 2015-10-01 07:28:05 2015-10-01 07:22:56
62 2015-10-01 07:25:07 2015-10-01 07:54:16 2015-10-01 07:39:41
64 2015-10-01 07:25:30 2015-10-01 07:46:50 2015-10-01 07:36:10
65 2015-10-01 09:34:44 2015-10-01 10:22:07 2015-10-01 09:58:25
72 2015-10-01 08:40:39 2015-10-01 09:06:20 2015-10-01 08:53:29
77 2015-10-01 07:47:57 2015-10-01 08:05:29 2015-10-01 07:56:43
80 2015-10-01 08:49:59 2015-10-01 09:04:52 2015-10-01 08:57:25
82 2015-10-01 08:59:52 2015-10-01 09:55:08 2015-10-01 09:27:30
85 2015-10-01 07:51:10 2015-10-01 08:03:41 2015-10-01 07:57:25
87 2015-10-01 08:06:23 2015-10-01 08:42:25 2015-10-01 08:24:24
89 2015-10-01 08:15:09 2015-10-01 08:45:26 2015-10-01 08:30:17
91 2015-10-01 08:50:32 2015-10-01 09:02:31 2015-10-01 08:56:31
92 2015-10-01 08:53:47 2015-10-01 09:49:00 2015-10-01 09:21:23
95 2015-10-01 08:20:05 2015-10-01 08:50:42 2015-10-01 08:35:23
98 2015-10-01 08:34:41 2015-10-01 09:02:43 2015-10-01 08:48:42
102 2015-10-01 08:37:07 2015-10-01 09:03:00 2015-10-01 08:50:03
112 2015-10-01 08:51:15 2015-10-01 09:46:12 2015-10-01 09:18:43
120 2015-10-01 08:56:43 2015-10-01 09:48:41 2015-10-01 09:22:42
121 2015-10-01 09:43:33 2015-10-01 10:34:54 2015-10-01 10:09:13
> dput(Pershing_trucks)
structure(list(X = c(2785L, 3052L, 3496L, 3778L, 3795L, 5046L,
5063L, 5461L, 5861L, 5956L, 6341L, 6415L, 6763L, 6800L, 7694L,
8224L, 8626L, 8903L, 8952L, 9664L, 9729L, 9730L, 10460L, 10461L,
11906L, 12310L, 12557L, 12568L, 13572L, 15530L, 15863L, 16901L,
17731L, 19130L, 21064L, 21572L, 21789L, 22822L, 24345L, 25520L,
28679L, 30827L, 31258L), bikeid = c(19911L, 23301L, 23818L, 23185L,
21630L, 22144L, 19132L, 22994L, 22176L, 14716L, 18099L, 19432L,
17121L, 22616L, 20754L, 15323L, 23472L, 19348L, 24236L, 24246L,
18330L, 18330L, 22067L, 22067L, 22430L, 17727L, 15174L, 24123L,
20687L, 22913L, 16252L, 24265L, 23792L, 17144L, 15553L, 22077L,
19456L, 19639L, 20860L, 17632L, 20514L, 19110L, 16744L), end.station.id = c(359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L), start.station.id = c(519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L), diff.time = c(930L,
1406L, 1164L, 1515L, 1563L, 884L, 1396L, 1574L, 1394L, 1932L,
1267L, 1143L, 827L, 1693L, 1690L, 2465L, 1046L, 1724L, 1289L,
2723L, 2030L, 2438L, 1069L, 1635L, 617L, 1749L, 1280L, 2843L,
1541L, 1052L, 893L, 3316L, 751L, 2162L, 1817L, 719L, 3313L, 1837L,
1682L, 1553L, 3297L, 3118L, 3081L), stoptime = structure(c(1443706216,
1443705630, 1443698594, 1443701050, 1443703259, 1443700306, 1443703417,
1443698439, 1443699965, 1443705136, 1443698599, 1443698758, 1443699192,
1443702210, 1443699684, 1443700340, 1443700072, 1443703071, 1443700088,
1443700893, 1443700807, 1443704118, 1443702605, 1443705392, 1443701868,
1443702307, 1443702330, 1443710084, 1443706839, 1443703677, 1443707399,
1443707992, 1443703870, 1443704783, 1443705309, 1443707432, 1443707627,
1443705605, 1443706481, 1443706627, 1443707475, 1443707803, 1443710613
), class = c("POSIXct", "POSIXt"), tzone = "EST"), starttime = structure(c(1443707146,
