如何在R中对动画上的图例级别进行绘图排序
我想在R中使用plotly进行动画打印，当我在不使用帧选项的情况下进行普通打印时，图例的级别按我所需的顺序排列，但当我添加帧选项时，图例的级别无序。我不知道如何解决这个问题 以下是我的代码和结果：如何在R中对动画上的图例级别进行绘图排序,r,plotly,R,Plotly,我想在R中使用plotly进行动画打印，当我在不使用帧选项的情况下进行普通打印时，图例的级别按我所需的顺序排列，但当我添加帧选项时，图例的级别无序。我不知道如何解决这个问题 以下是我的代码和结果： 我需要图例级别具有特定顺序： 当我运行此命令时： data_plot$var_group = factor(data_plot$var_group, c("R_D","R_C-","R_C+","R_B-",&qu
- 我需要图例级别具有特定顺序：
- 当我运行此命令时：
data_plot$var_group = factor(data_plot$var_group, c("R_D","R_C-","R_C+","R_B-","R_B+","R_A-","R_A+"))
> levels(data_plot$var_group)
[1] "R_D" "R_C-" "R_C+" "R_B-" "R_B+" "R_A-" "R_A+"
base <- plot_ly(x = data_plot[,"var_X"],
y = data_plot[,"var_Y"],
size = data_plot[,"var_population"] ,
color = data_plot[,"var_group"],
hoverinfo = "text",
type='scatter',
mode='markers',
showlegend = T)
base <- plot_ly(x = data_plot[,"var_X"],
y = data_plot[,"var_Y"],
size = data_plot[,"var_population"] ,
color = data_plot[,"var_group"],
hoverinfo = "text",
frame = data_plot[,'var_year'],
type='scatter',
mode='markers',
showlegend = T)
> data_plot
var_group var_year var_population var_X var_Y
1 R_A+ 2010 441586 7.971633 46.61086
2 R_A+ 2011 441586 19.797109 40.76217
3 R_A+ 2012 441586 19.766277 46.02090
4 R_A+ 2013 441586 16.504366 51.60697
5 R_A+ 2014 441586 6.629429 42.00877
6 R_A+ 2015 441586 10.207176 50.59914
7 R_A+ 2016 441586 8.113253 50.51997
8 R_A+ 2017 441586 13.809222 45.65124
9 R_A+ 2018 441586 21.204559 54.54564
10 R_A+ 2019 441586 16.323557 50.61546
11 R_A+ 2020 441586 9.395053 46.67655
12 R_A- 2010 752842 13.748339 47.09175
13 R_A- 2011 752842 15.945362 45.58824
14 R_A- 2012 752842 14.878077 48.51219
15 R_A- 2013 752842 13.462869 50.68530
16 R_A- 2014 752842 13.736505 49.01226
17 R_A- 2015 752842 13.782079 45.78995
18 R_A- 2016 752842 13.089312 47.15687
19 R_A- 2017 752842 14.296593 45.50281
20 R_A- 2018 752842 16.116978 46.94360
21 R_A- 2019 752842 15.477477 46.29914
22 R_A- 2020 752842 13.771521 46.58368
23 R_B+ 2010 779656 13.932735 48.31006
24 R_B+ 2011 779656 14.388946 51.73630
25 R_B+ 2012 779656 15.367567 47.42770
26 R_B+ 2013 779656 15.349203 43.48819
27 R_B+ 2014 779656 14.545901 49.89104
28 R_B+ 2015 779656 14.073454 48.59113
29 R_B+ 2016 779656 14.669861 46.76586
30 R_B+ 2017 779656 14.235979 48.20083
31 R_B+ 2018 779656 15.690158 48.89362
32 R_B+ 2019 779656 14.141815 47.11869
33 R_B+ 2020 779656 14.196556 45.63945
34 R_B- 2010 1241331 13.850250 47.31124
35 R_B- 2011 1241331 15.615122 48.40796
36 R_B- 2012 1241331 15.821155 47.71673
37 R_B- 2013 1241331 14.745171 48.99816
38 R_B- 2014 1241331 14.179987 49.49550
39 R_B- 2015 1241331 13.511352 44.73631
40 R_B- 2016 1241331 13.518238 48.37322
41 R_B- 2017 1241331 14.509204 48.63535
42 R_B- 2018 1241331 15.114272 50.32725
43 R_B- 2019 1241331 15.210563 46.65757
44 R_B- 2020 1241331 13.324742 46.36694
45 R_C+ 2010 1944078 14.224204 47.98332
46 R_C+ 2011 1944078 14.264518 48.04793
47 R_C+ 2012 1944078 14.941984 46.64478
48 R_C+ 2013 1944078 14.466030 47.96907
49 R_C+ 2014 1944078 14.725540 47.61507
50 R_C+ 2015 1944078 14.168740 47.53845
51 R_C+ 2016 1944078 14.174140 46.54725
52 R_C+ 2017 1944078 14.982952 47.07512
53 R_C+ 2018 1944078 14.531390 47.99920
54 R_C+ 2019 1944078 14.935974 46.80847
55 R_C+ 2020 1944078 13.561471 46.98083
56 R_C- 2010 1502848 14.980631 47.66160
57 R_C- 2011 1502848 14.655282 46.63385
58 R_C- 2012 1502848 13.883987 48.41544
59 R_C- 2013 1502848 14.474711 46.26512
60 R_C- 2014 1502848 15.225393 48.90736
61 R_C- 2015 1502848 14.834675 48.89604
62 R_C- 2016 1502848 14.319218 47.17255
63 R_C- 2017 1502848 15.215459 47.63817
64 R_C- 2018 1502848 14.090972 46.29257
65 R_C- 2019 1502848 15.105556 45.71682
66 R_C- 2020 1502848 14.497683 50.18621
67 R_D 2010 893489 14.540623 47.03672
68 R_D 2011 893489 14.636348 46.55389
69 R_D 2012 893489 14.931650 46.60181
70 R_D 2013 893489 13.671536 46.17947
71 R_D 2014 893489 14.756695 48.01608
72 R_D 2015 893489 14.665928 47.21726
73 R_D 2016 893489 15.407930 48.55170
74 R_D 2017 893489 14.602070 50.54327
75 R_D 2018 893489 14.551677 48.19881
76 R_D 2019 893489 14.713524 47.78081
77 R_D 2020 893489 14.583820 47.31329