djprlabourdash: DJSIR Jobs Dashboard

#' @importFrom rlang `:=`
data_reg_unemp_emppop_partrate_vic <- function(data = filter_dash_data(c(
                                                 "A84599659L",
                                                 "A84600019W",
                                                 "A84600187J",
                                                 "A84599557X",
                                                 "A84600115W",
                                                 "A84599851L",
                                                 "A84599923L",
                                                 "A84600025T",
                                                 "A84600193C",
                                                 "A84599665J",
                                                 "A84600031L",
                                                 "A84599671C",
                                                 "A84599677T",
                                                 "A84599683L",
                                                 "A84599929A",
                                                 "A84600121T",
                                                 "A84600037A",
                                                 "A84599658K",
                                                 "A84599660W",
                                                 "A84600018V",
                                                 "A84600020F",
                                                 "A84600186F",
                                                 "A84600188K",
                                                 "A84599556W",
                                                 "A84599558A",
                                                 "A84600114V",
                                                 "A84600116X",
                                                 "A84599850K",
                                                 "A84599852R",
                                                 "A84599922K",
                                                 "A84599924R",
                                                 "A84600024R",
                                                 "A84600026V",
                                                 "A84600192A",
                                                 "A84600194F",
                                                 "A84599664F",
                                                 "A84599666K",
                                                 "A84600030K",
                                                 "A84600032R",
                                                 "A84599670A",
                                                 "A84599672F",
                                                 "A84599676R",
                                                 "A84599678V",
                                                 "A84599682K",
                                                 "A84599684R",
                                                 "A84599928X",
                                                 "A84599930K",
                                                 "A84600120R",
                                                 "A84600122V",
                                                 "A84600036X",
                                                 "A84600038C"
                                               ),
                                               df = dash_data
                                               ),
                                               selected_indicator = "unemp_rate") {
  df <- data %>%
    mutate(indicator_short = dplyr::case_when(
      .data$indicator == "Unemployment rate" ~ "unemp_rate",
      .data$indicator == "Participation rate" ~ "part_rate",
      .data$indicator == "Employment to population ratio" ~ "emp_pop"
    ))

  # Reduce to selected_indicator
  df <- df %>%
    dplyr::filter(.data$indicator_short == selected_indicator)

  # 3 month smoothing
  df <- df %>%
    group_by(.data$series_id) %>%
    mutate(value = slider::slide_mean(.data$value,
      before = 2,
      complete = TRUE
    )) %>%
    dplyr::filter(.data$date == max(.data$date))

  df
}


title_reg_unemp_emppop_partrate_vic <- function(data = data_reg_unemp_emppop_partrate_vic(),
                                                selected_indicator = "unemp_rate") {
  high_low <- data %>%
    dplyr::ungroup() %>%
    summarise(
      min_sa4 = .data$sa4[.data$value == min(.data$value)],
      min_ur = .data$value[.data$value == min(.data$value)],
      max_sa4 = .data$sa4[.data$value == max(.data$value)],
      max_ur = .data$value[.data$value == max(.data$value)],
      date = unique(.data$date)
    )

  indic_long <- dplyr::case_when(
    selected_indicator == "unemp_rate" ~ "Unemployment rate",
    selected_indicator == "part_rate" ~ "Participation rate",
    selected_indicator == "emp_pop" ~ "Employment to population ratio",
    TRUE ~ NA_character_
  )

  paste0(
    indic_long,
    " across Victoria ranged from ",
    round2(high_low$min_ur, 1),
    " per cent in ",
    high_low$min_sa4,
    " to ",
    round2(high_low$max_ur, 1),
    " per cent in ",
    high_low$max_sa4,
    " as at ",
    format(high_low$date, "%B %Y")
  )
}

map_reg_unemp_emppop_partrate_vic <- function(data = data_reg_unemp_emppop_partrate_vic(),
                                              selected_indicator = "unemp_rate",
                                              zoom = 6) {
  indic_long <- dplyr::case_when(
    selected_indicator == "unemp_rate" ~ "Unemployment rate",
    selected_indicator == "part_rate" ~ "Participation rate",
    selected_indicator == "emp_pop" ~ "Employment to population ratio",
    TRUE ~ NA_character_
  )


  # Call SA4 shape file, but only load Victoria and exclude 'weird' areas (migratory and other one)
  sa4_shp <- sa42016 %>%
    dplyr::filter(.data$state_name_2016 == "Victoria") %>%
    dplyr::filter(.data$sa4_code_2016 < 297)

  # Fix issue with different naming for North West region in Victoria
  df <- data %>%
    dplyr::mutate(
      sa4 = dplyr::if_else(.data$sa4 == "Victoria - North West",
        "North West",
        .data$sa4
      )
    )

  # Join shape file with data to create mapdata ----
  mapdata <- sa4_shp %>%
    dplyr::left_join(df, by = c("sa4_name_2016" = "sa4"))

  # Create colour palette
  # Switched here from binned to continuous colours
  # pal <- leaflet::colorBin("Blues", mapdata$value, 3) # last object is number of bins
  pal <- leaflet::colorNumeric("Blues", c(min(mapdata$value), max(mapdata$value)), alpha = T)

  # Create metro boundary (Greater Melbourne) ----
  metro_boundary_sa4 <- c(
    "Melbourne - Inner", "Melbourne - Inner East", "Melbourne - Inner South", "Melbourne - North East",
    "Melbourne - North West", "Melbourne - Outer East", "Melbourne - South East", "Melbourne - West",
    "Mornington Peninsula"
  )

  mapdata <- mapdata %>%
    sf::st_transform("+proj=longlat +datum=WGS84")

  metro_outline <- mapdata %>%
    dplyr::filter(.data$sa4_name_2016 %in% metro_boundary_sa4) %>%
    dplyr::summarise(areasqkm_2016 = sum(.data$areasqkm_2016))

  label_title <- dplyr::case_when(
    selected_indicator == "unemp_rate" ~ paste0("Unemployment<br/> rate (per cent)"),
    selected_indicator == "part_rate" ~ paste0("Participation<br/> rate (per cent)"),
    selected_indicator == "emp_pop" ~ paste0("Employment to<br/> population ratio<br/> (per cent)"),
    TRUE ~ NA_character_
  )

