我正在开发一个闪亮的应用程序，可以让您知道适合居住的地方，在这里是正在运行的应用程序：
到目前为止，我很喜欢它，但我真的更喜欢一个能够缩放的leaflet地图，而不是静态地图，但是到目前为止，我一直在使用leaflet软件包时遇到了许多渲染或更新栅格的问题。如果您需要文件，则github存储库为存储库
这是当前应用程序的代码服务器：
library(shiny)
library(raster)
library(rworldmap)
library(rgdal)
library(dplyr)
data("countriesCoarse")
uno <- readRDS("uno.rds")
World <- getData('worldclim', var='bio', res=10)
cities <- readRDS("cities.rds")
shinyServer(function(input, output) {
output$distPlot <- renderPlot({
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
plot(uno, col ="red", legend = FALSE)
plot(countriesCoarse, add = TRUE)
})
output$downloadPlot <- downloadHandler(
filename = function() { paste("WhereToLive", '.png', sep='') },
content = function(file) {
png(file)
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
plot(uno, col ="red", legend = FALSE)
plot(countriesCoarse, add = TRUE)
dev.off()
})
output$visFun <- renderDataTable({
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
cities$exists <- extract(uno, cities[,2:3])
cities <- filter(cities, exists == 1)
cities <- cities[,c(1,4,5,6)]
cities <- filter(cities, pop > min(as.numeric(as.character(input$Population))))
cities <- filter(cities, pop < max(as.numeric(as.character(input$Population))))
cities
})
output$downloadData <- downloadHandler(
filename = function() { paste("cities", '.csv', sep='') },
content = function(file) {
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
cities$exists <- extract(uno, cities[,2:3])
cities <- filter(cities, exists == 1)
cities <- filter(cities$pop > min(input$Population))
cities <- filter(cities$pop < max(input$Population))
cities <- cities[,c(1,4,5,6)]
write.csv(cities, file)
}
)
})
UI:
library(shiny)
library(raster)
library(rworldmap)
library(rgdal)
data("countriesCoarse")
shinyUI(fluidPage(
titlePanel("Where should you live according to your climate preferences?"),
sidebarLayout(
sidebarPanel(
h3("Select your climate preferences"),
p("Using worldclim database, and knowing your climate prefeneces, you can now using this tool get an idea of where in the world you should live."),
p("Just use the sliders to anwer the simple questions we ask and you will get a map together with a downloadable table of where the climate suits you."),
selectInput(inputId = "degrees", label = "Temp units:", choices =
c("Celcius"= "Celcius",
"Fahrenheit" = "Fahrenheit")),
submitButton("Update View", icon("refresh")),
conditionalPanel(condition = "input.degrees == 'Celcius'",
sliderInput(inputId = "MaxTempC",
label = "What's the average maximum temperature you want to endure during the summer?",
min = 0,
max = 50,
value = 30),
sliderInput(inputId = "MinTempC",
label = "What's the average minimum temperature you want to endure during the winter?",
min = -40,
max = 60,
value = 0),
sliderInput(inputId = "RangeTempC",
label = "What's your prefered temperature range?",
min = -10,
max = 30,
value = c(0, 20)),
sliderInput(inputId = "RangePPC",
label = "What's your prefered precipitation range?",
min = 0,
max = 5000,
value = c(0, 5000))),
conditionalPanel(condition = "input.degrees == 'Fahrenheit'",
sliderInput(inputId = "MaxTempF",
label = "What's the average maximum temperature you want to endure during the summer?",
min = 0,
max = 120,
value = 90),
sliderInput(inputId = "MinTempF",
label = "What's the average minimum temperature you want to endure during the winter?",
min = -40,
max = 60,
value = 32),
sliderInput(inputId = "RangeTempF",
label = "What's your prefered temperature range?",
min = -40,
max = 90,
value = c(32, 70)),
sliderInput(inputId = "RangePPF",
label = "What's your prefered precipitation range?",
min = 0,
max = 200,
value = c(0, 200))),
sliderInput(inputId = "Population",
label = "how big of a town do you want to live in (Population)?",
min = 0,
max = 20000000,
value = c(0, 20000000, by = 1)))
,
# Show a plot of the generated distribution
mainPanel(
plotOutput("distPlot"),
downloadButton('downloadPlot', 'Download Plot'),
dataTableOutput("visFun"),
downloadButton('downloadData', 'Download Table')
)
)
))
到目前为止还不错，但是当我尝试将其更改为使用leaflet地图时，它并没有很好地工作，事实上leaflet地图并没有出现，我已经尝试了这个方法：
服务器：
library(shiny)
library(raster)
library(rworldmap)
library(rgdal)
library(dplyr)
library(leaflet)
library(sp)
data("countriesCoarse")
uno <- readRDS("uno.rds")
World <- getData('worldclim', var='bio', res=10)
cities <- readRDS("cities.rds")
shinyServer(function(input, output) {
output$map <- renderLeaflet({
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
l <- leaflet() %>% setView(0, 0, zoom = 1)
l <- l %>% addRasterImage(uno)
