R Plotly - 堆积条形图无法正确分组数据
R Plotly - Stacked Bar Chart failing to group data properly
我确定这是一个相对简单的问题，但我发现它很乏味。
我正在尝试在 Plotly 中绘制一个交互式堆积条形图，其中日期横跨 x 轴，床位数在 Y 轴上，按评级分组。下面我包括了 str()、head()、我的代码和现有的输出。
不胜感激。
我觉得最好的方法是以某种方式按日期汇总数据，以及特定评级的床位总和 - 但我不确定我将如何处理这个...
str()
'data.frame': 3492 obs. of 4 variables:
$ Date : Date, format: "2016-05-01" ...
$ Rating : Factor w/ 5 levels "Good","Inadequate",..: 1 1 1 1 4 1 1 1 1 4 ...
$ Beds : num 4 50 0 41 7 0 0 6 4 26 ...
$ Location: chr "73ecf8c8" "8b42c57b" "ba87a0b9" "56c5af23" ...
head()
Date Rating Beds Location
845296 2016-05-01 Good 4 73ecf8c8
531140 2017-07-01 Good 50 8b42c57b
258467 2016-12-01 Good 0 ba87a0b9
717015 2017-03-01 Good 41 56c5af23
26558 2016-08-01 Requires improvement 7 2a3b1665
32035 2017-11-01 Good 0 19a428f8
537744 2017-12-01 Good 0 f238d5b0
132722 2016-05-01 Good 6 281b0906
141156 2017-04-01 Good 4 b9a84e23
448129 2017-02-01 Requires improvement 26 b17b1679
215566 2017-01-01 Good 39 3deb6a53
93926 2017-04-01 Good 42 ea25b9ac
50825 2016-10-01 Requires improvement 38 a123e5e2
350187 2017-09-01 Good 0 20a5b3ff
228490 2015-12-01 Good 5 26bbe2d3
619449 2017-09-01 Requires improvement 64 4f1850cb
277463 2015-11-01 Requires improvement 60 da021815
747589 2018-07-01 Good 0 b680e86a
572946 2018-07-01 Good 36 8f768366
130828 2016-08-01 Good 14 26140cb7
176208 2017-01-01 Good 8 90ea5a9b
131422 2015-09-01 Good 34 3f710dda
314684 2017-07-01 Requires improvement 0 542ecec0
27104 2017-10-01 Requires improvement 14 a0a39a69
385850 2018-02-01 Inadequate 12 2c2157a3
131793 2017-06-01 Requires improvement 40 c5279a90
476514 2018-03-01 Good 61 1ede253c
234275 2017-12-01 Good 3 5b843535
814308 2016-08-01 Good 0 8e8ae05c
681168 2018-01-01 Good 19 c7503f95
228857 2016-03-01 Good 4 b79ec2e1
5076 2016-12-01 Good 9 c4571226
516624 2018-03-01 Good 4 3c4a60e8
328782 2016-11-01 Good 8 509886d8
454544 2017-10-01 Good 8 551e223
273560 2016-06-01 Requires improvement 57 fa936153
734068 2017-12-01 Good 0 1be00d0a
316369 2016-01-01 Inadequate 27 6a70309d
818050 2016-12-01 Good 0 feaefffd
99811 2017-03-01 Good 7 2832404f
376905 2016-10-01 Requires improvement 0 45494c27
18056 2018-02-01 Requires improvement 15 92d0f90e
183216 2017-04-01 Good 20 52061c2b
139057 2016-09-01 Requires improvement 42 38d2867b
349667 2016-09-01 Good 94 ae061300
538397 2018-04-01 Good 19 fe926d3c
70481 2015-11-01 Requires improvement 14 d889131
749222 2017-11-01 Good 0 990f6742
64034 2018-01-01 Good 8 c79a60e3
154178 2018-02-01 Good 66 74192594
85413 2017-01-01 Good 33 61bd8955
286676 2018-01-01 Requires improvement 29 289ee997
838167 2018-07-01 Good 0 3e3be7b6
594222 2017-05-01 Good 45 73149b93
341018 2017-06-01 Good 80 9c79bed4
87187 2016-11-01 Good 7 6511d087