  # Produce dynamic map, all of Victoria ----
  map <- mapdata %>%
    leaflet::leaflet(options = leaflet::leafletOptions(background = "white")) %>%
    leaflet::setView(
      lng = 145.4657, lat = -36.41472, # coordinates of map at first view
      zoom = zoom
    ) %>%
    # size of map at first view
    leaflet::addPolygons(
      color = "grey", # colour of boundary lines, 'transparent' for no lines
      weight = 1, # thickness of boundary lines
      fillColor = ~ pal(mapdata$value), # pre-defined above
      fillOpacity = 1.0, # strength of fill colour
      smoothFactor = 0.5, # smoothing between region
      stroke = T,
      highlightOptions = leaflet::highlightOptions( # to highlight regions as you hover over them
        color = "black", # boundary colour of region you hover over
        weight = 2, # thickness of region boundary
        bringToFront = FALSE
      ), # FALSE = metro outline remains
      label = sprintf(
        "<strong>%s</strong><br/>%s: %.1f",
        mapdata$sa4_name_2016, # region name displayed in label
        indic_long,
        mapdata$value
      ) %>% # eco data displayed in label
        lapply(shiny::HTML),
      labelOptions = leaflet::labelOptions( # label options
        style = list(
          "font-weight" = "normal", # "bold" makes it so
          padding = "3px 8px"
        ),
        textsize = "12px", # text size of label
        noHide = FALSE, # TRUE makes labels permanently visible (messy)
        direction = "auto"
      ) # text box flips from side to side as needed
    ) %>%
    leaflet::addLegend(
      position = "topright", # options: topright, bottomleft etc.
      pal = pal, # colour palette as defined
      values = mapdata$value, # fill data
      bins = 3,
      labFormat = leaflet::labelFormat(transform = identity),
      title = label_title,
      opacity = 1,
    ) %>%
    # label opacity
    leaflet::addPolygons(
      data = metro_outline, #
      fill = F,
      stroke = T,
      opacity = 1,
      color = "black",
      weight = 1
    )

  # Display dynamic map: can zoom in, zoom out and hover over regions displaying distinct data----
  map
}

# Comparison of change in employment since Mar-20 in Greater Melbourne region and Rest of Victoria
title_reg_emp_regions_sincecovid_line <- function(data) {
  current <- data %>%
    dplyr::group_by(.data$series) %>%
    dplyr::mutate(
      value = 100 * ((.data$value / .data$value[date == as.Date("2020-03-01")]) - 1),
      tooltip = paste0(
        .data$gcc_restofstate, "\n",
        format(.data$date, "%b %Y"), "\n",
        round2(.data$value, 1), "%"
      )
    ) %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::ungroup() %>%
    dplyr::select(.data$gcc_restofstate, .data$value) %>%
    tidyr::spread(key = .data$gcc_restofstate, value = .data$value)

  diff <- current$`Greater Melbourne` - current$`Rest of Vic.`
  case_when(
    abs(diff) < 0.1 ~
    "Employment in Greater Melbourne has caught up with the rest of Victoria",
    sign(diff) == -1 ~
    "Employment in Greater Melbourne has not kept pace with the rest of Victoria",
    TRUE ~ "Employment in Greater Melbourne has grown faster than the rest of Victoria"
  )
}

viz_reg_emp_regions_sincecovid_line <- function(data = filter_dash_data(c(
                                                  "A84600141A",
                                                  "A84600075R"
                                                )) %>%
                                                  dplyr::group_by(.data$series_id) %>%
                                                  dplyr::mutate(value = slider::slide_mean(.data$value,
                                                    before = 2,
                                                    complete = T
                                                  )) %>%
                                                  dplyr::filter(.data$date >= as.Date("2020-01-01")),
                                                title = title_reg_emp_regions_sincecovid_line(data = data)) {
  df <- data %>%
    dplyr::group_by(.data$series) %>%
    dplyr::mutate(
      value = 100 * ((.data$value / .data$value[.data$date == as.Date("2020-03-01")]) - 1),
      tooltip = paste0(
        .data$gcc_restofstate, "\n",
        format(.data$date, "%b %Y"), "\n",
        round2(.data$value, 1), "%"
      )
    )

  df %>%
    djpr_ts_linechart(
      col_var = .data$gcc_restofstate,
      label_num = paste0(round2(.data$value, 1), "%"),
      y_labels = function(x) paste0(x, "%"),
      hline = 0
    ) +
    labs(
      title = title,
      subtitle = "Cumulative change in employment in Greater Melbourne and the rest of Victoria since March 2020",
      caption = paste0(caption_lfs_det_m(), " Data not seasonally adjusted. Smoothed using a 3 month rolling average.")
    )
}

