R Shiny:如何根据用户输入数据的位置过滤数据
我有一个闪亮的应用程序正在运行。它在一张传单互动地图上绘制了大约12000套公寓和房间供出租,并根据用户输入的地址在地图上添加了一个标记。这是我的建议。抱歉,如果没有很好的文档记录 有两个不同的数据框对象:一个用于公寓(R Shiny:如何根据用户输入数据的位置过滤数据,r,shiny,leaflet,ggmap,R,Shiny,Leaflet,Ggmap,我有一个闪亮的应用程序正在运行。它在一张传单互动地图上绘制了大约12000套公寓和房间供出租,并根据用户输入的地址在地图上添加了一个标记。这是我的建议。抱歉,如果没有很好的文档记录 有两个不同的数据框对象:一个用于公寓(df.apt),另一个用于房间(df.quartos) 但是,由于应用程序加载的数据量太大,速度有点慢。我想添加一个资源,该资源仅在用户插入地址并选择邻近范围(例如,仅显示距输入地址10公里内的公寓)后才会绘制数据。我该怎么做呢 library(leaflet) library(
df.apt
),另一个用于房间(df.quartos
)
但是,由于应用程序加载的数据量太大,速度有点慢。我想添加一个资源,该资源仅在用户插入地址并选择邻近范围(例如,仅显示距输入地址10公里内的公寓)后才会绘制数据。我该怎么做呢
library(leaflet)
library(shiny)
library(ggmap)
source("post4-prepararshiny.R") #loads data and helper functions
ui = bootstrapPage(
div(class = "outer",
tags$head(
# Include our custom CSS
includeCSS("styles.css"),
includeScript("gomap.js")
),
tags$style(type = "text/css", "html, body {width:100%;height:100%}"),
leafletOutput("mymap", width = "100%", height = "100%"),
absolutePanel(id = "controls",# class = "panel panel-default",
fixed = TRUE,
draggable = TRUE,
top = 60, left = "auto", right = 20, bottom = "auto",
width = 330, height = "auto",
h2("Buscador OLX"),
textInput(inputId = "userlocation",
label = "Digite um endereço\n com pelo menos rua, número, bairro e cidade",
value = ""),
helpText("Exemplo: Rua Dias da Rocha, 85 - Copacabana, Rio de Janeiro - RJ"),
sliderInput(inputId = "distancia", label = "Escolha a distância em km:",
min = 0, max = 30, value = 15),
actionButton("go", "Buscar"),
helpText("Encontre imóveis para alugar perto de onde você quiser!"),
helpText("Cada ponto no mapa representa um imóvel para alugar.",
"A cor de um ponto é determinada pelo valor do aluguel.",
"Clique em um ponto para ter mais informações sobre o imóvel."),
helpText("Mais informações sobre este app em sillasgonzaga.github.io")
)
),
tags$div(id="cite",
'Dados extraídos do OLX em 12/11/2016.', ' Contato: sillasgonzaga.github.io'
)
)
服务器.R
但是,我如何以反应式的方式做到这一点,允许我在用户每次单击按钮时更改距离
列,并更改用于指示接近范围的滑块输入()
注:对不起,葡萄牙语的代码和注释
编辑:已解决
在@HubertL回复后,我找到了一个解决方案。以下是我在服务器上所做的工作:
distance_apt_reactive <- eventReactive(input$go, {
address_latlon <- geocode(input$userlocation)
dist <- distm(x = matrix(data = c(df.apt$lon, df.apt$lat), ncol = 2),
y = c(lon = address_latlon$lon, lat = address_latlon$lat),
fun = distVincentySphere)
dist <- dist/1000
})
apt_reactive <- reactive({df.apt[distance_reactive() < input$distancia,]})
output$mymap <- renderLeaflet({
map <- leaflet() %>%
addTiles() %>%
addProviderTiles("OpenStreetMap.BlackAndWhite") %>%
setView(lng = mean(df.apt$lon), lat = mean(df.apt$lat), zoom = 11) %>%