57191 2016-10-01 Requires improvement 0 489cccef
572701 2017-05-01 Good 0 9e5b7624
433898 2017-02-01 Requires improvement 36 9fb506e9
147450 2016-09-01 Good 51 f755015c
147465 2017-12-01 Good 51 f755015c
768482 2018-04-01 Good 35 2ec82812
164492 2016-11-01 Good 29 37b6722e
317253 2018-05-01 Good 51 330236c2
106128 2015-09-01 Good 14 73fc9b5d
719658 2018-03-01 Good 44 d01cd06a
22099 2016-01-01 Good 25 1278109f
25584 2018-03-01 Good 16 f7949387
128071 2016-01-01 Good 61 6b5c264d
114202 2017-07-01 Good 0 acc76673
212877 2017-10-01 Good 0 8435c62b
838998 2017-03-01 Requires improvement 21 d107525f
534825 2016-08-01 Good 34 f33ed6dd
810734 2017-11-01 Inadequate 0 cef20cb9
93621 2017-05-01 Good 23 76cc5458
855084 2016-01-01 Good 0 343f87e1
290641 2017-02-01 Good 76 7c96b559
422625 2016-02-01 Requires improvement 44 38d19478
698519 2018-05-01 Good 0 7e37c12
451978 2018-01-01 Good 28 5c85d81e
277454 2018-01-01 Good 30 6c01aba2
327926 2017-06-01 Good 8 4b3ef025
405035 2018-06-01 Good 51 5fb1a56f
458644 2017-11-01 Good 13 8fea9b31
101605 2016-06-01 Good 0 4849d79b
852084 2016-10-01 Good 6 3158ee3e
795288 2016-01-01 Good 0 ae9f7110
218899 2017-02-01 Good 14 2f77717a
352636 2017-11-01 Outstanding 4 3b8be9df
519895 2018-04-01 Requires improvement 33 1cda38ff
434329 2015-12-01 Requires improvement 62 669494f8
517045 2018-04-01 Good 0 3f317755
293071 2017-01-01 Good 16 e2283b4d
109754 2017-12-01 Good 40 6a4e3d80
833849 2018-05-01 Good 0 24c1fd1f
544942 2017-12-01 Good 40 6e9808b5
42046 2016-08-01 Requires improvement 3 b9c2bf48
264610 2016-05-01 Good 58 e3f2f21b
360231 2018-04-01 Good 26 8e4b7c51
405704 2016-11-01 Good 19 3224233f
729533 2016-01-01 Good 40 3f816ee2
587697 2017-01-01 Requires improvement 0 8a107710
802252 2018-04-01 Good 0 1be00d0a
722684 2017-04-01 Good 0 c29c49d0
506127 2017-02-01 Requires improvement 0 a4584633
224827 2017-01-01 Good 0 fbf59fb
532047 2017-06-01 Good 0 ccb9197a
30224 2016-04-01 Good 7 57ddb719
313612 2017-03-01 Good 54 fd3b1c23
437224 2017-08-01 Good 10 1cae80aa
39140 2018-03-01 Good 9 50828278
673780 2017-04-01 Good 0 4e09a7e3
713658 2016-07-01 Good 5 4cf5b0c8
356121 2018-05-01 Good 42 ca78522
266006 2016-07-01 Good 0 8a123db2
137548 2017-05-01 Good 4 ee22dee9
302152 2016-08-01 Requires improvement 120 be5c07bc
107929 2016-01-01 Requires improvement 40 ad634df3
129558 2016-12-01 Good 6 7d76fe74
220591 2018-03-01 Good 5 31c4f6f5
410000 2016-09-01 Good 0 8536f1ae
512051 2017-04-01 Good 0 e2dbda9
669160 2017-03-01 Good 0 5d0dd1c9
502046 2017-11-01 Inadequate 16 c44f2103
778049 2016-12-01 Good 9 d9df8a9b
327324 2017-06-01 Good 6 82925f7a
10338 2017-01-01 Good 23 9367642e
380084 2016-07-01 Good 0 26dba9aa
177099 2017-10-01 Good 29 55c5b0c4