viz_reg_unemp_emppop_partrate_multiline <- function(data = filter_dash_data(
                                                      c(
                                                        "A84600253V",
                                                        "A84599659L",
                                                        "A84600019W",
                                                        "A84600187J",
                                                        "A84599557X",
                                                        "A84600115W",
                                                        "A84599851L",
                                                        "A84599923L",
                                                        "A84600025T",
                                                        "A84600193C",
                                                        "A84599665J",
                                                        "A84600031L",
                                                        "A84599671C",
                                                        "A84599677T",
                                                        "A84599683L",
                                                        "A84599929A",
                                                        "A84600121T",
                                                        "A84600037A",
                                                        "A84599658K",
                                                        "A84599660W",
                                                        "A84600018V",
                                                        "A84600020F",
                                                        "A84600186F",
                                                        "A84600188K",
                                                        "A84599556W",
                                                        "A84599558A",
                                                        "A84600114V",
                                                        "A84600116X",
                                                        "A84599850K",
                                                        "A84599852R",
                                                        "A84599922K",
                                                        "A84599924R",
                                                        "A84600024R",
                                                        "A84600026V",
                                                        "A84600192A",
                                                        "A84600194F",
                                                        "A84599664F",
                                                        "A84599666K",
                                                        "A84600030K",
                                                        "A84600032R",
                                                        "A84599670A",
                                                        "A84599672F",
                                                        "A84599676R",
                                                        "A84599678V",
                                                        "A84599682K",
                                                        "A84599684R",
                                                        "A84599928X",
                                                        "A84599930K",
                                                        "A84600120R",
                                                        "A84600122V",
                                                        "A84600036X",
                                                        "A84600038C",
                                                        "A84600252T",
                                                        "A84600254W"
                                                      ),
                                                      df = dash_data
                                                    ),
                                                    selected_indicator = "unemp_rate",
                                                    dates = c(data_dates$`6202016`$min, data_dates$`6202016`$max),
                                                    n_col = 3) {
  indic_long <- dplyr::case_when(
    selected_indicator == "unemp_rate" ~ "Unemployment rate",
    selected_indicator == "part_rate" ~ "Participation rate",
    selected_indicator == "emp_pop" ~ "Employment to population ratio",
    TRUE ~ NA_character_
  )

  df <- data %>%
    dplyr::filter(date >= dates[1], date <= dates[2]) %>%
    mutate(indicator_short = dplyr::case_when(
      .data$indicator == "Unemployment rate" ~ "unemp_rate",
      .data$indicator == "Participation rate" ~ "part_rate",
      .data$indicator == "Employment to population ratio" ~ "emp_pop"
    ))

  # Reduce to selected_indicator
  df <- df %>%
    dplyr::filter(.data$indicator_short == selected_indicator) %>%
    dplyr::select(.data$series_id, .data$value, .data$date, .data$sa4)

  # 3 month smoothing
  df <- df %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2,
      complete = TRUE
    )) %>%
    dplyr::filter(!is.na(.data$value))

  df <- df %>%
    dplyr::mutate(
      tooltip = paste0(
        .data$sa4, "\n", format(.data$date, "%b %Y"),
        "\n", round2(.data$value, 1), "%"
      ),
      sa4 = gsub(" and South ", " & S. ", .data$sa4, fixed = TRUE)
    )

  max_y <- max(df$value) * 1.08
  mid_x <- stats::median(df$date)

  df <- df %>%
    dplyr::mutate(
      sa4 = dplyr::if_else(.data$sa4 == "", "Victoria", .data$sa4),
      is_vic = dplyr::if_else(.data$sa4 == "Victoria", TRUE, FALSE)
    )

  df <- df %>%
    dplyr::mutate(
      line_col =
        dplyr::case_when(
          .data$is_vic ~
          "Victoria",
          grepl("Melbourne|Mornington", .data$sa4) ~
          "Greater Melbourne",
          TRUE ~ "Rest of Victoria"
        )
    )

  vic <- df %>%
    filter(.data$sa4 == "Victoria") %>%
    select(-.data$sa4)

  facet_labels <- df %>%
    dplyr::group_by(.data$sa4, .data$is_vic, .data$line_col) %>%
    dplyr::summarise() %>%
    dplyr::mutate(
      x = .env$mid_x,
      y = .env$max_y
    )

  reg_sa4s <- sort(unique(df$sa4[df$line_col == "Rest of Victoria"]))
  melb_sa4s <- sort(unique(df$sa4[df$line_col == "Greater Melbourne"]))

  df$sa4 <- factor(df$sa4,
    levels = c("Victoria", reg_sa4s, melb_sa4s)
  )

  highest_current_ur <- df %>%
    dplyr::ungroup() %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::filter(.data$value == max(.data$value)) %>%
    dplyr::pull(.data$sa4)

  title <- paste0(
    highest_current_ur, " had the highest ", tolower(indic_long), " in Victoria in ",
    format(max(data$date), "%B %Y")
  )

  subtitle <- paste0(indic_long, " by region (SA4)")

  min_limit <- dplyr::if_else(
    selected_indicator == "unemp_rate",
    0,
    min(df$value)
  )

  lower_padding <- dplyr::if_else(
    min_limit == 0,
    0,
    0.05
  )

  df %>%
    djpr_ts_linechart(
      col_var = .data$line_col,
      label = F,
      dot = F
    ) +
    scale_colour_manual(
      values = c(
        "Victoria"          = djprtheme::djpr_cobalt,
        "Rest of Victoria"  = djprtheme::djpr_lima,
        "Greater Melbourne" = djprtheme::djpr_bondi_blue
      )
    ) +
    scale_y_continuous(
      labels = function(x) paste0(x, "%"),
      limits = c(min_limit, max_y),
      breaks = scales::breaks_pretty(n = 3),
      expand = expansion(mult = c(lower_padding, 0.1))
    ) +
    geom_text(
      data = facet_labels,
      aes(
        label = stringr::str_wrap(.data$sa4, 11),
        y = .data$y,
        x = .data$x
      ),
      lineheight = 0.85,
      size = 12 / .pt
    ) +
    geom_line(data = vic) +
    facet_wrap(~ factor(sa4), ncol = n_col, scales = "free_x") +
    scale_x_date(
      date_labels = "%Y",
      breaks = scales::breaks_pretty(n = 3)
    ) +
    theme(
      strip.text = element_blank(),
      panel.spacing = unit(1.5, "lines"),
      axis.text = element_text(size = 12)
    ) +
    labs(
      title = title,
      subtitle = subtitle,
      caption = paste0(caption_lfs_det_m(), " Data not seasonally adjusted. Smoothed using a 3 month rolling average.")
    )
}