1443707036, 1443699758, 1443702565, 1443704822, 1443701190, 1443704813,
1443700013, 1443701359, 1443707068, 1443699866, 1443699901, 1443700019,
1443703903, 1443701374, 1443702805, 1443701118, 1443704795, 1443701377,
1443703616, 1443702837, 1443706556, 1443703674, 1443707027, 1443702485,
1443704056, 1443703610, 1443712927, 1443708380, 1443704729, 1443708292,
1443711308, 1443704621, 1443706945, 1443707126, 1443708151, 1443710940,
1443707442, 1443708163, 1443708180, 1443710772, 1443710921, 1443713694
), class = c("POSIXct", "POSIXt"), tzone = "EST"), midtime = structure(c(1443706681,
1443706333, 1443699176, 1443701807.5, 1443704040.5, 1443700748,
1443704115, 1443699226, 1443700662, 1443706102, 1443699232.5,
1443699329.5, 1443699605.5, 1443703056.5, 1443700529, 1443701572.5,
1443700595, 1443703933, 1443700732.5, 1443702254.5, 1443701822,
1443705337, 1443703139.5, 1443706209.5, 1443702176.5, 1443703181.5,
1443702970, 1443711505.5, 1443707609.5, 1443704203, 1443707845.5,
1443709650, 1443704245.5, 1443705864, 1443706217.5, 1443707791.5,
1443709283.5, 1443706523.5, 1443707322, 1443707403.5, 1443709123.5,
1443709362, 1443712153.5), class = c("POSIXct", "POSIXt"), tzone = "")), .Names = c("X",
"bikeid", "end.station.id", "start.station.id", "diff.time",
"stoptime", "starttime", "midtime"), row.names = c(10L, 12L,
14L, 15L, 17L, 21L, 22L, 24L, 25L, 27L, 32L, 33L, 34L, 35L, 38L,
39L, 40L, 43L, 45L, 49L, 50L, 51L, 55L, 56L, 59L, 62L, 64L, 65L,
72L, 77L, 80L, 82L, 85L, 87L, 89L, 91L, 92L, 95L, 98L, 102L,
112L, 120L, 121L), class = "data.frame")
理想情况下，剧情应该是这样的：
我认为这是一个相当复杂的问题。但是，我可以给你它的一部分和解决它的想法。
我的想法是创建一个循环函数来创建这样的东西。它可能无法按原样工作，但通过一些调整，它可能可以。
vertBars <- lapply(seq_along(Pershing_trucks), function(x) {
# Start with a vector that is empty
vertBars <- 0
# Add a ggplot call that creates a grey box with the desired
# start and stop times for a single line number "x"
a <- geom_rect(data=Pershing_trucks, aes(xmin=stoptime[x],
xmax=starttime[x], ymin=-Inf, ymax=+Inf), fill='gray80',
alpha=0.02)
# Add the plot "a" to the empty vector you created. This will loop and
# add all of the bars that you require without having to manually specify them
vertBars <- vertBars + a
# Return the vector "vertBars" that should be able to be added to your plot
return(vertBars)
}
此外，为这样的情节添加 + theme_bw() 可能是个聪明的主意，因为它看起来更干净。
您的要求很简单，我们只需要添加一个 geom_rect 层与您的 Pershing_trucks 数据。
我不想费心导入你的 Pershing 数据框，所以在从你的 dput() 获得 Pershing_trucks 数据后，我 sampled/simulated 一个 Pershing 像这样的数据框：
Pershing = data.frame(
created.at.x = sample(Pershing_trucks$starttime, 20),
available_bike_count = rpois(20, 20)
)
让我们创建一个基本图，我们可以用不同的方式添加矩形：
persh_plot =
ggplot(
data = Pershing,
aes(x = created.at.x, y = available_bike_count)
) +
geom_point(color = "red") +
ylab("Available Bikes") +
xlab("time") + theme_bw()
我喜欢另一个答案的geom_rect，但是lapply是不必要的。此外，美学常量（如 ymin = -Inf）应始终设置在 之外 aes()。我们还需要设置 inherit.aes = FALSE 以防止继承早期的 x 和 y 映射：
persh_plot +
geom_rect(