451721 2017-03-01 Good 10 adabb9bd
148916 2016-12-01 Requires improvement 33 a4b1aed0
331453 2017-03-01 Good 31 c8c635e4
215781 2015-11-01 Good 54 fc3ec716
703935 2016-05-01 Requires improvement 60 e864967b
422946 2016-08-01 Requires improvement 20 57686493
689546 2018-06-01 Requires improvement 0 d187188c
81389 2017-08-01 Good 15 c6876e99
217176 2017-12-01 Good 54 dbc379c1
861416 2015-12-01 Requires improvement 3 3e421008
182407 2016-12-01 Good 11 db975de3
830441 2016-04-01 Good 60 8c435ffd
395178 2017-12-01 Good 46 ca8ec055
279329 2016-10-01 Good 0 41fde67d
286571 2017-05-01 Good 0 814050b3
519878 2016-11-01 Requires improvement 33 1cda38ff
356862 2018-07-01 Good 0 23df4b6a
349400 2017-10-01 Good 25 345b540d
424046 2017-04-01 Requires improvement 22 ac9c36ec
703641 2018-06-01 Good 11 b9d33a8c
158566 2018-02-01 Good 0 ece2a325
805260 2018-02-01 Good 50 ec13225
266666 2015-10-01 Requires improvement 24 3b276650
489618 2018-02-01 Good 15 86171b0c
253591 2018-03-01 Good 49 1971d20a
158697 2017-04-01 Requires improvement 23 3b0964d5
702350 2017-07-01 Good 33 6660201e
221603 2016-09-01 Good 5 d0ee1cb0
629620 2018-03-01 Good 0 ad593a5e
364422 2017-08-01 Good 5 10d07106
209910 2017-10-01 Good 45 becda3b9
805251 2017-05-01 Requires improvement 50 ec13225
48490 2017-07-01 Requires improvement 129 b961e4b9
653676 2017-11-01 Good 0 b009efa
104525 2016-11-01 Good 4 af2447cf
136762 2017-08-01 Good 22 3ab84c39
301929 2017-11-01 Good 6 6a9754db
254622 2017-01-01 Good 5 62d29e47
171199 2017-01-01 Good 9 d6b557e5
308962 2017-07-01 Outstanding 0 a1c3486d
679497 2018-03-01 Requires improvement 19 f0b69678
583701 2018-01-01 Good 2 7bed9aee
818816 2015-09-01 Requires improvement 80 ee56de08
470911 2016-06-01 Good 31 df3e2263
324680 2016-01-01 Good 7 6f14dc6e
286625 2016-09-01 Good 9 82987732
851305 2017-11-01 Requires improvement 0 e644f19
431351 2017-09-01 Good 75 cab33e6c
377702 2017-10-01 Good 10 cb16e04f
659315 2017-12-01 Good 46 cd3ed3dd
635446 2017-11-01 Good 3 9d2079f3
723729 2018-07-01 Good 10 e844216b
568093 2017-09-01 Good 28 64585bf9
43025 2016-02-01 Requires improvement 0 bd8776e3
304749 2017-04-01 Good 17 88bd81a5
104726 2018-01-01 Good 32 4b3eb01e
102932 2016-06-01 Requires improvement 52 8854302
320747 2016-09-01 Requires improvement 6 ea96ec94
211455 2017-11-01 Good 7 960ff19f
365985 2018-07-01 Good 6 2d61675f
112236 2017-02-01 Good 8 18917c87
321448 2016-04-01 Good 8 a2020b57
252145 2017-04-01 Good 4 856d0879
592746 2018-03-01 Good 0 b4fcf4c
92992 2016-05-01 Good 15 c7652688
242329 2016-07-01 Requires improvement 34 99205575
482507 2016-01-01 Requires improvement 59 1bf96a6e
438733 2016-09-01 Requires improvement 0 9bd0afa6
849800 2016-02-01 Good 0 d03c7ca7