viz_reg_unemp_emppop_partrate_bar <- function(data = data_reg_unemp_emppop_partrate_vic(),
                                              selected_indicator = "unemp_rate") {
  df <- data %>%
    dplyr::filter(.data$sa4 != "") %>%
    dplyr::mutate(sa4 = dplyr::if_else(grepl("Warrnambool", .data$sa4, fixed = TRUE),
      "Warrnambool & S. West",
      .data$sa4
    ))

  df %>%
    ggplot(aes(
      x = stats::reorder(.data$sa4, .data$value),
      y = .data$value
    )) +
    geom_col(
      col = "grey85",
      aes(fill = -.data$value)
    ) +
    geom_text(
      nudge_y = 0.1,
      aes(label = paste0(round2(.data$value, 1), "%")),
      colour = "black",
      hjust = 0,
      size = 12 / .pt
    ) +
    coord_flip(clip = "off") +
    scale_fill_distiller(palette = "Blues") +
    scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
    djprtheme::theme_djpr(flipped = TRUE) +
    theme(
      axis.title.x = element_blank(),
      panel.grid = element_blank(),
      axis.text.y = element_text(size = 12),
      axis.text.x = element_blank()
    ) +
    labs(title = "")
}

text_reg_regions_sincecovid <- function(data = filter_dash_data(c(
                                          "A84600141A",
                                          "A84600075R"
                                        ))) {
  emp_gcc_rest <- data %>%
    dplyr::group_by(.data$series_id) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value,
      before = 2,
      complete = TRUE
    )) %>%
    dplyr::filter(.data$date >= as.Date("2020-01-01")) %>%
    dplyr::ungroup()

  emp_gcc_rest <- emp_gcc_rest %>%
    dplyr::filter(.data$date %in% c(
      as.Date("2020-03-01"),
      as.Date("2020-10-01"),
      max(.data$date)
    )) %>%
    dplyr::select(.data$date, .data$value, .data$gcc_restofstate) %>%
    dplyr::group_by(.data$gcc_restofstate) %>%
    dplyr::mutate(
      d_sincecovid_abs = .data$value - .data$value[date == as.Date("2020-03-01")],
      d_sincecovid_perc = 100 * ((.data$value /
        .data$value[date == as.Date("2020-03-01")]) - 1),
      gcc_restofstate = dplyr::if_else(.data$gcc_restofstate == "Greater Melbourne",
        "melb", "rest"
      )
    )

  emp_gcc_rest <- emp_gcc_rest %>%
    split(emp_gcc_rest$gcc_restofstate)

  melb_emp_precovid <- emp_gcc_rest$melb %>%
    dplyr::filter(.data$date == min(.data$date)) %>%
    dplyr::pull(.data$value) / 1000


  melb_emp_oct20 <- emp_gcc_rest$melb %>%
    dplyr::filter(.data$date == as.Date("2020-10-01")) %>%
    dplyr::pull(.data$value) / 1000

  melb_emp_covid_to_oct_abs <- (1000 * (melb_emp_oct20 - melb_emp_precovid)) %>%
    round2(0)

  melb_emp_covid_to_oct_perc <- (100 * ((melb_emp_oct20 / melb_emp_precovid) - 1)) %>%
    round2(1)

  rest_emp_covid_to_oct_perc <- emp_gcc_rest$rest %>%
    dplyr::filter(.data$date == as.Date("2020-10-01")) %>%
    dplyr::pull(.data$d_sincecovid_perc) %>%
    round2(1)

  melb_emp_current <- (emp_gcc_rest$melb %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::pull(.data$d_sincecovid_perc)) %>%
    round2(1)

  latest_month <- max(emp_gcc_rest$melb$date) %>% format("%B %Y")

  rest_emp_current <- (emp_gcc_rest$rest %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::pull(.data$d_sincecovid_perc)) %>%
    round2(1)


  text_active(
    string = paste0(
      "In March 2020, before COVID-19 struck Victoria, there were XX million ",
      "people employed in Greater Melbourne. Employment fell to XX million ",
      "people by October 2020 - a decline of XX or XX per cent. Employment ",
      "in the rest of Victoria fell by only XX per cent over the same period. ",
      "Greater Melbourne employment is XX per cent XX pre-COVID-19 levels as at XX, ",
      "while employment in the rest of Victoria is XX per cent XX where it ",
      "was in March 2020."
    ),
    numbers = c(
      round2(melb_emp_precovid, 2),
      round2(melb_emp_oct20, 2),
      paste0(abs(melb_emp_covid_to_oct_abs), ",000"),
      abs(melb_emp_covid_to_oct_perc),
      abs(rest_emp_covid_to_oct_perc),
      abs(melb_emp_current),
      dplyr::if_else(melb_emp_current > 0, "above", "below"),
      latest_month,
      abs(rest_emp_current),
      dplyr::if_else(rest_emp_current > 0, "above", "below")
    )
  )
}