data = Pershing_trucks,
aes(xmin = stoptime,
xmax = starttime),
ymin = -Inf,
ymax = Inf,
fill = 'gray80',
alpha = 0.2,
inherit.aes = FALSE
)
我不确定您要显示什么 - 但此处矩形上的 alpha 透明度给出了相当好的重叠指示。请记住，我的 Pershing 数据与您的数据具有相似的结构，但是是模拟的。您可能需要在矩形图层之后添加 geom_point() 图层，以便点位于顶部。
这与您想要的输出不太匹配，所以这里有一个没有任何填充的轮廓矩形版本，并且 y 最小值和最大值分别为 0 和 20，如您的绘图所示。对我来说看起来很糟糕，但对每个人来说：
persh_plot +
geom_rect(
data = Pershing_trucks,
aes(xmin = stoptime,
xmax = starttime),
ymin = 0,
ymax = 20,
color = "black",
fill = NA,
size = 1,
inherit.aes = FALSE
) +
expand_limits(y = 0) # just to force y down to 0 with my simulated data
我正在绘制车站自行车可用性的时间序列图。到目前为止，我能够按 10 分钟的时间间隔绘制可用性。请注意，created_at.x 是 POSIXct 格式。
> head(Pershing, n =20)
id status available_bike_count available_dock_count created_at.x station_summary_id citibike_station_id latitude longitude
41473 293 Active 2 57 2015-10-01 05:20:08 82792 519 40.75188 -73.9777
41474 293 Active 3 56 2015-10-01 09:20:10 82816 519 40.75188 -73.9777
41475 293 Active 4 52 2015-10-01 12:00:06 82832 519 40.75188 -73.9777
41476 293 Active 3 56 2015-10-01 04:50:06 82789 519 40.75188 -73.9777
41477 293 Active 3 56 2015-10-01 06:30:08 82799 519 40.75188 -73.9777
41478 293 Active 2 57 2015-10-01 05:10:07 82791 519 40.75188 -73.9777
41479 293 Active 9 47 2015-10-01 12:40:06 82836 519 40.75188 -73.9777
41480 293 Active 2 57 2015-10-01 07:10:06 82803 519 40.75188 -73.9777
41481 293 Active 1 57 2015-10-01 00:00:12 82760 519 40.75188 -73.9777
41482 293 Active 3 56 2015-10-01 05:00:07 82790 519 40.75188 -73.9777
41483 293 Active 1 56 2015-10-01 02:50:07 82777 519 40.75188 -73.9777
41484 293 Active 3 56 2015-10-01 08:40:06 82812 519 40.75188 -73.9777
41485 293 Active 49 7 2015-10-01 22:50:07 82897 519 40.75188 -73.9777
41486 293 Active 11 44 2015-10-01 16:40:07 82860 519 40.75188 -73.9777
41487 293 Active 3 54 2015-10-01 01:40:06 82770 519 40.75188 -73.9777
41488 293 Active 5 52 2015-10-01 11:10:07 82827 519 40.75188 -73.9777
41489 293 Active 9 47 2015-10-01 18:40:13 82872 519 40.75188 -73.9777
41490 293 Active 8 48 2015-10-01 12:20:08 82834 519 40.75188 -73.9777
41491 293 Active 2 56 2015-10-01 00:40:06 82764 519 40.75188 -73.9777
41492 293 Active 4 52 2015-10-01 12:50:06 82837 519 40.75188 -73.9777
ggplot(data=Pershing,aes(x= created_at.x, y=available_bike_count)) +
geom_point(colour="red") +
ylab("Available Bikes") +
xlab("time")
我想添加显示时间范围的垂直线（实际上是柱状图）。时间范围应从 Pershing_trucks$stoptime 开始并在 Pershing_trucks$starttime
> Pershing_trucks
X bikeid end.station.id start.station.id diff.time
10 2785 19911 359 519 930
12 3052 23301 359 519 1406
14 3496 23818 359 519 1164
15 3778 23185 359 519 1515
17 3795 21630 359 519 1563
21 5046 22144 359 519 884
22 5063 19132 359 519 1396
24 5461 22994 359 519 1574
25 5861 22176 359 519 1394
27 5956 14716 359 519 1932
32 6341 18099 359 519 1267
33 6415 19432 359 519 1143
34 6763 17121 359 519 827
35 6800 22616 359 519 1693
38 7694 20754 359 519 1690
39 8224 15323 359 519 2465
40 8626 23472 359 519 1046
43 8903 19348 359 519 1724
