R/mod_home.R

In lanceupton/shinyROI: shinyROI

# Module UI

#' @title   mod_home_ui and mod_home_server
#' @description  A shiny Module.
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_home
#'
#' @keywords internal
#' @export
#' @importFrom shiny NS tagList
mod_home_ui <- function(id){
  ns <- NS(id)
  uiOutput(ns("ui"))
}

# Module Server

#' @rdname mod_home
#' @export
#' @keywords internal

mod_home_server <- function(input, output, session) {
  ns <- session$ns

  # # Load server logic for modules
  # summaryData() %>% map(~ callModule(mod_summaryBox_server, .x))

  # UI ----------------------------------------------------------------------

  output$ui <- renderUI({
    tagList(
      fluidRow(uiOutput("top")),
      fluidRow(uiOutput("bot"))
    )
  })

  output$top <- renderUI({
    tagList(
      fluidRow(bs4Dash::box('summary for the last 8 weeks')),
      fluidRow(column(width = 6, uiOutput(ns("ui_costs"))),
               column(width = 6, uiOutput(ns("ui_conversions")),
                      uiOutput(ns("ui_cpa"))))
    )
  })

  # output$bot <- renderUI({
  #   summaryData() %>% map(mod_summaryBox_ui)
  # })
  #
  # output$costs <- renderUI({
  #   tagList(
  #     div('costs'),
  #     plotlyOutput(ns('costs_plot'), width = '100%', height = '100%')
  #   )
  # })
  #
  # output$conversions <- renderUI({
  #   tagList(
  #     uiOutput(ns('conversions_header')),
  #     uiOutput(ns('conversions_plot'))
  #   )
  # })
  #
  # output$conversions_header <- renderUI({
  #   tagList(
  #     div('conversions'),
  #     div(paste0(per_conversions(), '%'),
  #         style = paste0('color: ',ifelse(
  #           per_conversions() < 0, '#ff0d0d', '#00ffaa'
  #         ))
  #     )
  #   )
  # })
  #
  # output$conversions_plot <- renderUI({
  #   # Normalize heights
  #   max_val <- home_plot_conversions_data() %>%
  #     unlist() %>% as.numeric() %>% max()
  #   lapply(X = names(home_plot_conversions_data()), FUN = function(x) {
  #     val <- home_plot_conversions_data()[x] %>% as.numeric()
  #     div(
  #       div(x),
  #       div(
  #         round(x = val, digits = 0),
  #         style = paste0('height: ', val / as.numeric(max_val) * 100, '%')
  #       )
  #     )
  #   })
  # })
  #
  # output$cpa <- renderUI({
  #   tagList(
  #     uiOutput(ns('cpa_header')),
  #     uiOutput(ns('cpa_plot'))
  #   )
  # })
  #
  # output$cpa_header <- renderUI({
  #   tagList(
  #     div('cpa'),
  #     div(paste0(per_cpa(), '%'),
  #         style = paste0('color: ',ifelse(
  #           per_cpa() < 0, '#ff0d0d', '#00ffaa'
  #         ))
  #     )
  #   )
  # })
  #
  # output$cpa_plot <- renderUI({
  #   # Normalize heights
  #   max_val <- home_plot_cpa_data() %>%
  #     unlist() %>% max()
  #   lapply(X = names(home_plot_cpa_data()), FUN = function(x) {
  #     val <- home_plot_cpa_data()[x] %>% as.numeric()
  #     div(
  #       div(x),
  #       div(
  #         val %>% round(digits = 0),
  #         style = paste0('height: ', val / as.numeric(max_val) * 100, '%')
  #       )
  #     )
  #   })
  # })
  #
  # output$costs_plot <- renderPlotly({
  #   p <- ggplot(
  #     data = home_plot_trend_data(),
  #     mapping = aes(x = week_id)
  #   ) +
  #     geom_line(mapping = aes(y = last), size = 1.5, color = '#00c8ff') +
  #     geom_line(mapping = aes(y = algorithmic), size = 1.5, color = '#00ffaa') +
  #     theme(
  #       panel.background   = element_blank(),
  #       plot.background    = element_blank(),
  #       panel.grid.minor.x = element_blank(),
  #       panel.grid.major.x = element_blank(),
  #       panel.grid.minor.y = element_blank(),
  #       panel.grid.major.y = element_line(color = '#111111'),
  #       axis.title = element_blank(),
  #       axis.text = element_text(family = 'Lato', colour = '#8a8a8a')
  #     )
  #   ggplotly(p = p) %>%
  #     layout(plot_bgcolor = 'transparent') %>%
  #     layout(paper_bgcolor = 'transparent')
  # })
  #
  # output$bottom <- renderUI({
  #   lapply(X = ns(8:1), FUN = homeModuleUI)
  # })
  #
  # # DATA --------------------------------------------------------------------
  #
  # ga_this <- reactive({
  #   ga_full() %>%
  #     filter(
  #       week_id %in% ga_weeks()[1:8]
  #     ) %>%
  #     group_by(week_id) %>%
  #     summarise(
  #       paths       = paths[1],
  #       first       = sum(first, na.rm = TRUE),
  #       # last_n_d = last touch exluding direct channel
  #       # Must calculate before last to avoid NS collision!
  #       last_n_d    = sum(last[event != '(direct)/(none)'], na.rm = TRUE),
  #       last        = sum(last, na.rm = TRUE),
  #       linear      = sum(linear, na.rm = TRUE),
  #       algorithmic = sum(algorithmic, na.rm = TRUE),
  #       cost        = cost[1]
  #     ) %>%
  #     select(week_id, paths, first, last_n_d, last, linear, algorithmic, cost)
  # })
  #
  # home_plot_trend_data <- reactive({
  #   ga_this() %>%
  #     group_by(week_id)
  # })
  #
  # home_plot_conversions_data <- reactive({
  #   ga_this() %>%
  #     summarise(
  #       first       = sum(first, na.rm = TRUE),
  #       last_n_d    = sum(last_n_d, na.rm = TRUE),
  #       last        = sum(last, na.rm = TRUE),
  #       linear      = sum(linear, na.rm = TRUE),
  #       algorithmic = sum(algorithmic, na.rm = TRUE)
  #     ) %>%
  #     select(first, last_n_d, last, linear, algorithmic) %>%
  #     setNames(c('First', 'Last n-d', 'Last', 'Linear', 'Algorithmic')) %>%
  #     as.list()
  # })
  #
  # home_plot_cpa_data <- reactive({
  #   this_cost <- sum(ga_this()$cost, na.rm = TRUE)
  #   ga_this() %>%
  #     summarise(
  #       first       = this_cost / sum(first, na.rm = TRUE),
  #       last_n_d    = this_cost / sum(last_n_d, na.rm = TRUE),
  #       last        = this_cost / sum(last, na.rm = TRUE),
  #       linear      = this_cost / sum(linear, na.rm = TRUE),
  #       algorithmic = this_cost / sum(algorithmic, na.rm = TRUE)
  #     ) %>%
  #     setNames(c('First', 'Last n-d', 'Last', 'Linear', 'Algorithmic')) %>%
  #     as.list()
  # })
  #
  # # Percent difference (Algorithmic - last touch)
  # per_cpa <- reactive({
  #   home_plot_cpa_data() %>%
  #     data.frame() %>%
  #     transmute(Algorithmic - Last) %>%
  #     round(digits = 0)
  # })
  # per_conversions <- reactive({
  #   home_plot_conversions_data() %>%
  #     data.frame() %>%
  #     transmute(Algorithmic - Last) %>%
  #     round(digits = 0)
  # })

}