闪亮的应用程序速度非常慢，并且出现RegData错误，代码中的什么可能会导致这种情况？
我尝试在shiny上运行仪表板，结果速度非常慢，运行时也出现错误，所以我怀疑应用程序可能崩溃了？知道代码中的什么可能会导致这种情况吗 我刚开始编写线性回归的代码，我不确定代码的有效性 绘图不会显示在仪表板上 谢谢你闪亮的应用程序速度非常慢，并且出现RegData错误，代码中的什么可能会导致这种情况？,r,shiny,R,Shiny,我尝试在shiny上运行仪表板，结果速度非常慢，运行时也出现错误，所以我怀疑应用程序可能崩溃了？知道代码中的什么可能会导致这种情况吗 我刚开始编写线性回归的代码，我不确定代码的有效性 绘图不会显示在仪表板上 谢谢你 # Define UI ---- ui <- fluidPage( titlePanel("AirBnb NYC"), sidebarLayout( sidebarPanel(
# Define UI ----
ui <- fluidPage(
titlePanel("AirBnb NYC"),
sidebarLayout(
sidebarPanel(
fluidRow(
column(3,
selectInput("select", h3("Which Neighbourhood group ?"), choices =
c("Brooklyn","Manhattan","Queens","Staten Island", "Bronx"))),
column(3,
selectInput("select2", h3("which Neighbourhood ?"), choices = "")),
column(3,
selectInput("select1", h3("Room Type"), choices = ""))),
p("Select the inputs for the Dependent Variable"),
selectInput(inputId = "DepVar", label = "Dependent Variables", multiple = FALSE, choices =
colnames(AB_NYC_2019)),
p("Select the inputs for the Independent Variable"),
selectInput(inputId = "IndVar", label = "Independent Variables", multiple = FALSE,
choices = list( "price"))
),
mainPanel( leafletOutput("map",width = "100%",height = "800"),
fluidRow(column(width = 6, plotOutput("data")),
column(width = 6, plotOutput("plot"))),
verbatimTextOutput(outputId = "RegSum"),
verbatimTextOutput(outputId = "IndPrint"),
verbatimTextOutput(outputId = "DepPrint"))
))
AB_NYC_2019 <- AB_NYC_2019[1:50,]
# Define UI ----
ui <- fluidPage(
titlePanel("AirBnb NYC"),
sidebarLayout(
sidebarPanel(
selectInput("select", h3("Which Neighbourhood group ?"), choices =
c("Brooklyn","Manhattan","Queens","Staten Island", "Bronx")),
selectInput("select2", h3("which Neighbourhood ?"), choices = ""),
selectInput("select1", h3("Room Type"), choices = ""),
p("Select the inputs for the Dependent Variable"),
selectInput(inputId = "DepVar", label = "Dependent Variables", multiple = FALSE, choices =
colnames(AB_NYC_2019)),
p("Select the inputs for the Independent Variable"),
selectInput(inputId = "IndVar", label = "Independent Variables", multiple = FALSE,
choices = list( "price"))
),
mainPanel( leafletOutput("map",width = "100%",height = "800"),
fluidRow(column(width = 6, plotOutput("data")),
column(width = 6, plotOutput("plot"))),
verbatimTextOutput(outputId = "RegSum"),
verbatimTextOutput(outputId = "IndPrint"),
verbatimTextOutput(outputId = "DepPrint"))
)
)
server <- function(input, output, session) {
#Define parameters of search
observe({
req(input$select)
x <- AB_NYC_2019 %>% dplyr::filter(neighbourhood_group == input$select) %>% select(neighbourhood)
updateSelectInput(session, "select2", "select your neighbourhood", choice = unique(x))
})
observe({
req(input$select2)
productdata <- AB_NYC_2019$room_type[AB_NYC_2019$neighbourhood == input$select2]
updateSelectInput(session, "select1", "Which room type?", choices = unique(productdata))
})
#Create map
color <- colorFactor(palette = c("red", "green", "blue", "purple", "yellow"),
unique(AB_NYC_2019$neighbourhood_group))
filteredData <- reactive({
req(input$select)
filter(AB_NYC_2019, neighbourhood_group == input$select)})
output$map <- renderLeaflet({
req(filteredData())
map <- leaflet(filteredData()) %>% addProviderTiles(providers$Esri.WorldTopoMap) %>%
setView(lng = -73.98928, lat = 40.75042, zoom = 10) %>%