viz_reg_unemprate_dispersion <- function(data = filter_dash_data(
                                           c(
                                             "A84600253V",
                                             "A84599659L",
                                             "A84600019W",
                                             "A84600187J",
                                             "A84599557X",
                                             "A84600115W",
                                             "A84599851L",
                                             "A84599923L",
                                             "A84600025T",
                                             "A84600193C",
                                             "A84599665J",
                                             "A84600031L",
                                             "A84599671C",
                                             "A84599677T",
                                             "A84599683L",
                                             "A84599929A",
                                             "A84600121T",
                                             "A84600037A"
                                           ),
                                           df = dash_data
                                         ),
                                         selected_indicator = "all",
                                         dates = as.Date(c("1910-01-01", "2030-01-01"))) {
  df <- data %>%
    dplyr::filter(date >= dates[1], date <= dates[2]) %>%
    dplyr::mutate(
      sa4 = dplyr::if_else(.data$sa4 == "", "Victoria", .data$sa4)
    ) %>%
    dplyr::mutate(
      geog = dplyr::if_else(grepl("Melbourne|Mornington", .data$sa4),
        "Melbourne",
        .data$sa4
      )
    ) %>%
    dplyr::mutate(indicator_short = dplyr::case_when(
      .data$geog == "Victoria" ~ "vic",
      .data$geog == "Melbourne" ~ "metropolitan",
      TRUE ~ "regional"
    )) %>%
    dplyr::select(
      .data$date,
      .data$value,
      .data$indicator,
      .data$sa4,
      .data$indicator_short,
      .data$geog
    )

  df <- df %>%
    dplyr::mutate(sa4 = dplyr::if_else(grepl("Warrnambool", .data$sa4, fixed = TRUE),
      "Warrnambool & S. West",
      .data$sa4
    ))

  # Reduce df depending on selected_indicator
  if (selected_indicator == "metropolitan") {
    df <- df %>%
      dplyr::filter(.data$geog %in% c("Melbourne", "Victoria"))
  } else if (selected_indicator == "regional") {
    df <- df %>%
      dplyr::filter(!.data$geog %in% c("Melbourne"))
  }

  # 3 months smoothing
  df <- df %>%
    dplyr::group_by(.data$sa4) %>%
    dplyr::mutate(value = slider::slide_mean(.data$value, before = 2, complete = TRUE)) %>%
    dplyr::filter(!is.na(.data$value)) %>%
    dplyr::ungroup()

  df_summ <- df %>%
    dplyr::filter(!is.na(.data$value)) %>%
    dplyr::group_by(.data$date) %>%
    dplyr::summarise(
      vic = .data$value[.data$sa4 == "Victoria"],
      max_ur = max(.data$value),
      min_ur = min(.data$value)
    ) %>%
    dplyr::mutate(range = .data$max_ur - .data$min_ur)

  df_tidy <- df_summ %>%
    dplyr::select(-.data$range) %>%
    tidyr::pivot_longer(
      names_to = "series", values_to = "value",
      -.data$date
    )

  tooltip_ending <- dplyr::case_when(
    selected_indicator == "all" ~ "Victorian SA4 ",
    selected_indicator == "metropolitan" ~ "metro SA4 ",
    selected_indicator == "regional" ~ "regional SA4 "
  )

  df_tidy <- df_tidy %>%
    mutate(
      tooltip = case_when(
        .data$series == "vic" ~ "Victoria ",
        .data$series == "max_ur" ~ paste0("Highest ", tooltip_ending),
        .data$series == "min_ur" ~ paste0("Lowest ", tooltip_ending),
        TRUE ~ NA_character_
      ),
      tooltip = paste0(.data$tooltip, round2(.data$value, 1), "%")
    )

  days_in_data <- as.numeric(max(data$date) - min(data$date))

  subtitle_1 <- paste0(
    "Highest and lowest unemployment rates\nin ",
    selected_indicator, " Victorian SA4s"
  )

  # First plot: Show highest / lowest / state-wide unemp rates----
  plot_high_low <- df_tidy %>%
    ggplot(aes(x = .data$date)) +
    geom_ribbon(
      data = df_summ,
      aes(
        ymin = .data$min_ur,
        ymax = .data$max_ur
      ),
      colour = NA,
      alpha = 0.25
    ) +
    geom_line(
      aes(
        y = .data$value,
        color = .data$series
      )
    ) +
    ggiraph::geom_point_interactive(aes(
      tooltip = .data$tooltip,
      y = .data$value
    ),
    size = 3,
    colour = "white",
    alpha = 0.01
    ) +
    ggrepel::geom_label_repel(
      data = ~ filter(., .data$date == max(.data$date)),
      aes(
        label = stringr::str_wrap(.data$tooltip, 7),
        col = .data$series,
        y = .data$value
      ),
      label.size = NA,
      lineheight = 0.85,
      size = 12 / .pt,
      hjust = 0,
      min.segment.length = unit(10000, "lines"),
      nudge_x = days_in_data * 0.1,
      label.padding = 0.01,
      direction = "y"
    ) +
    djprtheme::theme_djpr() +
    djprtheme::djpr_y_continuous(
      limits = c(0, max(df_tidy$value)),
      labels = function(x) paste0(x, "%")
    ) +
    scale_x_date(
      expand = expansion(
        add = c(0, days_in_data * 0.065)
      ),
      breaks = djprtheme::breaks_right(
        limits = c(
          min(df_tidy$date),
          max(df_tidy$date)
        ),
        n_breaks = 4
      ),
      date_labels = "%b\n%Y"
    ) +
    scale_colour_manual(values = c(
      "min_ur" = djprtheme::djpr_lima,
      "max_ur" = djprtheme::djpr_cobalt,
      "vic" = "black"
    )) +
    theme(
      axis.title = element_blank(),
      axis.text = element_text(size = 12),
      plot.subtitle = element_text(size = 14)
    ) +
    labs(subtitle = subtitle_1)

  subtitle_2 <- paste0(
    "Range between highest and lowest\n(percentage points), ",
    selected_indicator, " SA4s"
  )