665019 2018-01-01 Requires improvement 0 1e370102
55080 2016-07-01 Good 6 b7a5aa66
326518 2018-07-01 Good 3 a59654e7
744501 2017-08-01 Good 5 4f23064f
810313 2017-09-01 Good 17 ce5f8661
859849 2016-03-01 Inadequate 0 7e2226fd
552668 2017-12-01 Good 21 94b3b830
657502 2017-03-01 Good 19 bc403e1b
837974 2016-10-01 Good 18 70db26b0
552564 2018-01-01 Good 0 df5a1352
422645 2017-10-01 Good 44 38d19478
408661 2016-12-01 Good 26 36a954d
234622 2018-04-01 Requires improvement 0 768a842
118553 2018-02-01 Good 37 3c7b7179
68081 2017-09-01 Good 35 744d32bd
164336 2018-06-01 Good 29 c1fcb144
85725 2018-04-01 Requires improvement 24 341c2894
771416 2018-03-01 Requires improvement 0 f4b7202b
811557 2018-03-01 Good 50 2c99c463
356332 2016-02-01 Requires improvement 24 893bd8f1
271763 2016-07-01 Good 55 b4973db4
844597 2018-03-01 Good 14 dd7afdc4
108342 2017-12-01 Good 0 3e7910a8
397322 2017-04-01 Good 6 2eea4528
279122 2018-06-01 Good 90 64256055
326607 2017-03-01 Good 3 f95fba06
12693 2017-09-01 Good 0 2c1ab4c8
835690 2016-07-01 Requires improvement 46 912e4df4
500979 2016-09-01 Good 6 d001d2f
228199 2016-06-01 Good 16 a63ca739
317800 2015-12-01 Requires improvement 50 3dbd50ea
406448 2016-01-01 Inadequate 9 a877167e
383427 2015-10-01 Good 86 ad4d7ba2
336054 2017-04-01 Requires improvement 80 e47a766e
746245 2015-10-01 Good 52 b71907f1
552129 2016-04-01 Good 0 9f418780
525654 2016-04-01 Good 0 8369a68f
132864 2016-07-01 Good 15 70340e4b
67588 2016-12-01 Requires improvement 5 d8d8e1f9
608797 2015-10-01 Good 4 5ab14628
475296 2016-08-01 Requires improvement 35 1f637bf2
163400 2015-11-01 Requires improvement 35 5d6f7386
550891 2018-06-01 Good 30 4c6f5407
270228 2018-04-01 Good 16 21df63bf
60885 2017-10-01 Good 40 6c7ab064
685038 2017-06-01 Good 30 686c921f
466925 2017-04-01 Good 1 8b4e8494
835711 2018-04-01 Inadequate 46 912e4df4
523228 2017-02-01 Good 0 fac83fc1
309203 2017-11-01 Good 69 79864c4f
290655 2018-04-01 Requires improvement 76 7c96b559
847687 2018-05-01 Good 0 9cd483a1
现有代码
plot_ly(test2,
x = ~Date, y = ~Beds, color = ~Rating, marker = col, type = "bar"
)%>%
layout(barmode = "stack")
问题图片
尝试使用 dplyr::count 来执行此操作（与 plyr::count function 略有不同）。下面是使用 ggplotly 函数的示例。
这个函数基本上使用 group_by、sum() 或 n() 和权重 (wt) 汇总数据，然后 ungroup() 都在一个命令中。
由于每个日期已经有多个床位计数（在评级内），因此计数需要 "weighted" 乘以 Beds。
test2 <- test2 %>%
mutate(Date = lubridate::ymd(Date),
Rating = factor(Rating))
test2 %>%
dplyr::count(Date, wt=Beds, Rating) %>%
ggplot(aes(Date, n, fill = Rating)) +
geom_bar(stat = "identity")
ggplotly()
我确定这是一个相对简单的问题，但我发现它很乏味。
我正在尝试在 Plotly 中绘制一个交互式堆积条形图，其中日期横跨 x 轴，床位数在 Y 轴上，按评级分组。下面我包括了 str()、head()、我的代码和现有的输出。
不胜感激。
我觉得最好的方法是以某种方式按日期汇总数据，以及特定评级的床位总和 - 但我不确定我将如何处理这个...
