带有ggplot的闪亮R动态热图.规模和速度问题 [英] Shiny R dynamic heatmap with ggplot. Scale and speed issues
问题描述
我正在尝试使用一些公共信息来为某些劳工统计数据绘制加拿大的热图.使用来自加拿大统计局的数据(这些是无需深入研究的大型zip文件).以下是一个工作示例，说明了我在区域之间的相对变化很小的情况下遇到的两个问题(尽管周期之间可能存在很大的绝对变化以及绘制时间很慢.要使其正常工作，您需要下载.zip普查链接中的文件，然后将文件解压缩到数据文件夹中.
library(shiny)
library(maptools)
library(ggplot2)
require(reshape2)
library(tidyr)
library(maptools)
library(ggplot2)
library(RColorBrewer)
ui <- fluidPage(
titlePanel("heatmap"),
# Sidebar with a slider input for year of interest
sidebarLayout(
sidebarPanel(
sliderInput("year",h3("Select year or push play button"),
min = 2000, max = 2002, step = 1, value = 2000,
animate = TRUE)
),
# Output of the map
mainPanel(
plotOutput("unemployment")
)
)
)
server <- function(input, output) {
#to get the spacial data: from file in link above
provinces<-maptools::readShapeSpatial("data/gpr_000a11a_e.shp")
data.p<- ggplot2::fortify(provinces, region = "PRUID")
data.p<-data.p[which(data.p$id<60),]
#dataframe with same structure as statscan csv after processing
unem <- runif(10,min=0,max=100)
unem1 <- unem+runif(1,-10,10)
unem2 <- unem1+runif(1,-10,10)
unemployment <- c(unem,unem1,unem2)
#dataframe with same structure as statscan csv after processing
X <- data.frame("id" = c(10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59),
"Unemployment" = unemployment,
"year" = c(rep(2000,10),rep(2001,10),rep(2002,10))
)
plot.data<- reactive({
a<- X[which(X$year == input$year),]
return(merge(data.p,a,by = "id"))
})
output$unemployment <- renderPlot({
ggplot(plot.data(),
aes(x = long, y = lat,
group = group , fill =Unemployment)) +
geom_polygon() +
coord_equal()
})
}
# Run the application
shinyApp(ui = ui, server = server)
对于任何一个问题的任何帮助，将不胜感激
对于这种类型的动画，使用传单而不是ggplot更快，因为传单仅允许您重新渲染多边形，而不是整个地图./p>
我使用另外两个技巧来加快动画的播放速度:
-
我将数据加入反应式之外.在反应堆中，它只是一个简单的子集.请注意，联接可以在应用程序外部完成，并作为预处理的.rds文件读取.
-
我使用rmapshaper软件包简化了多边形，以减少传单的绘制时间.同样，可以在应用程序外部执行此操作以减少启动时的加载时间.
如果您使用圆圈(即每个省的质心)而不是多边形，则动画可能更加无缝.圈子的大小可能随失业值而变化.
请注意，此方法需要使用leaflet，sf，dplyr和rmapshaper软件包.
library(shiny)
library(dplyr)
library(leaflet)
library(sf)
library(rmapshaper)
ui <- fluidPage(
titlePanel("heatmap"),
# Sidebar with a slider input for year of interest
sidebarLayout(
sidebarPanel(
sliderInput("year",h3("Select year or push play button"),
min = 2000, max = 2002, step = 1, value = 2000,
animate = TRUE)
),
# Output of the map
mainPanel(
leafletOutput("unemployment")
)
)
)
server <- function(input, output) {
#to get the spacial data: from file in link above
data.p <- sf::st_read("input/gpr_000a11a_e.shp") %>%
st_transform(4326) %>%
rmapshaper::ms_simplify()
data.p$PRUID <- as.character(data.p$PRUID) %>% as.numeric
data.p <- data.p[which(data.p$PRUID < 60),]
lng.center <- -99
lat.center <- 60
zoom.def <- 3
#dataframe with same structure as statscan csv after processing
unem <- runif(10,min=0,max=100)
unem1 <- unem+runif(1,-10,10)
unem2 <- unem1+runif(1,-10,10)
unemployment <- c(unem,unem1,unem2)
#dataframe with same structure as statscan csv after processing
X <- data.frame("id" = c(10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59),
"Unemployment" = unemployment,
"year" = c(rep(2000,10),rep(2001,10),rep(2002,10))
)
data <- left_join(data.p, X, by = c("PRUID"= "id"))
output$unemployment <- renderLeaflet({
leaflet(data = data.p) %>%
addProviderTiles("OpenStreetMap.Mapnik", options = providerTileOptions(opacity = 1), group = "Open Street Map") %>%
setView(lng = lng.center, lat = lat.center, zoom = zoom.def) %>%
addPolygons(group = 'base',
fillColor = 'transparent',
color = 'black',
weight = 1.5) %>%
addLegend(pal = pal(), values = X$Unemployment, opacity = 0.7, title = NULL,
position = "topright")
})
get_data <- reactive({
data[which(data$year == input$year),]
})
pal <- reactive({
colorNumeric("viridis", domain = X$Unemployment)
})
observe({
data <- get_data()
leafletProxy('unemployment', data = data) %>%
clearGroup('polygons') %>%
addPolygons(group = 'polygons',
fillColor = ~pal()(Unemployment),
fillOpacity = 0.9,
color = 'black',
weight = 1.5)
})
}
# Run the application
shinyApp(ui = ui, server = server)
I am attempting to use some public information to produce a heat-map of Canada for some labor statistics. Using the spacial files from the census, and data from Statistics Canada (these are large zip files that are not necessary to dig into). Below is a working example that illustrates both the problems I am having with little relative change between regions( though there may be a big absolute change between periods, and the slow draw time.To get this to work, you need to download the .zip file from the census link and unzip the files to a data folder.
