djprlabourdash: DJSIR Jobs Dashboard

#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_overview <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                             unset = "dashboard"
                           ),
                           title = paste0(
                             "Victorian employment summary, ",
                             format(max(data$date), "%B %Y")
                           ),
                           df = dash_data
                           ) {

  series_ids = c(
    "A84423354L",
    "A84423242V",
    "A84423466F",
    "A84424691V",
    "A84600079X",
    "A84423350C",
    "A84423349V",
    "A84423357V",
    "pt_emp_vic",
    "A84423237A",
    "A84423461V",
    "A84424687C",
    "A84423355R",
    "A84423243W",
    "A84423467J",
    "A84424692W",
    "A84426256L",
    "A85223450L",
    "A85223451R",
    "A84423356T"
  )

  data <- filter_dash_data(
    series_ids = series_ids,
    df = df
  )

  # Youth data = 12m rolling average
  data <- data %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = dplyr::if_else(.data$series_id %in% c(
      "A84433601W",
      "A84424691V",
      "A84424687C",
      "A84424692W"
    ),
    slider::slide_mean(.data$value, before = 11, complete = TRUE),
    .data$value
    ))

  # Regional data = 3m rolling average
  data <- data %>%
    dplyr::mutate(value = dplyr::if_else(.data$series_id == "A84600079X",
      slider::slide_mean(.data$value, before = 2, complete = TRUE),
      .data$value
    )) %>%
    dplyr::ungroup()

  make_table_mem(
    data = data,
    destination = destination,
    title = title,
    row_order = series_ids,
    highlight_rows = c(
      "A84423354L",
      "A84423349V",
      "A84423355R",
      "A84426256L",
      "A85223450L",
      "A85223451R",
      "A84423356T"
    ),
    notes = " All data seasonally adjusted, other than youth figures, which are smoothed using a 12-month rolling average, and regional figures, which are smoothed using a 3-month rolling average."
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_gr_sex <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                           unset = "dashboard"
                         ),
                         title = paste0(
                           "Victorian employment summary by sex, ",
                           format(max(data$date), "%B %Y")
                         ),
                         df = dash_data) {

  series_ids <- c(
    "A84423237A",
    "A84423461V",
    "A84423238C",
    "A84423462W",
    "A84423242V",
    "A84423466F",
    "A84423243W",
    "A84423467J"
  )

  data <- filter_dash_data(series_ids, df = df)

  make_table_mem(data,
    row_order = series_ids,
    title = title,
    destination = destination,
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_gr_youth_summary <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                     unset = "dashboard"
                                   ),
                                   title = paste0(
                                     "Victorian youth (15-24) labour force status summary, ",
                                     format(max(data$date), "%B %Y"),
                                     " (12-month average)"
                                   ),
                                   df = dash_data) {
  series_ids <- c(
    "A84424687C",
    "A84424691V",
    "15-24_males_unemployment rate",
    "15-24_females_unemployment rate",
    "A84424688F",
    "A84424692W",
    "A84424602F"
  )

  data <- filter_dash_data(series_ids, df = df)

  data <- data %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value, before = 11, complete = TRUE)) %>%
    dplyr::ungroup() %>%
    dplyr::filter(!is.na(.data$value))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c(
        "A84424687C",
        "A84424691V",
        "A84424688F",
        "A84424692W"
      ),
      title = title,
      destination = destination,
      notes = "Data not seasonally adjusted; smoothed using a 12-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_gr_youth_unemp_region <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                          unset = "dashboard"
                                        ),
                                        title = paste0(
                                          "Youth (15-24) unemployment rate across Victoria, ",
                                          format(max(data$date), "%B %Y"),
                                          " (12-month average)"
                                        ),
                                        df = dash_data) {