str()
'data.frame': 3492 obs. of 4 variables:
$ Date : Date, format: "2016-05-01" ...
$ Rating : Factor w/ 5 levels "Good","Inadequate",..: 1 1 1 1 4 1 1 1 1 4 ...
$ Beds : num 4 50 0 41 7 0 0 6 4 26 ...
$ Location: chr "73ecf8c8" "8b42c57b" "ba87a0b9" "56c5af23" ...
head()
Date Rating Beds Location
845296 2016-05-01 Good 4 73ecf8c8
531140 2017-07-01 Good 50 8b42c57b
258467 2016-12-01 Good 0 ba87a0b9
717015 2017-03-01 Good 41 56c5af23
26558 2016-08-01 Requires improvement 7 2a3b1665
32035 2017-11-01 Good 0 19a428f8
537744 2017-12-01 Good 0 f238d5b0
132722 2016-05-01 Good 6 281b0906
141156 2017-04-01 Good 4 b9a84e23
448129 2017-02-01 Requires improvement 26 b17b1679
215566 2017-01-01 Good 39 3deb6a53
93926 2017-04-01 Good 42 ea25b9ac
50825 2016-10-01 Requires improvement 38 a123e5e2
350187 2017-09-01 Good 0 20a5b3ff
228490 2015-12-01 Good 5 26bbe2d3
619449 2017-09-01 Requires improvement 64 4f1850cb
277463 2015-11-01 Requires improvement 60 da021815
747589 2018-07-01 Good 0 b680e86a
572946 2018-07-01 Good 36 8f768366
130828 2016-08-01 Good 14 26140cb7
176208 2017-01-01 Good 8 90ea5a9b
131422 2015-09-01 Good 34 3f710dda
314684 2017-07-01 Requires improvement 0 542ecec0
27104 2017-10-01 Requires improvement 14 a0a39a69
385850 2018-02-01 Inadequate 12 2c2157a3
131793 2017-06-01 Requires improvement 40 c5279a90
476514 2018-03-01 Good 61 1ede253c
234275 2017-12-01 Good 3 5b843535
814308 2016-08-01 Good 0 8e8ae05c
681168 2018-01-01 Good 19 c7503f95
228857 2016-03-01 Good 4 b79ec2e1
5076 2016-12-01 Good 9 c4571226
516624 2018-03-01 Good 4 3c4a60e8
328782 2016-11-01 Good 8 509886d8
454544 2017-10-01 Good 8 551e223
273560 2016-06-01 Requires improvement 57 fa936153
734068 2017-12-01 Good 0 1be00d0a
316369 2016-01-01 Inadequate 27 6a70309d
818050 2016-12-01 Good 0 feaefffd
99811 2017-03-01 Good 7 2832404f
376905 2016-10-01 Requires improvement 0 45494c27
18056 2018-02-01 Requires improvement 15 92d0f90e
183216 2017-04-01 Good 20 52061c2b
139057 2016-09-01 Requires improvement 42 38d2867b
349667 2016-09-01 Good 94 ae061300
538397 2018-04-01 Good 19 fe926d3c
70481 2015-11-01 Requires improvement 14 d889131
749222 2017-11-01 Good 0 990f6742
64034 2018-01-01 Good 8 c79a60e3
154178 2018-02-01 Good 66 74192594
85413 2017-01-01 Good 33 61bd8955
286676 2018-01-01 Requires improvement 29 289ee997
838167 2018-07-01 Good 0 3e3be7b6
594222 2017-05-01 Good 45 73149b93
341018 2017-06-01 Good 80 9c79bed4
87187 2016-11-01 Good 7 6511d087
57191 2016-10-01 Requires improvement 0 489cccef
572701 2017-05-01 Good 0 9e5b7624
433898 2017-02-01 Requires improvement 36 9fb506e9