45 8952 24236 359 519 1289
49 9664 24246 359 519 2723
50 9729 18330 359 519 2030
51 9730 18330 359 519 2438
55 10460 22067 359 519 1069
56 10461 22067 359 519 1635
59 11906 22430 359 519 617
62 12310 17727 359 519 1749
64 12557 15174 359 519 1280
65 12568 24123 359 519 2843
72 13572 20687 359 519 1541
77 15530 22913 359 519 1052
80 15863 16252 359 519 893
82 16901 24265 359 519 3316
85 17731 23792 359 519 751
87 19130 17144 359 519 2162
89 21064 15553 359 519 1817
91 21572 22077 359 519 719
92 21789 19456 359 519 3313
95 22822 19639 359 519 1837
98 24345 20860 359 519 1682
102 25520 17632 359 519 1553
112 28679 20514 359 519 3297
120 30827 19110 359 519 3118
121 31258 16744 359 519 3081
stoptime starttime midtime
10 2015-10-01 08:30:16 2015-10-01 08:45:46 2015-10-01 08:38:01
12 2015-10-01 08:20:30 2015-10-01 08:43:56 2015-10-01 08:32:13
14 2015-10-01 06:23:14 2015-10-01 06:42:38 2015-10-01 06:32:56
15 2015-10-01 07:04:10 2015-10-01 07:29:25 2015-10-01 07:16:47
17 2015-10-01 07:40:59 2015-10-01 08:07:02 2015-10-01 07:54:00
21 2015-10-01 06:51:46 2015-10-01 07:06:30 2015-10-01 06:59:08
22 2015-10-01 07:43:37 2015-10-01 08:06:53 2015-10-01 07:55:15
24 2015-10-01 06:20:39 2015-10-01 06:46:53 2015-10-01 06:33:46
25 2015-10-01 06:46:05 2015-10-01 07:09:19 2015-10-01 06:57:42
27 2015-10-01 08:12:16 2015-10-01 08:44:28 2015-10-01 08:28:22
32 2015-10-01 06:23:19 2015-10-01 06:44:26 2015-10-01 06:33:52
33 2015-10-01 06:25:58 2015-10-01 06:45:01 2015-10-01 06:35:29
34 2015-10-01 06:33:12 2015-10-01 06:46:59 2015-10-01 06:40:05
35 2015-10-01 07:23:30 2015-10-01 07:51:43 2015-10-01 07:37:36
38 2015-10-01 06:41:24 2015-10-01 07:09:34 2015-10-01 06:55:29
39 2015-10-01 06:52:20 2015-10-01 07:33:25 2015-10-01 07:12:52
40 2015-10-01 06:47:52 2015-10-01 07:05:18 2015-10-01 06:56:35
43 2015-10-01 07:37:51 2015-10-01 08:06:35 2015-10-01 07:52:13
45 2015-10-01 06:48:08 2015-10-01 07:09:37 2015-10-01 06:58:52
49 2015-10-01 07:01:33 2015-10-01 07:46:56 2015-10-01 07:24:14
50 2015-10-01 07:00:07 2015-10-01 07:33:57 2015-10-01 07:17:02
51 2015-10-01 07:55:18 2015-10-01 08:35:56 2015-10-01 08:15:37
55 2015-10-01 07:30:05 2015-10-01 07:47:54 2015-10-01 07:38:59
56 2015-10-01 08:16:32 2015-10-01 08:43:47 2015-10-01 08:30:09
59 2015-10-01 07:17:48 2015-10-01 07:28:05 2015-10-01 07:22:56
62 2015-10-01 07:25:07 2015-10-01 07:54:16 2015-10-01 07:39:41
64 2015-10-01 07:25:30 2015-10-01 07:46:50 2015-10-01 07:36:10
65 2015-10-01 09:34:44 2015-10-01 10:22:07 2015-10-01 09:58:25
72 2015-10-01 08:40:39 2015-10-01 09:06:20 2015-10-01 08:53:29
77 2015-10-01 07:47:57 2015-10-01 08:05:29 2015-10-01 07:56:43
80 2015-10-01 08:49:59 2015-10-01 09:04:52 2015-10-01 08:57:25
82 2015-10-01 08:59:52 2015-10-01 09:55:08 2015-10-01 09:27:30
85 2015-10-01 07:51:10 2015-10-01 08:03:41 2015-10-01 07:57:25
87 2015-10-01 08:06:23 2015-10-01 08:42:25 2015-10-01 08:24:24
89 2015-10-01 08:15:09 2015-10-01 08:45:26 2015-10-01 08:30:17
91 2015-10-01 08:50:32 2015-10-01 09:02:31 2015-10-01 08:56:31