l
})
output$downloadPlot <- downloadHandler(
filename = function() { paste("WhereToLive", '.png', sep='') },
content = function(file) {
png(file)
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
plot(uno, col ="red", legend = FALSE)
plot(countriesCoarse, add = TRUE)
dev.off()
})
output$visFun <- renderDataTable({
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
cities$exists <- extract(uno, cities[,2:3])
cities <- filter(cities, exists == 1)
cities <- cities[,c(1,4,5,6)]
cities <- filter(cities, pop > min(as.numeric(as.character(input$Population))))
cities <- filter(cities, pop < max(as.numeric(as.character(input$Population))))
cities
})
output$downloadData <- downloadHandler(
filename = function() { paste("cities", '.csv', sep='') },
content = function(file) {
uno[World[[10]] > ifelse(input$degrees == "Celcius", (input$MaxTempC*10), (((input$MaxTempF-32)*5/9)*10))] <- NA
uno[World[[11]] < ifelse(input$degrees == "Celcius", (input$MinTempC*10), (((input$MinTempF-32)*5/9)*10))] <- NA
uno[World[[1]] < ifelse(input$degrees == "Celcius", min(input$RangeTempC*10), min(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[1]] > ifelse(input$degrees == "Celcius", max(input$RangeTempC*10), max(((input$RangeTempF-32)*5/9)*10))] <- NA
uno[World[[12]] < ifelse(input$degrees == "Celcius", min(input$RangePPC), min(input$RangePPF*25.4))] <- NA
uno[World[[12]] > ifelse(input$degrees == "Celcius", max(input$RangePPC), max(input$RangePPF*25.4))] <- NA
cities$exists <- extract(uno, cities[,2:3])
cities <- filter(cities, exists == 1)
cities <- filter(cities$pop > min(input$Population))
cities <- filter(cities$pop < max(input$Population))
cities <- cities[,c(1,4,5,6)]
write.csv(cities, file)
}
)
})
and
UI:
library(shiny)
library(raster)
library(rworldmap)
library(rgdal)
library(leaflet)
data("countriesCoarse")
shinyUI(fluidPage(
titlePanel("Where should you live according to your climate preferences?"),
sidebarLayout(
sidebarPanel(
h3("Select your climate preferences"),
p("Using worldclim database, and knowing your climate prefeneces, you can now using this tool get an idea of where in the world you should live."),
p("Just use the sliders to anwer the simple questions we ask and you will get a map together with a downloadable table of where the climate suits you."),
selectInput(inputId = "degrees", label = "Temp units:", choices =
c("Celcius"= "Celcius",
"Fahrenheit" = "Fahrenheit")),
submitButton("Update View", icon("refresh")),
conditionalPanel(condition = "input.degrees == 'Celcius'",
sliderInput(inputId = "MaxTempC",
label = "What's the average maximum temperature you want to endure during the summer?",
min = 0,
max = 50,
value = 30),
sliderInput(inputId = "MinTempC",
label = "What's the average minimum temperature you want to endure during the winter?",
min = -40,
max = 60,
value = 0),
sliderInput(inputId = "RangeTempC",
label = "What's your prefered temperature range?",
min = -10,
max = 30,
value = c(0, 20)),
sliderInput(inputId = "RangePPC",
label = "What's your prefered precipitation range?",
min = 0,
max = 5000,
value = c(0, 5000))),
conditionalPanel(condition = "input.degrees == 'Fahrenheit'",
sliderInput(inputId = "MaxTempF",
label = "What's the average maximum temperature you want to endure during the summer?",
min = 0,
max = 120,
value = 90),
sliderInput(inputId = "MinTempF",
label = "What's the average minimum temperature you want to endure during the winter?",
min = -40,
max = 60,
value = 32),
sliderInput(inputId = "RangeTempF",
label = "What's your prefered temperature range?",
min = -40,
max = 90,
value = c(32, 70)),
sliderInput(inputId = "RangePPF",
label = "What's your prefered precipitation range?",
min = 0,
max = 200,
value = c(0, 200))),
sliderInput(inputId = "Population",
label = "how big of a town do you want to live in (Population)?",
min = 0,
max = 20000000,
value = c(0, 20000000, by = 1)))
,
# Show a plot of the generated distribution
mainPanel(
leafletOutput("map", width = "100%", height = "100%"),
downloadButton('downloadPlot', 'Download Plot'),
dataTableOutput("visFun"),
downloadButton('downloadData', 'Download Table')
)
)
))
这段代码没有报错，但地图没有渲染出来，并且出现了以下警告信息。
Listening on http://127.0.0.1:7231
Warning in rgdal::rawTransform(projfrom, projto, nrow(xy), xy[, 1], xy[, :
54 projected point(s) not finite
Warning in rgdal::rawTransform(projfrom, projto, nrow(xy), xy[, 1], xy[, :
54 projected point(s) not finite
Warning in rgdal::rawTransform(projfrom, projto, nrow(xy), xy[, 1], xy[, :
54 projected point(s) not finite