我目前正在使用 golem 框架构建一个闪亮的应用程序并尝试使用模块，但是我在连接模块方面遇到了困难。我当前的配对应用程序由 3 个基本模块组成：1) 确定用户的数据选择，2) 绘制图形，3) 全局 UI。我的问题是如何构建模块，以便当用户在模块 1 中选择输入时，数据会根据这些选择进行过滤，然后可用于在下一个模块中进行绘图。然后，绘图模块使用 if/else 语句根据数据选择模块中使用的 inputselect 确定应显示哪个绘图。由于某种原因，我在侧面板中选择了输入，而不是在绘图模块中，所以我想将它们分开。任何帮助，即使是如何工作的基本结构，都会很棒！
下面是我尝试过的代码，错误在于我跨模块通信的方式。但是，我只是不确定如何解决它。
library(tidyverse)
library(shiny)
library(shinyWidgets)
library(shinydashboard)
data<- expand.grid(
year = 1993:2005,
species = c("Chinook", "Steelhead"),
rear_type = c("Hatchery-origin", "Natural-origin"),
doy = 90:180)
data<-data %>%
mutate(SAR = rnorm(n(), mean = .05, sd = .01))
mod_dataselect_ui <- function(id){
ns <- NS(id)
tagList(
#select species
selectInput(inputId = ns("select_spp"),
label = "Select species",
choices = c("Chinook", "Steelhead"),#data.pred$species
selected = unique(data$species),
width = "200px",
multiple = T),
#select rear type
selectInput(inputId = ns("select_rear"),
label = "Select rearing type",
choices = c("Natural-origin", "Hatchery-origin"),
selected = unique(data$rear_type),
width = "200px",
multiple = T),
# prompt to select all years or by year
selectInput(
inputId = ns("year_display"),
label = "View by",
choices = c("All Years", "Year"),
selected = "All Years"
),
uiOutput(ns("year_picker"))
)
}
mod_dataselect_server <- function(id){
moduleServer( id, function(input, output, session){
ns <- session$ns
# Render the UI for the year picker
output$year_picker <- renderUI({
ns <- session$ns
if (input$year_display == "Year") {
pickerInput(
inputId = ns("select_years"),
label = "Select Year(s)",
choices = unique(data$year),
selected = 2000,
options = list(`actions-box` = TRUE),
multiple = TRUE
)
} else {
NULL
}
})
# Reactive for year_display
year_display <- reactive({
input$year_display
})
filtered_data <- reactive({
if (input$year_display == "All Years") {
data %>%
filter(
species %in% c(input$select_spp),
rear_type %in% c(input$select_rear)
)
} else if (input$year_display == "Year" && !is.null(input$select_years)) {
data %>%
filter(
species %in% c(input$select_spp),
rear_type %in% c(input$select_rear),
year %in% c(input$select_years)
)
} else {
NULL
}
})
})
}
mod_SAR_plot_ui <- function(id){
ns <- NS(id)
tagList(
plotly::plotlyOutput(outputId = ns("SAR_plot"))
)
}
#' SAR_plot Server Functions
#'
#' @noRd
mod_SAR_plot_server <- function(id, data){
moduleServer(id, function(input, output, session){
ns <- session$ns
# Retrieve reactive values from mod_dataselect_server
dataselect_reactives <- mod_dataselect_server("dataselect_1")
filtered_data <- dataselect_reactives$filtered_data
year_display <- dataselect_reactives$year_display
output$SAR_plot <- plotly::renderPlotly({
# Filter data based on user selection
if (dataselect_reactives$year_display == "All Years") {
ggplotly(ggplot(dataselect_reactives$filtered_data, aes(x=doy, y=SAR))+
geom_point() + facet_grid(rear_type ~ species))
} else if (dataselect_reactives$year_display == "Year") {
ggplotly(dataselect_reactives$filtered_data, aes(x=doy, y=SAR))+
geom_point() + facet_grid(rear_type ~ year + species))
}
})
})
}
app_ui <- function(request) {
tagList(
fluidPage(
shinydashboard::dashboardPage(
## Sidebar content - used as a navigation menu to each tab
sidebar = shinydashboard::dashboardSidebar(
shinydashboard::sidebarMenu(
# Setting id makes input$tabs give the tabName of currently-selected tab
id = "tabs",
shinydashboard::menuItem("Plots", tabName = "figs", icon = icon("chart-line")),
div(id = "tabs_filter",
conditionalPanel(condition = "input.tabs == 'figs'", mod_dataselect_ui("dataselect_1"))
)
)
),
body = shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "figs",mod_HydroSurv_ui("HydroSurv_ui_1"))
)
)
)
)
)
}
app_server <- function(input, output, session) {
filtered_data <- reactive(mod_dataselect_server("dataselect_1"))
mod_SAR_plot_server("SAR_plot_1", filtered_data())
mod_SAR_table_server("SAR_table_1", filtered_data())
}
您需要从第一个模块传递列表中的反应。然后将其作为反应对象传递到绘图模块。然后使用绘图模块内的
()library(tidyverse)
library(shiny)
library(shinyWidgets)
library(shinydashboard)
data<- expand.grid(
year = 1993:2005,