92 2015-10-01 08:53:47 2015-10-01 09:49:00 2015-10-01 09:21:23
95 2015-10-01 08:20:05 2015-10-01 08:50:42 2015-10-01 08:35:23
98 2015-10-01 08:34:41 2015-10-01 09:02:43 2015-10-01 08:48:42
102 2015-10-01 08:37:07 2015-10-01 09:03:00 2015-10-01 08:50:03
112 2015-10-01 08:51:15 2015-10-01 09:46:12 2015-10-01 09:18:43
120 2015-10-01 08:56:43 2015-10-01 09:48:41 2015-10-01 09:22:42
121 2015-10-01 09:43:33 2015-10-01 10:34:54 2015-10-01 10:09:13
> dput(Pershing_trucks)
structure(list(X = c(2785L, 3052L, 3496L, 3778L, 3795L, 5046L,
5063L, 5461L, 5861L, 5956L, 6341L, 6415L, 6763L, 6800L, 7694L,
8224L, 8626L, 8903L, 8952L, 9664L, 9729L, 9730L, 10460L, 10461L,
11906L, 12310L, 12557L, 12568L, 13572L, 15530L, 15863L, 16901L,
17731L, 19130L, 21064L, 21572L, 21789L, 22822L, 24345L, 25520L,
28679L, 30827L, 31258L), bikeid = c(19911L, 23301L, 23818L, 23185L,
21630L, 22144L, 19132L, 22994L, 22176L, 14716L, 18099L, 19432L,
17121L, 22616L, 20754L, 15323L, 23472L, 19348L, 24236L, 24246L,
18330L, 18330L, 22067L, 22067L, 22430L, 17727L, 15174L, 24123L,
20687L, 22913L, 16252L, 24265L, 23792L, 17144L, 15553L, 22077L,
19456L, 19639L, 20860L, 17632L, 20514L, 19110L, 16744L), end.station.id = c(359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L,
359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L, 359L), start.station.id = c(519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L,
519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L, 519L), diff.time = c(930L,
1406L, 1164L, 1515L, 1563L, 884L, 1396L, 1574L, 1394L, 1932L,
1267L, 1143L, 827L, 1693L, 1690L, 2465L, 1046L, 1724L, 1289L,
2723L, 2030L, 2438L, 1069L, 1635L, 617L, 1749L, 1280L, 2843L,
1541L, 1052L, 893L, 3316L, 751L, 2162L, 1817L, 719L, 3313L, 1837L,
1682L, 1553L, 3297L, 3118L, 3081L), stoptime = structure(c(1443706216,
1443705630, 1443698594, 1443701050, 1443703259, 1443700306, 1443703417,
1443698439, 1443699965, 1443705136, 1443698599, 1443698758, 1443699192,
1443702210, 1443699684, 1443700340, 1443700072, 1443703071, 1443700088,
1443700893, 1443700807, 1443704118, 1443702605, 1443705392, 1443701868,
1443702307, 1443702330, 1443710084, 1443706839, 1443703677, 1443707399,
1443707992, 1443703870, 1443704783, 1443705309, 1443707432, 1443707627,
1443705605, 1443706481, 1443706627, 1443707475, 1443707803, 1443710613
), class = c("POSIXct", "POSIXt"), tzone = "EST"), starttime = structure(c(1443707146,
1443707036, 1443699758, 1443702565, 1443704822, 1443701190, 1443704813,
1443700013, 1443701359, 1443707068, 1443699866, 1443699901, 1443700019,
1443703903, 1443701374, 1443702805, 1443701118, 1443704795, 1443701377,
1443703616, 1443702837, 1443706556, 1443703674, 1443707027, 1443702485,
1443704056, 1443703610, 1443712927, 1443708380, 1443704729, 1443708292,
1443711308, 1443704621, 1443706945, 1443707126, 1443708151, 1443710940,
1443707442, 1443708163, 1443708180, 1443710772, 1443710921, 1443713694
), class = c("POSIXct", "POSIXt"), tzone = "EST"), midtime = structure(c(1443706681,
