绘制ggvis面积图
我正在尝试使用ggvis构建交互式面积图时间序列，并沿x轴显示日期，并在y轴上显示床位数。但是，我在图表的绘制方式上遇到了一些问题。
str（）输出
> str(test2)
'data.frame': 5000 obs. of 3 variables:
$ Beds : num 22 0 0 11 18 30 0 0 3 0 ...
$ Date : Date, format: "2015-09-01" "2015-09-01" "2015-09-01" ...
$ Rating: Factor w/ 5 levels "Outstanding",..: 5 5 2 5 2 5 5 5 5 5 ...
数据样本
Date Rating Beds
1 2015-09-01 Good 9
2 2015-09-01 Requires improvement 30
3 2015-09-01 Unrated 8
4 2015-09-01 Good 4
5 2015-09-01 Unrated 3
6 2015-09-01 Unrated 63
7 2015-10-01 Requires improvement 38
8 2015-10-01 Unrated 9
9 2015-10-01 Unrated 4
10 2015-10-01 Good 4
11 2015-10-01 Requires improvement 40
12 2015-10-01 Unrated 6
13 2015-11-01 Good 9
14 2015-11-01 Outstanding 62
15 2015-11-01 Good 4
16 2015-11-01 Unrated 4
17 2015-12-01 Inadequate 22
18 2015-12-01 Requires improvement 30
19 2015-12-01 Requires improvement 60
20 2016-01-01 Good 5
21 2016-01-01 Requires improvement 9
22 2016-01-01 Unrated 40
23 2016-01-01 Unrated 30
24 2016-01-01 Good 4
25 2016-01-01 Good 4
26 2016-01-01 Good 49
27 2016-01-01 Unrated 8
28 2016-01-01 Good 6
29 2016-02-01 Requires improvement 22
30 2016-02-01 Good 13
31 2016-02-01 Unrated 15
32 2016-02-01 Good 4
33 2016-02-01 Good 11
34 2016-02-01 Good 6
35 2016-03-01 Good 6
36 2016-03-01 Good 6
37 2016-03-01 Unrated 40
38 2016-03-01 Good 4
39 2016-03-01 Requires improvement 4
40 2016-03-01 Requires improvement 15
41 2016-03-01 Requires improvement 9
42 2016-04-01 Unrated 4
43 2016-04-01 Unrated 3
44 2016-04-01 Requires improvement 30
45 2016-04-01 Unrated 3
46 2016-04-01 Requires improvement 38
47 2016-04-01 Inadequate 63
48 2016-04-01 Unrated 8
49 2016-05-01 Good 6
50 2016-05-01 Requires improvement 38
51 2016-05-01 Requires improvement 22
52 2016-05-01 Good 3
53 2016-05-01 Good 49
54 2016-05-01 Good 4
55 2016-05-01 Good 11
56 2016-05-01 Inadequate 63
57 2016-06-01 Good 3
58 2016-06-01 Requires improvement 9
59 2016-06-01 Good 22
60 2016-06-01 Requires improvement 38
61 2016-06-01 Good 4
62 2016-07-01 Good 18
63 2016-07-01 Good 55
64 2016-07-01 Unrated 3
65 2016-07-01 Unrated 10
66 2016-07-01 Requires improvement 26
67 2016-07-01 Good 16
68 2016-07-01 Unrated 16
69 2016-08-01 Unrated 14
70 2016-08-01 Unrated 3
71 2016-08-01 Good 18
72 2016-08-01 Good 78
73 2016-08-01 Good 12
74 2016-08-01 Good 34
75 2016-08-01 Requires improvement 26
76 2016-08-01 Outstanding 62
77 2016-08-01 Requires improvement 45
78 2016-09-01 Unrated 16
79 2016-09-01 Good 22
80 2016-09-01 Requires improvement 15
81 2016-09-01 Requires improvement 38
82 2016-09-01 Requires improvement 9
83 2016-10-01 Unrated 4
84 2016-10-01 Good 20
85 2016-10-01 Unrated 8
86 2016-10-01 Good 25
87 2016-11-01 Good 29
88 2016-11-01 Requires improvement 10
89 2016-11-01 Good 59
90 2016-11-01 Good 4
91 2016-11-01 Unrated 8
92 2016-11-01 Good 34
93 2016-11-01 Good 78
94 2016-12-01 Good 59