  data <- filter_dash_data(
    series_ids = c(
      "15-24_employed_ballarat",
      "15-24_employed_bendigo",
      "15-24_employed_geelong",
      "15-24_employed_hume",
      "15-24_employed_latrobe - gippsland",
      "15-24_employed_shepparton",
      "15-24_employed_victoria - north west",
      "15-24_employed_warrnambool and south west",
      "15-24_employed_rest of vic.",
      "15-24_unemployed_ballarat",
      "15-24_unemployed_bendigo",
      "15-24_unemployed_geelong",
      "15-24_unemployed_hume",
      "15-24_unemployed_latrobe - gippsland",
      "15-24_unemployed_shepparton",
      "15-24_unemployed_victoria - north west",
      "15-24_unemployed_warrnambool and south west",
      "15-24_unemployed_rest of vic.",
      "15-24_employed_melbourne - inner",
      "15-24_employed_melbourne - inner east",
      "15-24_employed_melbourne - inner south",
      "15-24_employed_melbourne - north east",
      "15-24_employed_melbourne - north west",
      "15-24_employed_melbourne - outer east",
      "15-24_employed_melbourne - south east",
      "15-24_employed_melbourne - west",
      "15-24_employed_mornington peninsula",
      "15-24_employed_greater melbourne",
      "15-24_unemployed_melbourne - inner",
      "15-24_unemployed_melbourne - inner east",
      "15-24_unemployed_melbourne - inner south",
      "15-24_unemployed_melbourne - north east",
      "15-24_unemployed_melbourne - north west",
      "15-24_unemployed_melbourne - outer east",
      "15-24_unemployed_melbourne - south east",
      "15-24_unemployed_melbourne - west",
      "15-24_unemployed_mornington peninsula",
      "15-24_unemployed_greater melbourne"
    ),
    df = df
  )

  data <- data %>%
    dplyr::select(
      .data$date, .data$series, .data$table_no,
      .data$frequency, .data$value
    ) %>%
    dplyr::mutate(
      split_series = stringr::str_split_fixed(.data$series,
        pattern = " ; ",
        n = 3
      ),
      age = .data$split_series[, 1],
      indicator = .data$split_series[, 2],
      sa4 = .data$split_series[, 3]
    ) %>%
    dplyr::select(-.data$split_series, -.data$series)

  data <- data %>%
    tidyr::pivot_wider(
      names_from = .data$indicator,
      values_from = .data$value
    ) %>%
    dplyr::group_by(.data$sa4, .data$date, .data$age, .data$table_no, .data$frequency) %>%
    dplyr::mutate(value = 100 * (.data$Unemployed /
      (.data$Unemployed + .data$Employed))) %>%
    dplyr::select(-.data$Employed, -.data$Unemployed) %>%
    dplyr::group_by(.data$sa4, .data$age) %>%
    dplyr::mutate(
      indicator = "Unemployment rate",
      value = slider::slide_mean(.data$value,
        before = 11L,
        complete = TRUE
      )
    ) %>%
    dplyr::filter(!is.na(.data$value)) %>%
    dplyr::mutate(
      unit = "Percent",
      series_id = paste(.data$age, .data$indicator, .data$sa4, sep = "_"),
      series = paste(.data$age, .data$indicator, .data$sa4, sep = " ; ")
    ) %>%
    dplyr::ungroup()

  data <- data %>%
    dplyr::mutate(
      sa4 = dplyr::if_else(.data$sa4 == "Rest of Vic.",
        "Regional Victoria",
        .data$sa4
      ),
      indicator = dplyr::if_else(
        .data$sa4 %in% c(
          "Greater Melbourne",
          "Regional Victoria"
        ),
        paste0(.data$sa4, " youth unemployment rate"),
        .data$sa4
      )
    )