  # Second plot: Range between high and low -----
  plot_range <- df_summ %>%
    ggplot(aes(x = .data$date, y = .data$range)) +
    ggiraph::geom_col_interactive(aes(tooltip = paste0(
      format(.data$date, "%B %Y"),
      "\n", round2(.data$range, 1), " ppts"
    )),
    fill = djpr_pal(1),
    colour = NA,
    size = 0,
    alpha = 0.25
    ) +
    theme_djpr() +
    scale_x_date(
      date_labels = "%b\n%Y",
      breaks = djprtheme::breaks_right(c(
        min(df_summ$date),
        max(df_summ$date)
      ),
      n_breaks = 4
      )
    ) +
    djpr_y_continuous(
      limits = function(x) c(0, max(x)) # ,
      # labels = function(x) paste0(x, " ppts")
    ) +
    theme(
      axis.title = element_blank(),
      axis.text = element_text(size = 12),
      plot.subtitle = element_text(size = 14)
    ) +
    labs(subtitle = subtitle_2)

  # Create title -----

  current_range <- df_summ %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::pull(.data$range) %>%
    round2(1)

  plot_title <- paste0(
    "There was a ", current_range,
    " percentage point gap between the highest and lowest ",
    "unemployment rates in ",
    selected_indicator, " Victorian regions in ",
    format(max(df_tidy$date), "%B %Y")
  )

  subtitle <- paste0(
    "Gap between unemployment rates across Victorian regions: ",
    selected_indicator, " SA4s"
  )

  # Combine plots -----
  plots_combined <- patchwork::wrap_plots(plot_high_low,
    plot_range,
    patchwork::plot_spacer(),
    ncol = 2
  ) +
    coord_cartesian(clip = "off") +
    patchwork::plot_layout(heights = c(0.99, 0.01)) +
    patchwork::plot_annotation(
      title = plot_title,
      subtitle = subtitle,
      caption = paste0(caption_lfs_det_m(), " Data not seasonally adjusted. Smoothed using a 3 month rolling average."),
      theme = theme_djpr()
    )

  plots_combined
}

map_reg_sa4 <- function(sa4 = c(
                          "Melbourne - North East",
                          "Melbourne - Inner",
                          "Ballarat",
                          "Geelong",
                          "Hume",
                          "Latrobe - Gippsland",
                          "Melbourne - Outer East",
                          "Melbourne - South East",
                          "North West",
                          "Melbourne - Inner East",
                          "Melbourne - West",
                          "Bendigo",
                          "Warrnambool and South West",
                          "Melbourne - North West",
                          "Shepparton",
                          "Melbourne - Inner South",
                          "Mornington Peninsula"
                        )) {
  sa4 <- match.arg(sa4)

  all_areas <- sa42016 %>%
    dplyr::filter(.data$state_name_2016 == "Victoria") %>%
    dplyr::mutate(selected = dplyr::if_else(.data$sa4_name_2016 == .env$sa4, TRUE, FALSE))

  selected_area <- all_areas %>%
    dplyr::filter(.data$selected == TRUE)

  all_areas %>%
    ggplot() +
    geom_sf(aes(alpha = .data$selected),
      size = 0.25,
      fill = djprtheme::djpr_cobalt,
      colour = djprtheme::djpr_cool_grey_11
    ) +
    geom_curve(
      data = selected_area,
      aes(x = .data$cent_long, y = .data$cent_lat),
      xend = 147, yend = -35,
      curvature = 0.2,
      colour = "#1F1547"
    ) +
    geom_point(
      data = selected_area,
      aes(x = .data$cent_long, y = .data$cent_lat),
      colour = "#1F1547",
      size = 3,
      shape = "circle filled",
      stroke = 1.5,
      fill = "white"
    ) +
    annotate("label",
      x = 147,
      y = -34.5,
      label = stringr::str_wrap(sa4, 14),
      colour = "#1F1547",
      size = 24 / .pt,
      fontface = "bold",
      lineheight = 0.9,
      label.size = 0
    ) +
    scale_alpha_manual(values = c(
      `FALSE` = 0.2,
      `TRUE` = 0.8
    )) +
    theme_void() +
    theme(legend.position = "none")
}

viz_reg_sa4unemp_cf_broadregion <- function(data = filter_dash_data(
                                              c(
                                                "A84599659L",
                                                "A84600019W",
                                                "A84600187J",
                                                "A84599557X",
                                                "A84600115W",
                                                "A84599851L",
                                                "A84599923L",
                                                "A84600025T",
                                                "A84600193C",
                                                "A84599665J",
                                                "A84600031L",
                                                "A84599671C",
                                                "A84599677T",
                                                "A84599683L",
                                                "A84599929A",
                                                "A84600121T",
                                                "A84600037A",
                                                "A84595516F",
                                                "A84595471L"
                                              )
                                            ) %>%
                                              dplyr::group_by(.data$series_id) %>%
                                              dplyr::mutate(value = slider::slide_mean(.data$value, before = 2, complete = TRUE)) %>%
                                              dplyr::filter(.data$date >= max(.data$date) - (365.25 * 5)),
                                            sa4 = "Geelong") {
  in_melb <- grepl("Melbourne|Mornington", sa4)

  broad_region <- dplyr::if_else(in_melb,
    "Greater Melbourne",
    "Regional Victoria"
  )

  data <- data %>%
    mutate(sa4 = dplyr::if_else(.data$sa4 == "Victoria - North West",
      "North West",
      .data$sa4
    ))

  sa4_df <- data %>%
    dplyr::filter(.data$sa4 == .env$sa4) %>%
    mutate(col_var = .data$sa4)

  current_sa4_ur <- sa4_df %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::pull(.data$value) %>%
    round2(1)

  comparator_id <- dplyr::if_else(in_melb,
    "A84595516F",
    "A84595471L"
  )

  comparator_df <- data %>%
    dplyr::filter(
      .data$series_id == comparator_id,
      .data$date >= min(sa4_df$date)
    ) %>%
    mutate(col_var = broad_region)

  current_comp_ur <- comparator_df %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::pull(.data$value) %>%
    round2(1)

  sa4_cf_comp <- dplyr::case_when(
    current_sa4_ur > current_comp_ur ~ "higher",
    current_sa4_ur < current_comp_ur ~ "lower",
    current_sa4_ur == current_comp_ur ~ "the same",
    TRUE ~ NA_character_
  )