addCircleMarkers(
lng=~longitude, # Longitude coordinates
lat=~latitude, # Latitude coordinates
stroke=TRUE, # Circle stroke
weight = 0.1,
radius = 0.5,
fillOpacity=0.5,
color=~color(neighbourhood_group),
label = paste("Name:", filteredData()$name,
"\nPrice:", filteredData()$price,
"\nReviews:", filteredData()$number_of_reviews)
) %>%
addLegend("bottomright", pal = color, values = ~neighbourhood_group,
title = "Neighbourhood groups",
opacity = 1
)
})
#filter map
observe({
req(filteredData())
labs <- lapply(seq(nrow(filteredData())), function(i) {
paste0( '<p> Name: ', filteredData()[i, "name"], '<p></p>', 'Price: ',
filteredData()[i, "price"],'</p><p>', 'Reviews: ',
filteredData()[i, "number_of_reviews"], '</p>' )
})
leafletProxy("map", data = filteredData()) %>%
clearShapes() %>%
addMarkers(~longitude, ~latitude,
label = lapply(labs, htmltools::HTML),
labelOptions = labelOptions(textsize = "12px"))
})
lm1 <- reactive({
req(filteredData())
lm(reformulate(input$IndVar, input$DepVar), data = filteredData())})
output$DepPrint <- renderPrint({input$DepVar})
output$IndPrint <- renderPrint({input$IndVar})
output$RegSum <- renderPrint({
req(lm1())
summary(lm1())})
#Get many plots
output$data <- renderPlot({
ggplot(AB_NYC_2019, aes(price)) +
geom_histogram(bins = 30, aes(y = ..density..), fill = "purple") +
geom_density(alpha = 0.2, fill = "purple") +
theme_bw() +
ggtitle("Distribution of price",
subtitle = "The distribution is very skewed") +
theme(axis.title = element_text(), axis.title.x = element_text()) +
geom_vline(xintercept = round(mean(AB_NYC_2019$price), 2), size = 2, linetype = 3)
})
output$plot <- renderPlot({
AB_NYC_2019 %>% filter(price >= mean(price)) %>% group_by(neighbourhood_group, room_type) %>%
tally %>%
ggplot(aes(reorder(neighbourhood_group,desc(n)), n, fill = room_type)) +
theme_bw() +
xlab(NULL) +
ylab("Number of objects") +
ggtitle("Number of above average price objects",
subtitle = "Most of them are entire homes or apartments") +
geom_bar(stat = "identity")
})
}
shinyApp(ui, server)
AB_纽约大学2019%
clearShapes（）%>%
添加标记（~经度，~纬度，
label=lappy（实验室，htmltools:：HTML），
labelOptions=labelOptions（textsize=“12px”））
})
正在使用的绘图中的lm1th
。我不知道那是什么。也许你的意思是theme\u bw（）
。此外，最好在数据帧中仅使用50个观察值来运行代码，因为添加数千个标记太慢。一旦将RegData
替换为filteredData（）
，th
在绘图中使用theme\u bw（）
，并在必要时使用req（）
，绘图工作正常。要测试它，请使用数据帧中的前50行或前100行。如果有必要，我不确定req（）函数是什么？有关req（）
，请参阅此行现在不起作用：label=paste（“Name:，AB_NYC_2019$Name，
，“Price:，AB_NYC_2019$Price，
”你知道为什么吗？看起来地图过滤器不起作用了。您需要使用标签中filteredData（）
中的变量，而不是AB_NYC_2019
中的变量。请查看更新的代码。修复了标签显示在多行上的问题。非常感谢。我想做一个交互式回归，但它不像我做的那样有效
shinyApp(ui = ui, server = server)
AB_NYC_2019 <- AB_NYC_2019[1:50,]
# Define UI ----
ui <- fluidPage(
titlePanel("AirBnb NYC"),
sidebarLayout(
sidebarPanel(
selectInput("select", h3("Which Neighbourhood group ?"), choices =
c("Brooklyn","Manhattan","Queens","Staten Island", "Bronx")),
selectInput("select2", h3("which Neighbourhood ?"), choices = ""),
selectInput("select1", h3("Room Type"), choices = ""),
p("Select the inputs for the Dependent Variable"),
selectInput(inputId = "DepVar", label = "Dependent Variables", multiple = FALSE, choices =
colnames(AB_NYC_2019)),
p("Select the inputs for the Independent Variable"),
selectInput(inputId = "IndVar", label = "Independent Variables", multiple = FALSE,