  data %>%
    make_table_mem(
      rename_indicators = F,
      row_order = c(
        "15-24_Unemployment rate_Greater Melbourne",
        "15-24_Unemployment rate_Melbourne - Inner",
        "15-24_Unemployment rate_Melbourne - Inner East",
        "15-24_Unemployment rate_Melbourne - Inner South",
        "15-24_Unemployment rate_Melbourne - North East",
        "15-24_Unemployment rate_Melbourne - North West",
        "15-24_Unemployment rate_Melbourne - Outer East",
        "15-24_Unemployment rate_Melbourne - South East",
        "15-24_Unemployment rate_Melbourne - West",
        "15-24_Unemployment rate_Mornington Peninsula",
        "15-24_Unemployment rate_Rest of Vic.",
        "15-24_Unemployment rate_Ballarat",
        "15-24_Unemployment rate_Bendigo",
        "15-24_Unemployment rate_Geelong",
        "15-24_Unemployment rate_Hume",
        "15-24_Unemployment rate_Latrobe - Gippsland",
        "15-24_Unemployment rate_Shepparton",
        "15-24_Unemployment rate_Victoria - North West",
        "15-24_Unemployment rate_Warrnambool and South West"
      ),
      highlight_rows = c(
        "15-24_Unemployment rate_Rest of Vic.",
        "15-24_Unemployment rate_Greater Melbourne"
      ),
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 12-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_nonmetro_states_unemprate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                                  unset = "dashboard"
                                                ),
                                                title = paste0(
                                                  "Regional unemployment rate by state, ",
                                                  format(max(data$date), "%B %Y"),
                                                  " (3-month average)"
                                                ),
                                                df = dash_data) {
  series_ids <- c(
    "A84600079X",
    "A84599629X",
    "A84599785A",
    "A84600247X",
    "A84599719C",
    "A84599635V"
  )

  data <- filter_dash_data(series_ids, df)

  data <- data %>%
    dplyr::mutate(indicator = gsub("Rest of ", "Regional ", .data$gcc_restofstate, fixed = T)) %>%
    dplyr::group_by(.data$indicator) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    )) %>%
    dplyr::ungroup()

  make_table_mem(data,
    row_order = series_ids,
    rename_indicators = FALSE,
    destination = destination,
    title = title,
    notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_nonmetro_emp <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                     unset = "dashboard"
                                   ),
                                   title = paste0(
                                     "Employment across regional Victoria, ",
                                     format(max(data$date), "%B %Y"),
                                     " (3-month average)"
                                   ),
                                   df = dash_data) {
  series_ids <- c(
    "A84600075R",
    "A84599661X",
    "A84600027W",
    "A84599667L",
    "A84599673J",
    "A84599679W",
    "A84599925T",
    "A84600117A",
    "A84600033T"
  )
  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Regional Victoria employed persons",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600075R"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_nonmetro_unemp <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                       unset = "dashboard"
                                     ),
                                     title = paste0(
                                       "Unemployment across regional Victoria, ",
                                       format(max(data$date), "%B %Y"),
                                       " (3-month average)"
                                     ),
                                     df = dash_data) {
  series_ids <- c(
    "A84600076T",
    "A84599662A",
    "A84600028X",
    "A84599668R",
    "A84599674K",
    "A84599680F",
    "A84599926V",
    "A84600118C",
    "A84600034V"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Regional Victoria unemployed persons",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600076T"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_nonmetro_unemprate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                           unset = "dashboard"
                                         ),
                                         title = paste0(
                                           "Unemployment rate across regional Victoria, ",
                                           format(max(data$date), "%B %Y"),
                                           " (3-month average)"
                                         ),
                                         df = dash_data) {
  series_ids <- c(
    "A84595471L",
    "A84599665J",
    "A84600031L",
    "A84599671C",
    "A84599677T",
    "A84599683L",
    "A84599929A",
    "A84600121T",
    "A84600037A"
  )