147450 2016-09-01 Good 51 f755015c
147465 2017-12-01 Good 51 f755015c
768482 2018-04-01 Good 35 2ec82812
164492 2016-11-01 Good 29 37b6722e
317253 2018-05-01 Good 51 330236c2
106128 2015-09-01 Good 14 73fc9b5d
719658 2018-03-01 Good 44 d01cd06a
22099 2016-01-01 Good 25 1278109f
25584 2018-03-01 Good 16 f7949387
128071 2016-01-01 Good 61 6b5c264d
114202 2017-07-01 Good 0 acc76673
212877 2017-10-01 Good 0 8435c62b
838998 2017-03-01 Requires improvement 21 d107525f
534825 2016-08-01 Good 34 f33ed6dd
810734 2017-11-01 Inadequate 0 cef20cb9
93621 2017-05-01 Good 23 76cc5458
855084 2016-01-01 Good 0 343f87e1
290641 2017-02-01 Good 76 7c96b559
422625 2016-02-01 Requires improvement 44 38d19478
698519 2018-05-01 Good 0 7e37c12
451978 2018-01-01 Good 28 5c85d81e
277454 2018-01-01 Good 30 6c01aba2
327926 2017-06-01 Good 8 4b3ef025
405035 2018-06-01 Good 51 5fb1a56f
458644 2017-11-01 Good 13 8fea9b31
101605 2016-06-01 Good 0 4849d79b
852084 2016-10-01 Good 6 3158ee3e
795288 2016-01-01 Good 0 ae9f7110
218899 2017-02-01 Good 14 2f77717a
352636 2017-11-01 Outstanding 4 3b8be9df
519895 2018-04-01 Requires improvement 33 1cda38ff
434329 2015-12-01 Requires improvement 62 669494f8
517045 2018-04-01 Good 0 3f317755
293071 2017-01-01 Good 16 e2283b4d
109754 2017-12-01 Good 40 6a4e3d80
833849 2018-05-01 Good 0 24c1fd1f
544942 2017-12-01 Good 40 6e9808b5
42046 2016-08-01 Requires improvement 3 b9c2bf48
264610 2016-05-01 Good 58 e3f2f21b
360231 2018-04-01 Good 26 8e4b7c51
405704 2016-11-01 Good 19 3224233f
729533 2016-01-01 Good 40 3f816ee2
587697 2017-01-01 Requires improvement 0 8a107710
802252 2018-04-01 Good 0 1be00d0a
722684 2017-04-01 Good 0 c29c49d0
506127 2017-02-01 Requires improvement 0 a4584633
224827 2017-01-01 Good 0 fbf59fb
532047 2017-06-01 Good 0 ccb9197a
30224 2016-04-01 Good 7 57ddb719
313612 2017-03-01 Good 54 fd3b1c23
437224 2017-08-01 Good 10 1cae80aa
39140 2018-03-01 Good 9 50828278
673780 2017-04-01 Good 0 4e09a7e3
713658 2016-07-01 Good 5 4cf5b0c8
356121 2018-05-01 Good 42 ca78522
266006 2016-07-01 Good 0 8a123db2
137548 2017-05-01 Good 4 ee22dee9
302152 2016-08-01 Requires improvement 120 be5c07bc
107929 2016-01-01 Requires improvement 40 ad634df3
129558 2016-12-01 Good 6 7d76fe74
220591 2018-03-01 Good 5 31c4f6f5
410000 2016-09-01 Good 0 8536f1ae
512051 2017-04-01 Good 0 e2dbda9
669160 2017-03-01 Good 0 5d0dd1c9
502046 2017-11-01 Inadequate 16 c44f2103
778049 2016-12-01 Good 9 d9df8a9b
327324 2017-06-01 Good 6 82925f7a
10338 2017-01-01 Good 23 9367642e
380084 2016-07-01 Good 0 26dba9aa
177099 2017-10-01 Good 29 55c5b0c4
451721 2017-03-01 Good 10 adabb9bd