addLegend(pal = vetorCoresApt, values = df.apt$preco,
position = "bottomright",
layerId = "legend")
map
})
observe({
leafletProxy("mymap") %>%
clearMarkers() %>%
#addMarkers(lng = myadress()$lon, lat = myadress()$lat) %>%
addCircleMarkers(data = apt_reactive(),
lng = ~lon, lat = ~lat,
color = ~vetorCoresQuarto(preco),
opacity = 1.5,
# adicionar popup
popup = textoPopup(apt_reactive(), "apartamento"),
group = "Apartamentos")
})
distance\u apt\u reactive%
addCircleMarkers(数据=apt_reactive(),
液化天然气=~lon,lat=~lat,
颜色=~vetorCoresQuarto(preco),
不透明度=1.5,
#adicionar弹出窗口
popup=textoPopup(apt_reactive(),“Apartmento”),
group=“Apartmentos”)
})
您可以添加一个反应式
,它将根据到地址的距离过滤您的数据。frame
:
apt_reactive <- reactive({
address_latlon <- geocode(input$userlocation)
dist <- distm(x = matrix(data = c(df.apt$lon, df.apt$lat), ncol = 2),
y = c(lon = address_latlon$lon, lat = address_latlon$lat),
fun = distVincentySphere)
apt.df[dist < input$distancia,]
})
借
(对于df.quartos
,对quartos\u reactive
重复相同的过程)您的解决方案非常好,但仍然存在问题。每次用户更改距离范围时,renderLeaflet()
都会渲染一张新地图,这会减慢应用程序的速度。有没有办法让它更平滑?我想知道这是否与proxy()
有关。是的,但是因为渲染点要少得多,所以渲染速度会非常快。但您是对的,可能可以使用proxy
。我会看看这个(对我来说是新的)函数,所以,我在这里做了很多测试,似乎我想要的是根本不可能的。假设您选择3 km作为距离范围。它将仅绘制该范围内的数据。如果我使用滑块将此范围增加到10公里,它将绘制此新范围中包含的新数据。但是,问题是,如果我将其滑回3公里,10公里内的数据不会消失。相反,它将绘制已经绘制的数据。如果我不清楚,请告诉我。
distance_apt_reactive <- eventReactive(input$go, {
address_latlon <- geocode(input$userlocation)
dist <- distm(x = matrix(data = c(df.apt$lon, df.apt$lat), ncol = 2),
y = c(lon = address_latlon$lon, lat = address_latlon$lat),
fun = distVincentySphere)
dist <- dist/1000
})
apt_reactive <- reactive({df.apt[distance_reactive() < input$distancia,]})
output$mymap <- renderLeaflet({
map <- leaflet() %>%
addTiles() %>%
addProviderTiles("OpenStreetMap.BlackAndWhite") %>%
setView(lng = mean(df.apt$lon), lat = mean(df.apt$lat), zoom = 11) %>%
addLegend(pal = vetorCoresApt, values = df.apt$preco,
position = "bottomright",
layerId = "legend")
map
})
observe({
leafletProxy("mymap") %>%
clearMarkers() %>%
#addMarkers(lng = myadress()$lon, lat = myadress()$lat) %>%
addCircleMarkers(data = apt_reactive(),
lng = ~lon, lat = ~lat,
color = ~vetorCoresQuarto(preco),
opacity = 1.5,
# adicionar popup
popup = textoPopup(apt_reactive(), "apartamento"),
group = "Apartamentos")
})
apt_reactive <- reactive({
address_latlon <- geocode(input$userlocation)
dist <- distm(x = matrix(data = c(df.apt$lon, df.apt$lat), ncol = 2),
y = c(lon = address_latlon$lon, lat = address_latlon$lat),
fun = distVincentySphere)
apt.df[dist < input$distancia,]
})
addCircleMarkers(data = df.apt
addCircleMarkers(data = apt_reactive()