这是我第一次使用闪亮。我建立了一个数据库来提供现有的应用程序。我创建的新模块（通过修改 by_country 页面太复杂了）并且没有完全工作。
表格“tablepreped.xlsx”正在显示，下拉菜单也是如此，但是当我更改选择器时，表格的过滤不会更新。
基本上，我想选择一个指标，并在此基础上在我的表格中过滤国家名称、差距大小和差距值，理想情况下还能够选择差距大小。如果有人能指出我正确的方向，我将不胜感激！
server <- function(input, output, session) {
observeEvent(input$go_to_aimm_landing, {
hideElement("main_app")
showElement("landing_page")
})
observeEvent(landing_page_return$page_selected_trigger(), {
page_ <- landing_page_return$page_selected()
updateNavbarPage(
session,
"nav_pages",
selected = page_
)
landing_page_return <- callModule(
landing_page_module,
"landing_page_id"
)
by_country_return <- callModule(
by_country_module,
'by_country',
indicator_dat = dat,
rank_ranges = band_ranges,
defaults = landing_page_return$default_selected
)
callModule(
by_size_module,
'by_size'
)
callModule(
map_module,
'map_id',
default_selected = landing_page_return$default_selected
)
}
服务器
by_size_module_ui <- function(id) {
ns <- NS(id)
tabsetPanel(
tabPanel(
"Indicators",
fluidRow(
br(),
column(
width = 10,
fluidRow(
column(
width = 12,
tagList(
fluidRow(
column(
width = 2,
pickerInput(
ns("country"),
"Country",
choices = choices$country,
selected = "Tanzania",
options = list(
'live-search' = TRUE,
'live-search-placeholder' = 'Search...',
'live-search-normalize' = TRUE,
'live-search-style' = 'contains'
),
width = "100%"
)
),
column(
width = 4,
pickerInput(
ns("indicator_names"),
"Indicator",
choices = choices$indicator_names,
selected = "Adult account holders",
options = list(
'live-search' = TRUE,
'live-search-placeholder' = 'Search...',
'live-search-normalize' = TRUE,
'live-search-style' = 'contains'
),
width = "100%"
)
),
column(
width = 2,
pickerInput(
ns("indicator_type"),
"Type of Indicator",
choices = choices$indicator_type,
choicesOpt = list(
disabled = c("gap", "intensity") %in% c("intensity")
),
selected = "gap",
multiple = TRUE,
options = pickerOptions(
actionsBox = TRUE,
liveSearch = TRUE,
liveSearchPlaceholder = 'Search...',
liveSearchNormalize = TRUE,
liveSearchStyle = 'contains'
),
width = "100%"
)
),
column(
width = 2,
pickerInput(
ns("gap_size"),
"Gap Size",
choices = choices$gap_size,
selected = "l",
multiple = TRUE,
options = pickerOptions(
actionsBox = TRUE,
liveSearch = TRUE,
liveSearchPlaceholder = 'Search...',
liveSearchNormalize = TRUE,
liveSearchStyle = 'contains'
),
width = "100%"
)
)
)
)
),
column(
width = 12,
h2(
style = "margin-left: 5px; margin-top: 25px; white-space: nowrap;",
"Indicator: ",
textOutput(ns("sel_indicator_name"), inline = TRUE)
)
)
)
)
),
tagList(DTOutput(ns("by_size_table")) %>%
withSpinner(),
tags$script(src = "by_country_time_series.js"),
tags$script(paste0("by_country_module_js('", ns(''), "')"))
),
br(),
br()
)
)
}
by_size_module <- function(input, output, session, tablepreped, default_selected) {
ns <- session$ns
default_selected <- reactive({
list(
default_indicator_name = input$indicator_names,
default_country = input$country,
default_gap_size = input$gap_size,
default_indicator_type = input$indicator_type
)
})
### GAP Ranges table ----------
library('datasets')
library("openxlsx")
tablepreped <- read.xlsx("tablepreped.xlsx")
##Makes title reactive to indicator input
indoc <- reactive({
sel_indicator_name <- input$indicator_names
})
output$sel_indicator_name <- renderText({
indoc()
})
# filter data for the selected indicator and gap size
tablepreped_filtered <- reactive({
gsize<-input$gap_size
hold<-input$indicator_names
hold_gap_size <- hold %>%
dplyr::filter(gap_size == gsize)
ctry_indicator_values <- hold_gap_size %>%
select(country, gap_vals,gap_size)
})
# create data frame
out <- tablepreped
out<-out %>%
select(
country,
indicator_name,
gap_vals,
gap_size
)
output$by_size_table <- DT::renderDataTable({
DT::datatable(out,
rownames = FALSE,
class = "compact stripe",
options = list(orderClasses = TRUE)
)
})
}
这是数据帧头
structure(list(country = c("Afghanistan", "Afghanistan", "Afghanistan",
"Afghanistan", "Afghanistan", "Afghanistan"), code = c("gap_ind_0001",
"gap_ind_0002", "gap_ind_0003", "gap_ind_0005", "gap_ind_0006",
"gap_ind_0007"), indicator_name = c("Affordability ",
"Infrastructure", "Poverty Rate",
"Adjusted years of schooling", "Adult literacy",
"Account holders, female"), s = c(70, 60, 67, 8, 58.2,
55), m = c(60, 45, 50, 7, 39.5, 34.9), l = c(45, 30, 30, 5, 26,
21.5), gap_size = c("vl", "l", "m", "vl", "s", "vl"), gap_vals = c(29.58082,
23.3432, 18.330012, 4.87, 14.893313, 7.160684)), row.names = c(NA,
6L), class = "data.frame")