148916 2016-12-01 Requires improvement 33 a4b1aed0
331453 2017-03-01 Good 31 c8c635e4
215781 2015-11-01 Good 54 fc3ec716
703935 2016-05-01 Requires improvement 60 e864967b
422946 2016-08-01 Requires improvement 20 57686493
689546 2018-06-01 Requires improvement 0 d187188c
81389 2017-08-01 Good 15 c6876e99
217176 2017-12-01 Good 54 dbc379c1
861416 2015-12-01 Requires improvement 3 3e421008
182407 2016-12-01 Good 11 db975de3
830441 2016-04-01 Good 60 8c435ffd
395178 2017-12-01 Good 46 ca8ec055
279329 2016-10-01 Good 0 41fde67d
286571 2017-05-01 Good 0 814050b3
519878 2016-11-01 Requires improvement 33 1cda38ff
356862 2018-07-01 Good 0 23df4b6a
349400 2017-10-01 Good 25 345b540d
424046 2017-04-01 Requires improvement 22 ac9c36ec
703641 2018-06-01 Good 11 b9d33a8c
158566 2018-02-01 Good 0 ece2a325
805260 2018-02-01 Good 50 ec13225
266666 2015-10-01 Requires improvement 24 3b276650
489618 2018-02-01 Good 15 86171b0c
253591 2018-03-01 Good 49 1971d20a
158697 2017-04-01 Requires improvement 23 3b0964d5
702350 2017-07-01 Good 33 6660201e
221603 2016-09-01 Good 5 d0ee1cb0
629620 2018-03-01 Good 0 ad593a5e
364422 2017-08-01 Good 5 10d07106
209910 2017-10-01 Good 45 becda3b9
805251 2017-05-01 Requires improvement 50 ec13225
48490 2017-07-01 Requires improvement 129 b961e4b9
653676 2017-11-01 Good 0 b009efa
104525 2016-11-01 Good 4 af2447cf
136762 2017-08-01 Good 22 3ab84c39
301929 2017-11-01 Good 6 6a9754db
254622 2017-01-01 Good 5 62d29e47
171199 2017-01-01 Good 9 d6b557e5
308962 2017-07-01 Outstanding 0 a1c3486d
679497 2018-03-01 Requires improvement 19 f0b69678
583701 2018-01-01 Good 2 7bed9aee
818816 2015-09-01 Requires improvement 80 ee56de08
470911 2016-06-01 Good 31 df3e2263
324680 2016-01-01 Good 7 6f14dc6e
286625 2016-09-01 Good 9 82987732
851305 2017-11-01 Requires improvement 0 e644f19
431351 2017-09-01 Good 75 cab33e6c
377702 2017-10-01 Good 10 cb16e04f
659315 2017-12-01 Good 46 cd3ed3dd
635446 2017-11-01 Good 3 9d2079f3
723729 2018-07-01 Good 10 e844216b
568093 2017-09-01 Good 28 64585bf9
43025 2016-02-01 Requires improvement 0 bd8776e3
304749 2017-04-01 Good 17 88bd81a5
104726 2018-01-01 Good 32 4b3eb01e
102932 2016-06-01 Requires improvement 52 8854302
320747 2016-09-01 Requires improvement 6 ea96ec94
211455 2017-11-01 Good 7 960ff19f
365985 2018-07-01 Good 6 2d61675f
112236 2017-02-01 Good 8 18917c87
321448 2016-04-01 Good 8 a2020b57
252145 2017-04-01 Good 4 856d0879
592746 2018-03-01 Good 0 b4fcf4c
92992 2016-05-01 Good 15 c7652688
242329 2016-07-01 Requires improvement 34 99205575
482507 2016-01-01 Requires improvement 59 1bf96a6e