library(shiny)
library(maptools)
library(ggplot2)
require(reshape2)
library(tidyr)
library(maptools)
library(ggplot2)
library(RColorBrewer)
ui <- fluidPage(
titlePanel("heatmap"),
# Sidebar with a slider input for year of interest
sidebarLayout(
sidebarPanel(
sliderInput("year",h3("Select year or push play button"),
min = 2000, max = 2002, step = 1, value = 2000,
animate = TRUE)
),
# Output of the map
mainPanel(
plotOutput("unemployment")
)
)
)
server <- function(input, output) {
#to get the spacial data: from file in link above
provinces<-maptools::readShapeSpatial("data/gpr_000a11a_e.shp")
data.p<- ggplot2::fortify(provinces, region = "PRUID")
data.p<-data.p[which(data.p$id<60),]
#dataframe with same structure as statscan csv after processing
unem <- runif(10,min=0,max=100)
unem1 <- unem+runif(1,-10,10)
unem2 <- unem1+runif(1,-10,10)
unemployment <- c(unem,unem1,unem2)
#dataframe with same structure as statscan csv after processing
X <- data.frame("id" = c(10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59),
"Unemployment" = unemployment,
"year" = c(rep(2000,10),rep(2001,10),rep(2002,10))
)
plot.data<- reactive({
a<- X[which(X$year == input$year),]
return(merge(data.p,a,by = "id"))
})
output$unemployment <- renderPlot({
ggplot(plot.data(),
aes(x = long, y = lat,
group = group , fill =Unemployment)) +
geom_polygon() +
coord_equal()
})
}
# Run the application
shinyApp(ui = ui, server = server)
Any help with either of the issues would be greatly appreciated
For this type of animation it is much faster to use leaflet instead of ggplot as leaflet allows you to only re-render the polygons, not the entire map.
I use two other tricks to speed up the animation:
I join the data outside of the reactive. Within the reactive it is just a simple subset. Note, the join could be done outside of the app and read in as a pre-processed .rds file.
I simplify the polygons with the rmapshaper package to reduce drawing time by leaflet. Again, this could be done outside the app to reduce loading time at the start.
The animation could likely be even more seamless if you use circles (i.e. centroid of each province) instead of polygons. Circle size could vary with Unemployment value.
Note, you need the leaflet, sf, dplyr and rmapshaper packages for this approach.
library(shiny)
library(dplyr)
library(leaflet)
library(sf)
library(rmapshaper)
ui <- fluidPage(
titlePanel("heatmap"),
# Sidebar with a slider input for year of interest
sidebarLayout(
sidebarPanel(
sliderInput("year",h3("Select year or push play button"),
min = 2000, max = 2002, step = 1, value = 2000,
animate = TRUE)
),
# Output of the map
mainPanel(
leafletOutput("unemployment")
)
)
)
server <- function(input, output) {
#to get the spacial data: from file in link above
data.p <- sf::st_read("input/gpr_000a11a_e.shp") %>%
st_transform(4326) %>%
rmapshaper::ms_simplify()
data.p$PRUID <- as.character(data.p$PRUID) %>% as.numeric
data.p <- data.p[which(data.p$PRUID < 60),]
lng.center <- -99
lat.center <- 60
zoom.def <- 3
#dataframe with same structure as statscan csv after processing
unem <- runif(10,min=0,max=100)
unem1 <- unem+runif(1,-10,10)
unem2 <- unem1+runif(1,-10,10)
unemployment <- c(unem,unem1,unem2)
#dataframe with same structure as statscan csv after processing
X <- data.frame("id" = c(10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59,
10,11,12,13,24,35,46,47,48,59),
"Unemployment" = unemployment,
"year" = c(rep(2000,10),rep(2001,10),rep(2002,10))
)
data <- left_join(data.p, X, by = c("PRUID"= "id"))
output$unemployment <- renderLeaflet({
leaflet(data = data.p) %>%
addProviderTiles("OpenStreetMap.Mapnik", options = providerTileOptions(opacity = 1), group = "Open Street Map") %>%
setView(lng = lng.center, lat = lat.center, zoom = zoom.def) %>%
addPolygons(group = 'base',
fillColor = 'transparent',
color = 'black',
weight = 1.5) %>%
addLegend(pal = pal(), values = X$Unemployment, opacity = 0.7, title = NULL,
position = "topright")
})
get_data <- reactive({
data[which(data$year == input$year),]
})
pal <- reactive({
colorNumeric("viridis", domain = X$Unemployment)
})
observe({
data <- get_data()
leafletProxy('unemployment', data = data) %>%
clearGroup('polygons') %>%
addPolygons(group = 'polygons',
fillColor = ~pal()(Unemployment),
fillOpacity = 0.9,
color = 'black',
weight = 1.5)
})
}
# Run the application
shinyApp(ui = ui, server = server)
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