r - 使用R Shiny整合时间序列图和传单 map

我的数据/结果同时包含一个地理编码位置(纬度/经度)和一个日期/时间戳，我想使用R Shiny与之交互。我创建了R Shiny 的应用程序，其中包含几个传单 map (传单R包)，还包含时间序列图(dygraphs R包)。我知道如何同步不同的笔形图(https://rstudio.github.io/dygraphs/gallery-synchronization.html)，但也不确定如何将其同步到传单 map 。我的问题是如何将所有图形最好地链接在一起，所以当我在传单图上选择一个区域或在dygraph时间序列图上选择一个时间段时，所有其他图都全部更新为仅显示经过过滤的数据？

我曾经以为是要使用传单插件，但不确定如何使用R/shiny来做到这一点？例如，我看到一些传单插件提供了对包含日期/时间信息(http://apps.socib.es/Leaflet.TimeDimension/examples/)的 map 进行动画处理的功能。另一个问题是，是否有任何文档/示例显示了如何使用R Shiny处理传单插件？

我认为可以提取从时间序列图(图表)中选择的时间/日期，但不确定是否/如何提取R Shining中传单 map 上显示的区域。我的最后一个问题是，是否有可能提取显示传单 map 的区域，以便更新时间序列图。

在此先感谢您提供任何有关如何使用R Shiny将传单 map 与时间序列图(即dygraph)耦合的建议!

最佳答案

这可能是一个持续的讨论，而不是一个单一的答案。

幸运的是，您的问题涉及RStudio创建的htmlwidgets，后者也制作了Shiny。他们付出了额外的努力将Shiny通讯集成到dygraphs和leaflet中。其他许多htmlwidgets并非如此。对于Shiny之外的htmlwidget内部通信的更广泛讨论，我建议遵循this Github issue。

第1部分-传单控制图

作为我的第一个示例，我们将让leaflet控制dygraphs，因此，单击墨西哥的一个州将dygraph图限制为该州。我应该赞扬这三个例子。

Kyle Walker's Rpub Mexico Choropleth Leaflet

Shiny example included in leaflet

Diego Valle Crime in Mexico project

R代码

  # one piece of an answer to this StackOverflow question
  #  http://stackoverflow.com/questions/31814037/integrating-time-series-graphs-and-leaflet-maps-using-r-shiny

  # for this we'll use Kyle Walker's rpubs example
  #   http://rpubs.com/walkerke/leaflet_choropleth
  # combined with data from Diego Valle's crime in Mexico project
  #   https://github.com/diegovalle/mxmortalitydb

  # we'll also build on the shiny example included in leaflet
  #  https://github.com/rstudio/leaflet/blob/master/inst/examples/shiny.R

  library(shiny)
  library(leaflet)
  library(dygraphs)
  library(rgdal)

  # let's build this in advance so we don't download the
  #    data every time
  tmp <- tempdir()
  url <- "http://personal.tcu.edu/kylewalker/data/mexico.zip"
  file <- basename(url)
  download.file(url, file)
  unzip(file, exdir = tmp)
  mexico <- {
    readOGR(dsn = tmp, layer = "mexico", encoding = "UTF-8")
    #delete our files since no longer need
    on.exit({unlink(tmp);unlink(file)})
  }
  pal <- colorQuantile("YlGn", NULL, n = 5)

  leaf_mexico <- leaflet(data = mexico) %>%
    addTiles() %>%
    addPolygons(fillColor = ~pal(gdp08),
                fillOpacity = 0.8,
                color = "#BDBDC3",
                weight = 1,
                layerId = ~id)

  # now let's get our time series data from Diego Valle
  crime_mexico <- jsonlite::fromJSON(
    "https://rawgit.com/diegovalle/crimenmexico.diegovalle.net/master/assets/json/states.json"
  )

  ui <- fluidPage(
    leafletOutput("map1"),
    dygraphOutput("dygraph1",height = 200),
    textOutput("message", container = h3)
  )

  server <- function(input, output, session) {
    v <- reactiveValues(msg = "")

    output$map1 <- renderLeaflet({
      leaf_mexico
    })

    output$dygraph1 <- renderDygraph({
      # start dygraph with all the states
      crime_wide <- reshape(
        crime_mexico$hd[,c("date","rate","state_code"),drop=F],
        v.names="rate",
        idvar = "date",
        timevar="state_code",
        direction="wide"
      )
      colnames(crime_wide) <- c("date",as.character(mexico$state))
      rownames(crime_wide) <- as.Date(crime_wide$date)
      dygraph(
        crime_wide[,-1]
      )
    })

    observeEvent(input$map1_shape_mouseover, {
      v$msg <- paste("Mouse is over shape", input$map1_shape_mouseover$id)
    })
    observeEvent(input$map1_shape_mouseout, {
      v$msg <- ""
    })
    observeEvent(input$map1_shape_click, {
      v$msg <- paste("Clicked shape", input$map1_shape_click$id)
      #  on our click let's update the dygraph to only show
      #    the time series for the clicked
      state_crime_data <- subset(crime_mexico$hd,state_code == input$map1_shape_click$id)
      rownames(state_crime_data) <- as.Date(state_crime_data$date)
      output$dygraph1 <- renderDygraph({
        dygraph(
          xts::as.xts(state_crime_data[,"rate",drop=F]),
          ylab = paste0(
            "homicide rate ",
            as.character(mexico$state[input$map1_shape_click$id])
          )
        )
      })
    })
    observeEvent(input$map1_zoom, {
      v$msg <- paste("Zoom changed to", input$map1_zoom)
    })
    observeEvent(input$map1_bounds, {
      v$msg <- paste("Bounds changed to", paste(input$map1_bounds, collapse = ", "))
    })

    output$message <- renderText(v$msg)
  }

  shinyApp(ui, server)

