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。
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/