Показывать общие точки данных (пересечение) по уровням в раскрывающемся меню / фильтре флажков R / plotly / crossstalk - PullRequest
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Показывать общие точки данных (пересечение) по уровням в раскрывающемся меню / фильтре флажков R / plotly / crossstalk

0 голосов
/ 08 ноября 2019

У меня есть точечная диаграмма с добавленным флажком через bscols crosstalk (). Когда я выбираю, скажем, Animal_Biology, Bioinformatics_Computational_Biology и Cell_Biology из раскрывающегося меню, я бы хотел, чтобы на диаграмме разброса были показаны ТОЛЬКО исследователи (точки данных), которые участвуют в этих трех темах, а не все во всех трех областях исследования. ,Любые идеи, как я могу достичь этого? Спасибо заранее и любая помощь / предложение высоко ценится.

Оскар. 

### create ShareData object
`shared_data <- indPlotlyMetadataLong %>% SharedData$new()

mcaPlot <-  shared_data %>% 
   plot_ly(x = ~Dim.1 , y = ~Dim.2, alpha = 0.9,  text = ~researcher,),
          color = ~research_field, 
          marker = list(size = 11),
          hoverinfo = "text") %>% 
  add_markers() %>% 
  highlight(persistent = TRUE) %>% 
  hide_legend() %>% 
  layout(title = "MCA of CSB data", 
         xaxis = list(title = "PC 1", range = c(-1, 1.2)),
         yaxis = list(title = "PC 2", range = c(-1, 1.2)))

### Add checkboxes to mcaPlot
bscols(widths = c(4, 8), 
             filter_checkbox(id = "research_field", label = "Select a research field",
                                         sharedData = shared_data, group = ~research_field,
                                         allLevels = TRUE, inline = FALSE, columns = 2),
             mcaPlot)`


Here is my indPlotlyMetadataLong data -used to create shared_data:

structure(list(Dim.1 = c(0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751, 
0.154663044922868, 0.039412348935979, -0.238553016168864, -0.857636033313211, 
0.136180995471785, -0.023405229926155, 0.309675654958752, 0.956678913668931, 
-0.402039082922732, -0.083222278467751, 0.154663044922868, 0.039412348935979, 
-0.238553016168864, -0.857636033313211, 0.136180995471785, -0.023405229926155, 
0.309675654958752, 0.956678913668931, -0.402039082922732, -0.083222278467751
), Dim.2 = c(0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
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-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
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-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
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0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473, 0.282285353128673, 
-0.111166763161315, -0.505836530845472, 0.271456399109778, -0.170820535747068, 
0.585172813105297, 0.347440487953943, -0.242846745355895, 0.450494439934673, 
-0.527719500890473, 0.282285353128673, -0.111166763161315, -0.505836530845472, 
0.271456399109778, -0.170820535747068, 0.585172813105297, 0.347440487953943, 
-0.242846745355895, 0.450494439934673, -0.527719500890473), researcher = c(1L, 
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7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
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7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
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7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), research_field = c(NA, 
NA, "Animal_Biology", "Animal_Biology", "Animal_Biology", "Animal_Biology", 
NA, NA, NA, "Animal_Biology", NA, NA, NA, NA, "Bioinformatics_Computational_Biology", 
"Bioinformatics_Computational_Biology", "Bioinformatics_Computational_Biology", 
NA, NA, NA, "Biotechnology", NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, "Cell_Biology", "Cell_Biology", NA, NA, NA, NA, NA, 
"Cell_Biology", NA, NA, NA, NA, NA, NA, NA, "Chemical_Biology", 
NA, NA, NA, "Developmental_Biology", "Developmental_Biology", 
NA, "Developmental_Biology", NA, NA, "Developmental_Biology", 
NA, "Developmental_Biology", NA, NA, NA, NA, NA, "Evolutionary_Biology", 
NA, NA, NA, NA, "Genetics", NA, "Genetics", NA, "Genetics", NA, 
NA, "Genetics", NA, "Genetics", NA, NA, NA, NA, "Genomics", "Genomics", 
NA, "Genomics", NA, "Genomics", NA, NA, NA, NA, NA, NA, NA, NA, 
"Lecturer", NA, NA, NA, NA, NA, NA, NA, "Metabolomics", NA, NA, 
NA, "Microbiology", NA, NA, NA, NA, NA, NA, "Microbiology", NA, 
NA, "Molecular_Biology", "Molecular_Biology", "Molecular_Biology", 
NA, "Molecular_Biology", "Molecular_Biology", "Molecular_Biology", 
"Molecular_Biology", NA, "Molecular_Biology", NA, NA, NA, "Neurobiology", 
"Neurobiology", "Neurobiology", NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, "Pathology", NA, NA, NA, NA, NA, "Physiology", NA, 
NA, NA, NA, NA, NA, NA, "Plant_Biology", NA, NA, NA, NA, "Plant_Biology", 
"Plant_Biology", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, "Structural_Biology", NA, NA, NA, NA, NA, NA, NA, "Systems_Biology", 
NA, NA, "Systems_Biology", NA, NA)), row.names = c(1L, 2L, 3L, 
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667L, 668L, 669L, 670L), class = "data.frame")
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