第2部分控制图单张+第1部分控制图单张

# one piece of an answer to this StackOverflow question
#  http://stackoverflow.com/questions/31814037/integrating-time-series-graphs-and-leaflet-maps-using-r-shiny

# for this we'll use Kyle Walker's rpubs example
#   http://rpubs.com/walkerke/leaflet_choropleth
# combined with data from Diego Valle's crime in Mexico project
#   https://github.com/diegovalle/mxmortalitydb

# we'll also build on the shiny example included in dygraphs
#  https://github.com/rstudio/leaflet/blob/master/inst/examples/shiny.R

library(shiny)
library(leaflet)
library(dygraphs)
library(dplyr)
library(rgdal)

# let's build this in advance so we don't download the
#    data every time
tmp <- tempdir()
url <- "http://personal.tcu.edu/kylewalker/data/mexico.zip"
file <- basename(url)
download.file(url, file)
unzip(file, exdir = tmp)
mexico <- {
  #delete our files since no longer need
  on.exit({unlink(tmp);unlink(file)})
  readOGR(dsn = tmp, layer = "mexico", encoding = "UTF-8")
}

# now let's get our time series data from Diego Valle
crime_mexico <- jsonlite::fromJSON(
  "https://rawgit.com/diegovalle/crimenmexico.diegovalle.net/master/assets/json/states.json"
)

# instead of the gdp data, let's use mean homicide_rate
#   for our choropleth
mexico$homicide <- crime_mexico$hd %>%
  group_by( state_code ) %>%
  summarise( homicide = mean(rate) ) %>%
  ungroup() %>%
  select( homicide ) %>%
  unlist


pal <- colorBin(
  palette = RColorBrewer::brewer.pal(n=9,"YlGn")[-(1:2)]
  , domain = c(0,50)
  , bins =7
)

popup <- paste0("<strong>Estado: </strong>",
                      mexico$name,
                      "<br><strong>Homicide Rate: </strong>",
                      round(mexico$homicide,2)
          )

leaf_mexico <- leaflet(data = mexico) %>%
  addTiles() %>%
  addPolygons(fillColor = ~pal(homicide),
              fillOpacity = 0.8,
              color = "#BDBDC3",
              weight = 1,
              layerId = ~id,
              popup = popup
              )


ui <- fluidPage(
  leafletOutput("map1"),
  dygraphOutput("dygraph1",height = 200),
  textOutput("message", container = h3)
)

server <- function(input, output, session) {
  v <- reactiveValues(msg = "")

  output$map1 <- renderLeaflet({
    leaf_mexico
  })

  output$dygraph1 <- renderDygraph({
    # start dygraph with all the states
    crime_wide <- reshape(
      crime_mexico$hd[,c("date","rate","state_code"),drop=F],
      v.names="rate",
      idvar = "date",
      timevar="state_code",
      direction="wide"
    )
    colnames(crime_wide) <- c("date",as.character(mexico$state))
    rownames(crime_wide) <- as.Date(crime_wide$date)
    dygraph( crime_wide[,-1])  %>%
      dyLegend( show = "never" )
  })

  observeEvent(input$dygraph1_date_window, {
    if(!is.null(input$dygraph1_date_window)){
      # get the new mean based on the range selected by dygraph
      mexico$filtered_rate <- crime_mexico$hd %>%
      filter(
              as.Date(date) >= as.Date(input$dygraph1_date_window[[1]]),
              as.Date(date) <= as.Date(input$dygraph1_date_window[[2]])
            ) %>%
      group_by( state_code ) %>%
      summarise( homicide = mean(rate) ) %>%
      ungroup() %>%
      select( homicide ) %>%
      unlist

      # leaflet comes with this nice feature leafletProxy
      #  to avoid rebuilding the whole map
      #  let's use it
      leafletProxy( "map1", data = mexico  ) %>%
        removeShape( layerId = ~id ) %>%
        addPolygons( fillColor = ~pal( filtered_rate ),
                    fillOpacity = 0.8,
                    color = "#BDBDC3",
                    weight = 1,
                    layerId = ~id,
                    popup = paste0("<strong>Estado: </strong>",
                        mexico$name,
                        "<br><strong>Homicide Rate: </strong>",
                        round(mexico$filtered_rate,2)
                    )
                    )
    }
  })

  observeEvent(input$map1_shape_click, {
    v$msg <- paste("Clicked shape", input$map1_shape_click$id)
    #  on our click let's update the dygraph to only show
    #    the time series for the clicked
    state_crime_data <- subset(crime_mexico$hd,state_code == input$map1_shape_click$id)
    rownames(state_crime_data) <- as.Date(state_crime_data$date)
    output$dygraph1 <- renderDygraph({
      dygraph(
        xts::as.xts(state_crime_data[,"rate",drop=F]),
        ylab = paste0(
          "homicide rate ",
          as.character(mexico$state[input$map1_shape_click$id])
        )
      )
    })
  })

}

shinyApp(ui, server)

关于r - 使用R Shiny整合时间序列图和传单 map ，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/31814037/