1443706333, 1443699176, 1443701807.5, 1443704040.5, 1443700748,
1443704115, 1443699226, 1443700662, 1443706102, 1443699232.5,
1443699329.5, 1443699605.5, 1443703056.5, 1443700529, 1443701572.5,
1443700595, 1443703933, 1443700732.5, 1443702254.5, 1443701822,
1443705337, 1443703139.5, 1443706209.5, 1443702176.5, 1443703181.5,
1443702970, 1443711505.5, 1443707609.5, 1443704203, 1443707845.5,
1443709650, 1443704245.5, 1443705864, 1443706217.5, 1443707791.5,
1443709283.5, 1443706523.5, 1443707322, 1443707403.5, 1443709123.5,
1443709362, 1443712153.5), class = c("POSIXct", "POSIXt"), tzone = "")), .Names = c("X",
"bikeid", "end.station.id", "start.station.id", "diff.time",
"stoptime", "starttime", "midtime"), row.names = c(10L, 12L,
14L, 15L, 17L, 21L, 22L, 24L, 25L, 27L, 32L, 33L, 34L, 35L, 38L,
39L, 40L, 43L, 45L, 49L, 50L, 51L, 55L, 56L, 59L, 62L, 64L, 65L,
72L, 77L, 80L, 82L, 85L, 87L, 89L, 91L, 92L, 95L, 98L, 102L,
112L, 120L, 121L), class = "data.frame")
理想情况下，剧情应该是这样的：
我认为这是一个相当复杂的问题。但是，我可以给你它的一部分和解决它的想法。
我的想法是创建一个循环函数来创建这样的东西。它可能无法按原样工作，但通过一些调整，它可能可以。
vertBars <- lapply(seq_along(Pershing_trucks), function(x) {
# Start with a vector that is empty
vertBars <- 0
# Add a ggplot call that creates a grey box with the desired
# start and stop times for a single line number "x"
a <- geom_rect(data=Pershing_trucks, aes(xmin=stoptime[x],
xmax=starttime[x], ymin=-Inf, ymax=+Inf), fill='gray80',
alpha=0.02)
# Add the plot "a" to the empty vector you created. This will loop and
# add all of the bars that you require without having to manually specify them
vertBars <- vertBars + a
# Return the vector "vertBars" that should be able to be added to your plot
return(vertBars)
}
此外，为这样的情节添加 + theme_bw() 可能是个聪明的主意，因为它看起来更干净。
您的要求很简单，我们只需要添加一个 geom_rect 层与您的 Pershing_trucks 数据。
我不想费心导入你的 Pershing 数据框，所以在从你的 dput() 获得 Pershing_trucks 数据后，我 sampled/simulated 一个 Pershing 像这样的数据框：
Pershing = data.frame(
created.at.x = sample(Pershing_trucks$starttime, 20),
available_bike_count = rpois(20, 20)
)
让我们创建一个基本图，我们可以用不同的方式添加矩形：
persh_plot =
ggplot(
data = Pershing,
aes(x = created.at.x, y = available_bike_count)
) +
geom_point(color = "red") +
ylab("Available Bikes") +
xlab("time") + theme_bw()
我喜欢另一个答案的geom_rect，但是lapply是不必要的。此外，美学常量（如 ymin = -Inf）应始终设置在 之外 aes()。我们还需要设置 inherit.aes = FALSE 以防止继承早期的 x 和 y 映射：
persh_plot +
geom_rect(
data = Pershing_trucks,
aes(xmin = stoptime,
xmax = starttime),
ymin = -Inf,
ymax = Inf,
fill = 'gray80',
alpha = 0.2,
inherit.aes = FALSE
)
我不确定您要显示什么 - 但此处矩形上的 alpha 透明度给出了相当好的重叠指示。请记住，我的 Pershing 数据与您的数据具有相似的结构，但是是模拟的。您可能需要在矩形图层之后添加 geom_point() 图层，以便点位于顶部。
这与您想要的输出不太匹配，所以这里有一个没有任何填充的轮廓矩形版本，并且 y 最小值和最大值分别为 0 和 20，如您的绘图所示。对我来说看起来很糟糕，但对每个人来说：
persh_plot +
geom_rect(
data = Pershing_trucks,
aes(xmin = stoptime,
xmax = starttime),
ymin = 0,
ymax = 20,
color = "black",
fill = NA,
size = 1,
inherit.aes = FALSE
) +
expand_limits(y = 0) # just to force y down to 0 with my simulated data