R Plotly-堆積的條形圖無法正確分組數據

Question

我敢肯定這是一個相對簡單的問題，但我發現它很乏味。

我正在嘗試在Plotly中繪制交互式堆積的條形圖，其中x軸上的日期和Y軸上的床位數按“等級”分組。 在下面，我包括了str（），head（），我的代碼和現有的輸出。

一些指針將不勝感激。

我覺得最好的方法是按日期以某種特定的評分將床的總和與數據匯總-但我不確定該如何處理...

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" ...

頭（）

              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

現有代碼

plot_ly(test2, 
        x = ~Date, y = ~Beds, color = ~Rating, marker = col, type = "bar"
        )%>%
   layout(barmode = "stack")

問題的形象

Answer 1

嘗試使用dplyr::count來執行此操作（與plyr::count function稍有不同）。 這是使用ggplotly函數的示例。

該函數基本上總結了使用數據group_by ， sum()或n()與加權（重量），然后ungroup()都在同一個命令。 由於每個日期已經有多個Bed計數（在評分之內），因此需要通過Beds對計數進行“加權”。

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()