  data <- filter_dash_data(series_ids, df)  %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Regional Victoria unemployment rate",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84595471L"),
      rename_indicators = FALSE,
      title = title,
      destination = destination,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_nonmetro_partrate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                          unset = "dashboard"
                                        ),
                                        title = paste0(
                                          "Participation rate across regional Victoria, ",
                                          format(max(data$date), "%B %Y"),
                                          " (3-month average)"
                                        ),
                                        df = dash_data) {
  series_ids <- c(
    "A84600080J",
    "A84599666K",
    "A84600032R",
    "A84599672F",
    "A84599678V",
    "A84599684R",
    "A84599930K",
    "A84600122V",
    "A84600038C"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Regional Victoria participation rate",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600080J"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_metro_states_unemprate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                               unset = "dashboard"
                                             ),
                                             title = paste0(
                                               "Unemployment rate in metropolitan areas by State, ",
                                               format(max(data$date), "%B %Y")
                                             ),
                                             df = dash_data) {
  series_ids <- c(
    "A84600145K",
    "A84599623K",
    "A84600151F",
    "A84600241K",
    "A84600157V",
    "A84599791W"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(indicator = gsub("Greater ", "", .data$gcc_restofstate,
      fixed = T
    )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    )) %>%
    dplyr::filter(!is.na(.data$value))

  make_table_mem(data,
    title = title,
    destination = destination,
    row_order = series_ids,
    notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_metro_emp <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                  unset = "dashboard"
                                ),
                                title = paste0(
                                  "Employment across Greater Melbourne, ",
                                  format(max(data$date), "%B %Y"),
                                  " (3-month average)"
                                ),
                                df = dash_data) {
  series_ids <- c(
    "A84600141A",
    "A84599655C",
    "A84600015L",
    "A84600183X",
    "A84599553R",
    "A84600111L",
    "A84599847W",
    "A84599919W",
    "A84600021J",
    "A84600189L"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Greater Melbourne employed persons",
        .data$sa4
      )
    ) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600141A"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_metro_unemp <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                    unset = "dashboard"
                                  ),
                                  title = paste0(
                                    "Unemployed persons across Greater Melbourne, ",
                                    format(max(data$date), "%B %Y"),
                                    " (3-month average)"
                                  ),
                                  df = dash_data) {
  series_ids <- c(
    "A84600142C",
    "A84599656F",
    "A84600016R",
    "A84600184A",
    "A84599554T",
    "A84600112R",
    "A84599848X",
    "A84599920F",
    "A84600022K",
    "A84600190W"
  )

  data <- filter_dash_data(series_ids) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Greater Melbourne unemployed persons",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600142C"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_metro_unemprate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                        unset = "dashboard"
                                      ),
                                      title = paste0(
                                        "Unemployment rate across Greater Melbourne, ",
                                        format(max(data$date), "%B %Y"),
                                        " (3-month average)"
                                      ),
                                      df = dash_data) {
  series_ids <- c(
    "A84600145K",
    "A84599659L",
    "A84600019W",
    "A84600187J",
    "A84599557X",
    "A84600115W",
    "A84599851L",
    "A84599923L",
    "A84600025T",
    "A84600193C"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Greater Melbourne unemployment rate",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600145K"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_reg_metro_partrate <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                       unset = "dashboard"
                                     ),
                                     title = paste0(
                                       "Participation rate across Greater Melbourne, ",
                                       format(max(data$date), "%B %Y"),
                                       " (3-month average)"
                                     ),
                                     df = dash_data) {
  series_ids <- c(
    "A84600146L",
    "A84599660W",
    "A84600020F",
    "A84600188K",
    "A84599558A",
    "A84600116X",
    "A84599852R",
    "A84599924R",
    "A84600026V",
    "A84600194F"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(
      indicator = dplyr::if_else(.data$sa4 == "",
        "Greater Melbourne participation rate",
        .data$sa4
      )) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2L,
      complete = TRUE
    ))