  comb <- dplyr::bind_rows(
    comparator_df,
    sa4_df
  )

  colours <- c(
    djprtheme::djpr_cobalt,
    djprtheme::djpr_lima
  )

  names(colours) <- c(
    "Regional Victoria",
    sa4
  )

  comb %>%
    djpr_ts_linechart(
      col_var = .data$col_var,
      label_num = paste0(round2(.data$value, 1), "%")
    ) +
    scale_colour_manual(values = colours) +
    scale_y_continuous(
      limits = function(limits) c(0, limits[2]),
      labels = function(x) paste0(x, "%"),
      breaks = scales::breaks_pretty(5),
      expand = expansion(mult = c(0, 0.15))
    ) +
    labs(
      title = paste0(
        "The unemployment rate is ",
        sa4_cf_comp,
        " in ",
        sa4,
        dplyr::if_else(sa4_cf_comp == "the same", " and ", " than "),
        "in ",
        broad_region,
        " as a whole"
      ),
      subtitle = paste0(
        "Unemployment rate in ",
        sa4,
        " and ",
        broad_region
      )
    )
}

table_region_focus <- function(data = filter_dash_data(
                                 c(
                                   "A84600141A",
                                   "A84600144J",
                                   "A84600145K",
                                   "A84599655C",
                                   "A84599658K",
                                   "A84599659L",
                                   "A84600015L",
                                   "A84600018V",
                                   "A84600019W",
                                   "A84600183X",
                                   "A84600186F",
                                   "A84600187J",
                                   "A84599553R",
                                   "A84599556W",
                                   "A84599557X",
                                   "A84600111L",
                                   "A84600114V",
                                   "A84600115W",
                                   "A84599847W",
                                   "A84599850K",
                                   "A84599851L",
                                   "A84599919W",
                                   "A84599922K",
                                   "A84599923L",
                                   "A84600021J",
                                   "A84600024R",
                                   "A84600025T",
                                   "A84600189L",
                                   "A84600192A",
                                   "A84600193C",
                                   "A84600075R",
                                   "A84600078W",
                                   "A84600079X",
                                   "A84599661X",
                                   "A84599664F",
                                   "A84599665J",
                                   "A84600027W",
                                   "A84600030K",
                                   "A84600031L",
                                   "A84599667L",
                                   "A84599670A",
                                   "A84599671C",
                                   "A84599673J",
                                   "A84599676R",
                                   "A84599677T",
                                   "A84599679W",
                                   "A84599682K",
                                   "A84599683L",
                                   "A84599925T",
                                   "A84599928X",
                                   "A84599929A",
                                   "A84600117A",
                                   "A84600120R",
                                   "A84600121T",
                                   "A84600033T",
                                   "A84600036X",
                                   "A84600037A",
                                   "A84599660W",
                                   "A84600020F",
                                   "A84600188K",
                                   "A84599558A",
                                   "A84600116X",
                                   "A84599852R",
                                   "A84599924R",
                                   "A84600026V",
                                   "A84600194F",
                                   "A84599666K",
                                   "A84600032R",
                                   "A84599672F",
                                   "A84599678V",
                                   "A84599684R",
                                   "A84599930K",
                                   "A84600122V",
                                   "A84600038C",
                                   "A84600080J",
                                   "A84600146L"
                                 )
                               ) %>%
                                 dplyr::group_by(.data$series_id) %>%
                                 dplyr::mutate(value = slider::slide_mean(.data$value, before = 2, complete = TRUE)),
                               sa4 = "Geelong") {
  in_melb <- grepl("Melbourne|Mornington", sa4)

  broad_region <- dplyr::if_else(in_melb,
    "Greater Melbourne",
    "Regional Victoria"
  )

  latest_date <- format(max(data$date), "%b %Y")

  df <- data %>%
    dplyr::mutate(sa4 = dplyr::if_else(.data$sa4 == "Victoria - North West",
      "North West",
      .data$sa4
    ))

  df <- df %>%
    dplyr::mutate(gcc_restofstate = dplyr::if_else(.data$gcc_restofstate ==
      "Rest of Vic.",
    "Regional Victoria",
    .data$gcc_restofstate
    )) %>%
    dplyr::mutate(geog = dplyr::if_else(.data$sa4 != "",
      .data$sa4,
      .data$gcc_restofstate
    )) %>%
    dplyr::filter(.data$geog %in% c(.env$broad_region, .env$sa4))

  table_df <- df %>%
    dplyr::group_by(.data$geog, .data$indicator) %>%
    dplyr::mutate(
      d_month = dplyr::if_else(.data$indicator == "Employed total",
        100 * ((.data$value / dplyr::lag(.data$value, 1)) - 1),
        .data$value - dplyr::lag(.data$value, 1)
      ),
      d_year = dplyr::if_else(.data$indicator == "Employed total",
        100 * ((.data$value / dplyr::lag(.data$value, 12)) - 1),
        .data$value - dplyr::lag(.data$value, 12)
      )
    ) %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    dplyr::select(
      .data$indicator, .data$value, .data$geog,
      .data$d_month, .data$d_year
    ) %>%
    dplyr::ungroup()

  table_df <- table_df %>%
    dplyr::mutate(across(
      c(.data$value, .data$d_month, .data$d_year),
      ~ round2(.x, 1)
    )) %>%
    dplyr::mutate(
      value = dplyr::if_else(.data$indicator == "Employed total",
        scales::comma(.data$value * 1000),
        paste0(.data$value, "%")
      ),
      d_month = dplyr::if_else(.data$indicator == "Employed total",
        paste0(.data$d_month, "%"),
        paste0(.data$d_month, " ppts")
      ),
      d_year = dplyr::if_else(.data$indicator == "Employed total",
        paste0(.data$d_year, "%"),
        paste0(.data$d_year, " ppts")
      )
    )