95 2016-12-01 Unrated 69
96 2016-12-01 Requires improvement 38
97 2016-12-01 Good 6
98 2016-12-01 Good 22
99 2016-12-01 Requires improvement 22
100 2017-01-01 Unrated 8
101 2017-01-01 Requires improvement 10
102 2017-02-01 Good 35
103 2017-02-01 Good 8
104 2017-02-01 Good 6
105 2017-02-01 Good 13
106 2017-02-01 Good 3
107 2017-02-01 Good 4
108 2017-02-01 Requires improvement 49
109 2017-02-01 Good 12
110 2017-02-01 Good 14
111 2017-02-01 Good 18
112 2017-03-01 Requires improvement 30
113 2017-03-01 Good 20
114 2017-03-01 Good 15
115 2017-03-01 Good 3
116 2017-03-01 Good 3
117 2017-03-01 Good 63
118 2017-03-01 Good 3
119 2017-03-01 Good 6
120 2017-03-01 Good 11
121 2017-04-01 Good 8
122 2017-04-01 Good 13
123 2017-04-01 Requires improvement 22
124 2017-04-01 Good 6
125 2017-05-01 Good 4
126 2017-05-01 Outstanding 9
127 2017-05-01 Good 20
128 2017-05-01 Good 6
129 2017-05-01 Good 6
130 2017-05-01 Requires improvement 7
131 2017-05-01 Good 4
132 2017-05-01 Good 63
133 2017-06-01 Requires improvement 22
134 2017-06-01 Good 6
135 2017-06-01 Good 18
136 2017-06-01 Good 5
137 2017-06-01 Good 6
138 2017-06-01 Good 15
139 2017-06-01 Outstanding 29
140 2017-07-01 Requires improvement 22
141 2017-07-01 Inadequate 67
142 2017-07-01 Good 30
143 2017-07-01 Outstanding 9
144 2017-07-01 Good 42
145 2017-07-01 Good 78
146 2017-08-01 Good 4
147 2017-08-01 Good 3
148 2017-08-01 Good 12
149 2017-08-01 Good 4
150 2017-08-01 Good 6
151 2017-08-01 Outstanding 9
152 2017-08-01 Good 15
153 2017-09-01 Good 6
154 2017-09-01 Good 16
155 2017-09-01 Good 50
156 2017-09-01 Requires improvement 49
157 2017-09-01 Good 18
158 2017-10-01 Good 78
159 2017-10-01 Good 4
160 2017-10-01 Good 60
161 2017-10-01 Good 3
162 2017-11-01 Good 4
163 2017-11-01 Good 63
164 2017-11-01 Good 14
165 2017-11-01 Good 4
166 2017-12-01 Good 17
167 2017-12-01 Good 17
168 2017-12-01 Good 4
169 2017-12-01 Good 22
170 2017-12-01 Good 4
171 2018-01-01 Good 55
172 2018-01-01 Good 3
173 2018-01-01 Outstanding 69
174 2018-01-01 Good 4
175 2018-01-01 Good 42
176 2018-02-01 Good 25
177 2018-02-01 Good 6
178 2018-02-01 Requires improvement 67
179 2018-02-01 Good 12
180 2018-02-01 Outstanding 69
181 2018-02-01 Good 4
182 2018-02-01 Good 8
183 2018-03-01 Good 63
184 2018-03-01 Good 42
185 2018-03-01 Good 4
186 2018-04-01 Good 22
187 2018-04-01 Good 25
188 2018-04-01 Requires improvement 67
189 2018-05-01 Good 4
190 2018-05-01 Good 4
191 2018-05-01 Good 20
192 2018-05-01 Good 3
193 2018-05-01 Good 35
194 2018-06-01 Requires improvement 62
195 2018-06-01 Good 22
196 2018-06-01 Good 4
197 2018-06-01 Good 4
198 2018-06-01 Outstanding 69
199 2018-06-01 Good 12
200 2018-07-01 Good 7
使用此示例数据集，我尝试使用以下代码构建面积图：
ggvis(test2, x = ~Date, y = ~Beds, fill = ~Rating)%>%
layer_ribbons()
但是，我得到了以下情节，这远非理想的！
有任何想法为什么会这样？
0 个答案:
没有答案
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