choices = list( "price"))
),
mainPanel( leafletOutput("map",width = "100%",height = "800"),
fluidRow(column(width = 6, plotOutput("data")),
column(width = 6, plotOutput("plot"))),
verbatimTextOutput(outputId = "RegSum"),
verbatimTextOutput(outputId = "IndPrint"),
verbatimTextOutput(outputId = "DepPrint"))
)
)
server <- function(input, output, session) {
#Define parameters of search
observe({
req(input$select)
x <- AB_NYC_2019 %>% dplyr::filter(neighbourhood_group == input$select) %>% select(neighbourhood)
updateSelectInput(session, "select2", "select your neighbourhood", choice = unique(x))
})
observe({
req(input$select2)
productdata <- AB_NYC_2019$room_type[AB_NYC_2019$neighbourhood == input$select2]
updateSelectInput(session, "select1", "Which room type?", choices = unique(productdata))
})
#Create map
color <- colorFactor(palette = c("red", "green", "blue", "purple", "yellow"),
unique(AB_NYC_2019$neighbourhood_group))
filteredData <- reactive({
req(input$select)
filter(AB_NYC_2019, neighbourhood_group == input$select)})
output$map <- renderLeaflet({
req(filteredData())
map <- leaflet(filteredData()) %>% addProviderTiles(providers$Esri.WorldTopoMap) %>%
setView(lng = -73.98928, lat = 40.75042, zoom = 10) %>%
addCircleMarkers(
lng=~longitude, # Longitude coordinates
lat=~latitude, # Latitude coordinates
stroke=TRUE, # Circle stroke
weight = 0.1,
radius = 0.5,
fillOpacity=0.5,
color=~color(neighbourhood_group),
label = paste("Name:", filteredData()$name,
"\nPrice:", filteredData()$price,
"\nReviews:", filteredData()$number_of_reviews)
) %>%
addLegend("bottomright", pal = color, values = ~neighbourhood_group,
title = "Neighbourhood groups",
opacity = 1
)
})
#filter map
observe({
req(filteredData())
labs <- lapply(seq(nrow(filteredData())), function(i) {
paste0( '<p> Name: ', filteredData()[i, "name"], '<p></p>', 'Price: ',
filteredData()[i, "price"],'</p><p>', 'Reviews: ',
filteredData()[i, "number_of_reviews"], '</p>' )
})
leafletProxy("map", data = filteredData()) %>%
clearShapes() %>%
addMarkers(~longitude, ~latitude,
label = lapply(labs, htmltools::HTML),
labelOptions = labelOptions(textsize = "12px"))
})
lm1 <- reactive({
req(filteredData())
lm(reformulate(input$IndVar, input$DepVar), data = filteredData())})
output$DepPrint <- renderPrint({input$DepVar})
output$IndPrint <- renderPrint({input$IndVar})
output$RegSum <- renderPrint({
req(lm1())
summary(lm1())})
#Get many plots
output$data <- renderPlot({
ggplot(AB_NYC_2019, aes(price)) +
geom_histogram(bins = 30, aes(y = ..density..), fill = "purple") +
geom_density(alpha = 0.2, fill = "purple") +
theme_bw() +
ggtitle("Distribution of price",
subtitle = "The distribution is very skewed") +
theme(axis.title = element_text(), axis.title.x = element_text()) +
geom_vline(xintercept = round(mean(AB_NYC_2019$price), 2), size = 2, linetype = 3)
})
output$plot <- renderPlot({
AB_NYC_2019 %>% filter(price >= mean(price)) %>% group_by(neighbourhood_group, room_type) %>%
tally %>%
ggplot(aes(reorder(neighbourhood_group,desc(n)), n, fill = room_type)) +
theme_bw() +
xlab(NULL) +
ylab("Number of objects") +
ggtitle("Number of above average price objects",
subtitle = "Most of them are entire homes or apartments") +
geom_bar(stat = "identity")
})
}
shinyApp(ui, server)