  table_df <- table_df %>%
    dplyr::rename({{ latest_date }} := .data$value,
      `Change over month` = .data$d_month,
      `Change over year` = .data$d_year
    )

  table_df <- table_df %>%
    tidyr::gather(
      key = "series", value = "value",
      -.data$indicator, -.data$geog
    ) %>%
    tidyr::spread(key = .data$geog, value = .data$value)

  table_df <- table_df %>%
    dplyr::group_by(.data$indicator) %>%
    mutate(order = dplyr::case_when(
      series == "Change over month" ~ 2,
      series == "Change over year" ~ 3,
      TRUE ~ 1
    )) %>%
    dplyr::arrange(desc(.data$indicator), .data$order) %>%
    dplyr::select(-.data$order)

  col_header_style <- list(
    `font-weight` = "600"
  )

  table_df <- table_df %>%
    dplyr::select(.data$indicator, .data$series, {{ sa4 }}, dplyr::everything())

  out <- table_df %>%
    dplyr::group_by(.data$indicator) %>%
    dplyr::mutate(indicator = dplyr::if_else(
      dplyr::row_number() != 1,
      "",
      .data$indicator
    )) %>%
    dplyr::rename(
      ` ` = .data$indicator,
      `  ` = .data$series
    ) %>%
    flextable::flextable() %>%
    flextable::bold(part = "header") %>%
    flextable::border_remove() %>%
    flextable::border(
      part = "body",
      j = 2:4,
      i = 2:nrow(table_df),
      border.top = flextable::fp_border_default(color = "grey90", width = 0.25)
    ) %>%
    flextable::border(
      part = "body",
      i = c(1, 4, 7),
      border.top = flextable::fp_border_default()
    ) %>%
    flextable::border(
      part = "body",
      i = nrow(table_df),
      border.bottom = flextable::fp_border_default()
    ) %>%
    # flextable::autofit(add_w = 0, add_h = 0) %>%
    flextable::set_table_properties("autofit", width = 1) %>%
    flextable::font(part = "body", fontname = "VIC-regular") %>%
    flextable::font(part = "header", fontname = "VIC-regular") %>%
    flextable::fontsize(size = 9) %>%
    flextable::fontsize(size = 9, part = "header")

  table_caption <- caption_auto(
    data = data,
    notes = "Data not seasonally adjusted; smoothed using a 3 month rolling average."
  )
  # Add caption
  out <- out %>%
    flextable::add_footer(` ` = table_caption) %>%
    flextable::merge_at(
      j = 1:flextable::ncol_keys(out),
      part = "footer"
    ) %>%
    flextable::font(fontname = "VIC-regular") %>%
    flextable::fontsize(
      size = 11 * 0.85,
      part = "footer"
    ) %>%
    flextable::color(
      part = "footer",
      color = "#343a40"
    ) %>%
    flextable::line_spacing(
      part = "footer",
      space = 0.8
    ) %>%
    flextable::font(
      fontname = "VIC-regular",
      part = "footer"
    )

  out
}

viz_reg_melvic_line <- function(data = filter_dash_data(
                                  c(
                                    "A84600144J",
                                    "A84600078W",
                                    "A84595516F",
                                    "A84595471L"
                                  ),
                                  df = dash_data
                                ) %>%
                                  dplyr::group_by(.data$series_id) %>%
                                  dplyr::mutate(value = slider::slide_mean(.data$value, before = 2, complete = TRUE)) %>%
                                  dplyr::filter(!is.na(.data$value)),
                                dates = as.Date(c("1910-01-01", "2030-01-01"))) {
  latest <- data %>%
    dplyr::ungroup() %>%
    dplyr::filter(
      .data$date == max(.data$date),
      .data$indicator == "Unemployment rate"
    ) %>%
    dplyr::select(.data$value, .data$gcc_restofstate) %>%
    dplyr::mutate(value = paste0(round2(.data$value, 1), " per cent")) %>%
    tidyr::spread(key = .data$gcc_restofstate, value = .data$value)


  title <- paste0(
    "The unemployment rate in Greater Melbourne was ",
    latest$`Greater Melbourne`,
    " while the rate in the rest of Victoria was ",
    latest$`Rest of Vic.`,
    " in ",
    format(max(data$date), "%B %Y")
  )

  data <- data %>%
    mutate(
      gcc_restofstate = gsub("Melbourne", "Melb", .data$gcc_restofstate,
        fixed = TRUE
      )
    ) %>%
    filter(date >= dates[1], date <= dates[2])

  max_date <- data %>%
    dplyr::filter(.data$date == max(.data$date)) %>%
    mutate(label = paste0(
      stringr::str_wrap(.data$gcc_restofstate, 9),
      "\n",
      round2(.data$value, 1), "%"
    ))

  days_in_data <- as.numeric(max(data$date) - min(data$date))

  data %>%
    dplyr::mutate(
      tooltip = paste0(
        .data$gcc_restofstate, "\n",
        format(.data$date, "%b %Y"),
        "\n",
        round2(.data$value, 1), "%"
      )
    ) %>%
    djpr_ts_linechart(
      col_var = .data$gcc_restofstate,
      label_num = paste0(round2(.data$value, 1), "%"),
      y_labels = function(x) paste0(x, "%")
    ) +
    facet_wrap(~indicator, scales = "free_y") +
    theme(
      axis.title = element_blank(),
      panel.spacing = unit(1.5, "lines")
    ) +
    labs(
      title = title,
      subtitle = "Employment to population ratio and unemployment rate in Greater Melbourne and the rest of Victoria",
      caption = paste0(caption_lfs_det_m(), " Data not seasonally adjusted. Smoothed using a 3 month rolling average.")
    )
}