438733 2016-09-01 Requires improvement 0 9bd0afa6
849800 2016-02-01 Good 0 d03c7ca7
665019 2018-01-01 Requires improvement 0 1e370102
55080 2016-07-01 Good 6 b7a5aa66
326518 2018-07-01 Good 3 a59654e7
744501 2017-08-01 Good 5 4f23064f
810313 2017-09-01 Good 17 ce5f8661
859849 2016-03-01 Inadequate 0 7e2226fd
552668 2017-12-01 Good 21 94b3b830
657502 2017-03-01 Good 19 bc403e1b
837974 2016-10-01 Good 18 70db26b0
552564 2018-01-01 Good 0 df5a1352
422645 2017-10-01 Good 44 38d19478
408661 2016-12-01 Good 26 36a954d
234622 2018-04-01 Requires improvement 0 768a842
118553 2018-02-01 Good 37 3c7b7179
68081 2017-09-01 Good 35 744d32bd
164336 2018-06-01 Good 29 c1fcb144
85725 2018-04-01 Requires improvement 24 341c2894
771416 2018-03-01 Requires improvement 0 f4b7202b
811557 2018-03-01 Good 50 2c99c463
356332 2016-02-01 Requires improvement 24 893bd8f1
271763 2016-07-01 Good 55 b4973db4
844597 2018-03-01 Good 14 dd7afdc4
108342 2017-12-01 Good 0 3e7910a8
397322 2017-04-01 Good 6 2eea4528
279122 2018-06-01 Good 90 64256055
326607 2017-03-01 Good 3 f95fba06
12693 2017-09-01 Good 0 2c1ab4c8
835690 2016-07-01 Requires improvement 46 912e4df4
500979 2016-09-01 Good 6 d001d2f
228199 2016-06-01 Good 16 a63ca739
317800 2015-12-01 Requires improvement 50 3dbd50ea
406448 2016-01-01 Inadequate 9 a877167e
383427 2015-10-01 Good 86 ad4d7ba2
336054 2017-04-01 Requires improvement 80 e47a766e
746245 2015-10-01 Good 52 b71907f1
552129 2016-04-01 Good 0 9f418780
525654 2016-04-01 Good 0 8369a68f
132864 2016-07-01 Good 15 70340e4b
67588 2016-12-01 Requires improvement 5 d8d8e1f9
608797 2015-10-01 Good 4 5ab14628
475296 2016-08-01 Requires improvement 35 1f637bf2
163400 2015-11-01 Requires improvement 35 5d6f7386
550891 2018-06-01 Good 30 4c6f5407
270228 2018-04-01 Good 16 21df63bf
60885 2017-10-01 Good 40 6c7ab064
685038 2017-06-01 Good 30 686c921f
466925 2017-04-01 Good 1 8b4e8494
835711 2018-04-01 Inadequate 46 912e4df4
523228 2017-02-01 Good 0 fac83fc1
309203 2017-11-01 Good 69 79864c4f
290655 2018-04-01 Requires improvement 76 7c96b559
847687 2018-05-01 Good 0 9cd483a1
现有代码
plot_ly(test2,
x = ~Date, y = ~Beds, color = ~Rating, marker = col, type = "bar"
)%>%
layout(barmode = "stack")
问题图片
尝试使用 dplyr::count 来执行此操作（与 plyr::count function 略有不同）。下面是使用 ggplotly 函数的示例。
这个函数基本上使用 group_by、sum() 或 n() 和权重 (wt) 汇总数据，然后 ungroup() 都在一个命令中。
由于每个日期已经有多个床位计数（在评级内），因此计数需要 "weighted" 乘以 Beds。
test2 <- test2 %>%
mutate(Date = lubridate::ymd(Date),
Rating = factor(Rating))
test2 %>%
dplyr::count(Date, wt=Beds, Rating) %>%
ggplot(aes(Date, n, fill = Rating)) +
geom_bar(stat = "identity")
ggplotly()