species = c("Chinook", "Steelhead"),
rear_type = c("Hatchery-origin", "Natural-origin"),
doy = 90:180)
data<-data %>%
dplyr::mutate(SAR = rnorm(n(), mean = .05, sd = .01))
mod_dataselect_ui <- function(id){
ns <- NS(id)
tagList(
#select species
selectInput(inputId = ns("select_spp"),
label = "Select species",
choices = c("Chinook", "Steelhead"),#data.pred$species
selected = unique(data$species),
width = "200px",
multiple = T),
#select rear type
selectInput(inputId = ns("select_rear"),
label = "Select rearing type",
choices = c("Natural-origin", "Hatchery-origin"),
selected = unique(data$rear_type),
width = "200px",
multiple = T),
# prompt to select all years or by year
selectInput(
inputId = ns("year_display"),
label = "View by",
choices = c("All Years", "Year"),
selected = "All Years"
),
uiOutput(ns("year_picker"))
)
}
mod_dataselect_server <- function(id){
moduleServer( id, function(input, output, session){
ns <- session$ns
# Render the UI for the year picker
output$year_picker <- renderUI({
req(input$year_display)
if (input$year_display == "Year") {
pickerInput(
inputId = ns("select_years"),
label = "Select Year(s)",
choices = unique(data$year),
selected = 2000,
options = list(`actions-box` = TRUE),
multiple = TRUE
)
} else {
NULL
}
})
# Reactive for year_display
yr <- reactive({
input$year_display
})
filtereddata <- reactive({
if (input$year_display == "All Years") {
df <- data %>%
dplyr::filter(
species %in% c(input$select_spp),
rear_type %in% c(input$select_rear)
)
} else if (input$year_display == "Year") {
df <- data %>%
dplyr::filter(
species %in% c(input$select_spp),
rear_type %in% c(input$select_rear)
)
if (!is.null(input$select_years)) df <- df %>% dplyr::filter(year %in% c(input$select_years))
} else {
df <- NULL
}
df
})
return(list(filtered_data = filtereddata, year_display= yr))
})
}
mod_SAR_plot_ui <- function(id){
ns <- NS(id)
tagList(
plotly::plotlyOutput(outputId = ns("SAR_plot"))
)
}
#' SAR_plot Server Functions
#'
#' @noRd
mod_SAR_plot_server <- function(id, data){
moduleServer(id, function(input, output, session){
ns <- session$ns
# Retrieve reactive values from mod_dataselect_server
# dataselect_reactives <- mod_dataselect_server("dataselect_1")
# filtered_data <- dataselect_reactives$filtered_data
# year_display <- dataselect_reactives$year_display
output$SAR_plot <- plotly::renderPlotly({
dataselect_reactives <- data
filtered_data <- dataselect_reactives$filtered_data()
year_display <- dataselect_reactives$year_display()
p <- NULL
# Filter data based on user selection
if (year_display == "All Years") {
p <- ggplotly(ggplot(filtered_data, aes(x=doy, y=SAR))+
geom_point() + facet_grid(rear_type ~ species))
} else if (year_display == "Year") {
p <- ggplotly(ggplot(filtered_data, aes(x=doy, y=SAR))+
geom_point() + facet_grid(rear_type ~ year + species))
}
p
})
})
}
app_ui <- function(request) {
tagList(
fluidPage(
shinydashboard::dashboardPage(
dashboardHeader(),
## Sidebar content - used as a navigation menu to each tab
sidebar = shinydashboard::dashboardSidebar(
shinydashboard::sidebarMenu(
# Setting id makes input$tabs give the tabName of currently-selected tab
id = "tabs",
shinydashboard::menuItem("Home", tabName = "home", icon = icon("home")),
shinydashboard::menuItem("Plots", tabName = "figs1", icon = icon("chart-line"),
mod_dataselect_ui("dataselect_1"),
shinydashboard::menuItem("Display Plots", tabName = "figs", icon = icon("chart-line")) ### need to select this tab to display plots
) #,
# div(id = "tabs_filter",
# conditionalPanel(condition = "input.tabs == 'figs'", mod_dataselect_ui("dataselect_1"))
# )
)
),
body = shinydashboard::dashboardBody(
shinydashboard::tabItems(
shinydashboard::tabItem(tabName = "figs",
# mod_HydroSurv_ui("HydroSurv_ui_1"),
mod_SAR_plot_ui("SAR_plot_1") #, mod_SAR_table_ui()
)
)
)
)
)
)
}
app_server <- function(input, output, session) {
filtered_data <- mod_dataselect_server("dataselect_1")
mod_SAR_plot_server("SAR_plot_1", filtered_data) ### filtered_data is reactive
# mod_SAR_table_server("SAR_table_1", filtered_data)
}
shinyApp(ui=app_ui, server=app_server)