  data %>%
    make_table_mem(
      row_order = series_ids,
      highlight_rows = c("A84600146L"),
      rename_indicators = FALSE,
      destination = destination,
      title = title,
      notes = "Data not seasonally adjusted; smoothed using a 3-month rolling average."
    )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_ind_unemp_state <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                    unset = "dashboard"
                                  ),
                                  title = paste0(
                                    "Unemployment rate by state, ",
                                    format(max(data$date), "%B %Y")
                                  ),
                                  df = dash_data) {
  series_ids <- c(
    "A84423050A",
    "A84423354L",
    "A84423270C",
    "A84423284T",
    "A84423326C",
    "A84423368A",
    "A84423298F"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(indicator = dplyr::if_else(.data$state == "", "Australia", .data$state))

  make_table_mem(
    data = data,
    row_order = series_ids,
    title = title,
    destination = destination,
    rename_indicators = F
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_ind_employment <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                   unset = "dashboard"
                                 ),
                                 df = dash_data) {
  series_ids = c(
    "A84423349V",
    "A84423357V",
    "pt_emp_vic",
    "A84423356T",
    "A84423244X",
    "A84423468K"
  )

  data <- filter_dash_data(series_ids, df) %>%
    mutate(indicator = if_else(.data$sex != "",
      paste0(.data$indicator, " (", .data$sex, ")"),
      .data$indicator
    ))

  make_table_mem(data,
    row_order = series_ids,
    highlight_rows = c(
      "A84423349V",
      "A84423356T"
    ),
    destination = destination,
    notes = "Data not seasonally adjusted."
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_ind_unemp_summary <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                      unset = "dashboard"
                                    ),
                                    df = dash_data) {
  series_ids <- c(
    "A84423350C",
    "A84423242V",
    "A84423466F",
    "A84424691V",
    "A84423354L",
    "A85223451R"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = dplyr::if_else(.data$series_id == "A84424691V",
      slider::slide_mean(.data$value, before = 11, complete = TRUE),
      .data$value
    )) %>%
    dplyr::ungroup() %>%
    mutate(indicator = if_else(.data$sex != "",
      paste0(.data$indicator, " (", .data$sex, ")"),
      .data$indicator
    ))

  make_table_mem(data,
    row_order = series_ids,
    highlight_rows = c(
      "A84423350C",
      "A84423354L",
      "A85223451R"
    ),
    destination = destination,
    notes = "Data not seasonally adjusted; smoothed using a 12-month rolling average."
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_ind_hours_summary <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                      unset = "dashboard"
                                    ),
                                    df = dash_data) {

  data <- filter_dash_data("A84426256L", df) %>%
    mutate(indicator = if_else(.data$sex != "",
      paste0(.data$indicator, " (", .data$sex, ")"),
      .data$indicator
    ))

  make_table_mem(data,
    destination = destination
  )
}


#' @rdname tables
#' Produce tables for LFS dashboard and associated briefing materials
#' @export
#' @noRd
table_industries_summary <- function(destination = Sys.getenv("R_DJPRLABOURDASH_TABLEDEST",
                                       unset = "dashboard"
                                     ),
                                     title = paste0(
                                       "Victorian employment by industry, ",
                                       format(max(data$date), "%B %Y"),
                                       " quarter (not seasonally adjusted)"
                                     ),
                                     df = dash_data) {
  series_ids <- c(
    "A84601662A",
    "A84601680F",
    "A84601683L",
    "A84601686V",
    "A84601665J",
    "A84601704L",
    "A84601707V",
    "A84601710J",
    "A84601638A",
    "A84601653X",
    "A84601689A",
    "A84601656F",
    "A84601713R",
    "A84601668R",
    "A84601695W",
    "A84601698C",
    "A84601650T",
    "A84601671C",
    "A84601641R",
    "A84601716W"
  )

  data <- filter_dash_data(series_ids, df) %>%
    dplyr::mutate(indicator = dplyr::if_else(.data$industry != "",
      .data$industry,
      "Victoria - all industries"
    ))

  make_table_mem(data,
    row_order = series_ids,
    highlight_rows = "A84601662A",
    title = title,
    notes = "Data is original (not seasonally adjusted).",
    destination = destination,
    rename_indicators = FALSE
  )
}