###############################
# Single mean - ui
###############################

# alternative hypothesis options
sm_alt <- c("Two sided" = "two.sided", "Less than" = "less", "Greater than" = "greater")
sm_plots <- c("Histogram" = "hist", "Simulate" = "simulate")

# list of function arguments
sm_args <- as.list(formals(single_mean))

# list of function inputs selected by user
sm_inputs <- reactive({
  # loop needed because reactive values don't allow single bracket indexing
  sm_args$data_filter <- if (input$show_filter) input$data_filter else ""
  sm_args$dataset <- input$dataset
  for (i in r_drop(names(sm_args)))
    sm_args[[i]] <- input[[paste0("sm_",i)]]
  sm_args
})

output$ui_sm_var <- renderUI({
  isNum <- "numeric" == .getclass() | "integer" == .getclass()
  vars <- c("None", varnames()[isNum])
  selectInput(inputId = "sm_var", label = "Variable (select one):",
    choices = vars, selected = state_single("sm_var",vars), multiple = FALSE)
})

output$ui_single_mean <- renderUI({
  req(input$dataset)
  tagList(
    conditionalPanel(condition = "input.tabs_single_mean == 'Plot'",
      wellPanel(
        selectizeInput(inputId = "sm_plots", label = "Select plots:",
          choices = sm_plots,
          selected = state_init("sm_plots", "hist"),
          multiple = TRUE,
          options = list(plugins = list('remove_button', 'drag_drop')))
      )
    ),
    wellPanel(
 	   	uiOutput("ui_sm_var"),
  	  selectInput(inputId = "sm_alternative", label = "Alternative hypothesis:",
  	  	choices = sm_alt,
        selected = state_init("sm_alternative", sm_args$alternative),
  	  	multiple = FALSE),
    	sliderInput('sm_conf_lev',"Confidence level:", min = 0.85, max = 0.99,
    		value = state_init('sm_conf_lev',sm_args$conf_lev), step = 0.01),
    	numericInput("sm_comp_value", "Comparison value:",
    	  state_init('sm_comp_value',sm_args$comp_value))
    ),
  	help_and_report(modal_title = 'Single mean', fun_name = 'single_mean',
                    help_file = inclMD(file.path(getOption("radiant.path.basics"),"app/tools/help/single_mean.md")))
 	)
})

sm_plot <- reactive({
  list(plot_width = 650, plot_height = 400 * length(input$sm_plots))
})

sm_plot_width <- function()
  sm_plot() %>% { if (is.list(.)) .$plot_width else 650 }

sm_plot_height <- function()
  sm_plot() %>% { if (is.list(.)) .$plot_height else 400 }

# output is called from the main radiant ui.R
output$single_mean <- renderUI({

		register_print_output("summary_single_mean", ".summary_single_mean")
		register_plot_output("plot_single_mean", ".plot_single_mean",
                         height_fun = "sm_plot_height")

		# two separate tabs
		sm_output_panels <- tabsetPanel(
	    id = "tabs_single_mean",
	    tabPanel("Summary", verbatimTextOutput("summary_single_mean")),
	    tabPanel("Plot", plot_downloader("single_mean", height = sm_plot_height()),
               plotOutput("plot_single_mean", height = "100%"))
	  )

		# one output with components stacked
		# sm_output_panels <- tagList(
	  #    tabPanel("Summary", verbatimTextOutput("summary_single_mean")),
	  #    tabPanel("Plot", plotOutput("plot_single_mean", height = "100%"))
	  # )

		stat_tab_panel(menu = "Basics > Means",
		              tool = "Single mean",
		              tool_ui = "ui_single_mean",
		             	output_panels = sm_output_panels)

		# add "data = NULL" if the app doesn't doesn't use data
})

sm_available <- reactive({
  if (not_available(input$sm_var))
    return("This analysis requires a variable of type numeric or interval. If none are\navailable please select another dataset.\n\n" %>% suggest_data("demand_uk"))

  if (is.na(input$sm_comp_value))
    return("Please choose a comparison value")

  "available"
})

.single_mean <- reactive({
	do.call(single_mean, sm_inputs())
})

.summary_single_mean <- reactive({
  if (sm_available() != "available") return(sm_available())
  summary(.single_mean())
})

.plot_single_mean <- reactive({
  if (sm_available() != "available") return(sm_available())
  plot(.single_mean(), plots = input$sm_plots, shiny = TRUE)
})

observeEvent(input$single_mean_report, {
  if (input$sm_var == "None") return(invisible())
  if (length(input$sm_plots) == 0) {
    figs <- FALSE
    outputs <- c("summary")
    inp_out <- list("","")
  } else {
    outputs <- c("summary","plot")
    inp_out <- list("", list(plots = input$sm_plots, custom = FALSE))
    figs <- TRUE
  }
  update_report(inp_main = clean_args(sm_inputs(), sm_args),
                fun_name = "single_mean", inp_out = inp_out,
                outputs = outputs, figs = figs,
                fig.width = sm_plot_width(),
                fig.height = sm_plot_height())
})
