R/mod_cum_usage_plot.R

In MHenderson/energy-use: My Energy Usage

#' cum_usage_plot UI Function
#'
#' @description A shiny Module.
#'
#' @param id,input,output,session Internal parameters for {shiny}.
#'
#' @noRd
#'
#' @importFrom shiny NS tagList
mod_cum_usage_plot_ui <- function(id){
  ns <- NS(id)
  tagList(
    column(10,
      shinycssloaders::withSpinner(dygraphs::dygraphOutput(ns("cum_usage_plot")))
    ),
    column(2,
      shinycssloaders::withSpinner(uiOutput(ns("cum_usage_info"))),
      shinycssloaders::withSpinner(tableOutput(ns("savings_table")))
    )
  )
}

#' cum_usage_plot Server Function
#'
#' @noRd
mod_cum_usage_plot_server <- function(id, tidy_energy, plot1vars){
  moduleServer( id, function(input, output, session){
  ns <- session$ns

  id_fuel_cumsum <- reactive({
    id_codes <- tibble::tribble(
      ~seq_id, ~code,
      1, "SP",
      2, "YE",
      3, "TE",
      4, "GS",
      5, ""
    )
    tidy_energy %>%
      dplyr::filter(
        var == plot1vars$var(),
        seq_id %in% c(plot1vars$tariff(), if(plot1vars$history()) c(5) else c()),
        fuel %in% plot1vars$fuel()
      ) %>%
      dplyr::select(seq_id, fuel, date, value) %>%
      dplyr::left_join(id_codes, by = "seq_id") %>%
      dplyr::mutate(
        fuel_code = substr(fuel, 1, 1),
        id_fuel = paste0(code, "_", fuel_code)
      ) %>%
      #dplyr::select(id_fuel, date, value) %>%
      dplyr::group_by(id_fuel) %>%
      dplyr::mutate(
        seq_id = dplyr::first(seq_id),
        fuel = dplyr::first(fuel),
        value = cumsum(value)
      ) %>%
      dplyr::ungroup()
  })

  id_fuel_cumsum_window_summary <- reactive({
    date_window <- as.Date(req(input$cum_usage_plot_date_window))
    id_fuel_cumsum() %>%
      dplyr::filter(
        date >= date_window[1],
        date <= date_window[2]
      ) %>%
      dplyr::group_by(id_fuel) %>%
      dplyr::summarise(
        seq_id = dplyr::first(seq_id),
        fuel = dplyr::first(fuel),
        max_value = max(value),
        min_value = min(value),
        diff_value = max_value - min_value
      )
  })

  gas_total <- reactive({
    id_fuel_cumsum_window_summary() %>%
      dplyr::filter(id_fuel == "_g") %>%
      dplyr::pull("diff_value") %>%
      round(2)
  })

  electricity_total <- reactive({
    id_fuel_cumsum_window_summary() %>%
      dplyr::filter(id_fuel == "_e") %>%
      dplyr::pull("diff_value") %>%
      round(2)
  })

  gas_savings <- reactive({
    id_fuel_cumsum_window_summary() %>%
      dplyr::filter(fuel == "gas") %>%
      dplyr::mutate(
        savings = diff_value - gas_total()
      )
  })

  electricity_savings <- reactive({
    id_fuel_cumsum_window_summary() %>%
      dplyr::filter(fuel == "electricity") %>%
      dplyr::mutate(
        savings = diff_value - electricity_total()
      )
  })

  savings <- reactive({
    dplyr::bind_rows(gas_savings(), electricity_savings())
  })

  output$savings_table <- renderTable({
    savings_data <- savings() %>%
      dplyr::filter(id_fuel != "_e") %>%
      dplyr::filter(id_fuel != "_g")

    result <- tibble::tibble()

    if(nrow(savings_data) > 0) {
      result <- savings_data %>%
        dplyr::select(id_fuel, savings) %>%
        tidyr::separate(id_fuel, into = c("id", "fuel")) %>%
        tidyr::pivot_wider(names_from = "fuel", values_from = "savings")

    }

    if("g" %in% colnames(result) & "e" %in% colnames(result)) {
      result <- result %>%
        dplyr::mutate(
          t = g + e
        )
    }

    result

  })

  output$cum_usage_info <- renderUI({

    shinydashboardPlus::boxPad(
      color = "green",
      shinydashboardPlus::descriptionBlock(
        header = gas_total(),
        text = "Gas",
        rightBorder = FALSE,
        marginBottom = TRUE
      ),
      shinydashboardPlus::descriptionBlock(
        header = electricity_total(),
        text = "Electricity",
        rightBorder = FALSE,
        marginBottom = TRUE
      ),
      shinydashboardPlus::descriptionBlock(
        header = gas_total() + electricity_total(),
        text = "Total",
        rightBorder = FALSE,
        marginBottom = TRUE
      )
    )

  })

  output$cum_usage_plot <- dygraphs::renderDygraph({

    q <- id_fuel_cumsum() %>%
      dplyr::select(id_fuel, date, value) %>%
      tidyr::pivot_wider(names_from = id_fuel, values_from = value)

    if(nrow(q) == 0) return()

    q <- tibble::as_tibble(q)
    xq <- xts::xts(q[,-1], order.by = q$date)

    if(plot1vars$var() == "cost") {
      y_axis_label <- "GBP"
    } else {
      y_axis_label <- "kWh"
    }

    p <- dygraphs::dygraph(xq, group = "usage") %>%
      dygraphs::dyRangeSelector(dateWindow = c("2021-01-01", as.character(Sys.Date()))) %>%
      dygraphs::dyOptions(stepPlot = TRUE) %>%
      dygraphs::dyLegend(show = "follow") %>%
      dygraphs::dyAxis("y", label = y_axis_label) %>%
      dygraphs::dyHighlight(highlightSeriesBackgroundAlpha = 0.2)

    if("_g" %in% colnames(q)) {
      p <- p %>%
        dygraphs::dySeries("_g", color = "#BDBDBD", strokePattern = "dashed")
    }

    if("_e" %in% colnames(q)) {
      p <- p %>%
        dygraphs::dySeries("_e", color = "#636363", strokePattern = "dashed")
    }

    p

  })
  })
}

## To be copied in the UI
# mod_cum_usage_plot_ui("cum_usage_plot_ui_1")

## To be copied in the server
# callModule(mod_cum_usage_plot_server, "cum_usage_plot_ui_1")