Tutorial

gwl <- stations$overview_list$groundwater.level %>% 
  dplyr::mutate(Datum = as.Date(Datum, format = "%d.%m.%Y"))

text_low_levels <- c("extrem niedrig", "sehr niedrig", "niedrig")
text_high_levels <- c("hoch", "sehr hoch", "extrem hoch")
levels_ordered <- c(text_low_levels, "normal", text_high_levels, "keine")

gwl$Klassifikation <- forcats::fct_relevel(gwl$Klassifikation, levels_ordered)

gwl_classified_only <- gwl %>% 
  dplyr::filter(Klassifikation != "keine")

percental_share_low_levels <- rounded_percentage(
  sum(gwl_classified_only$Klassifikation %in% text_low_levels), 
  basis = nrow(gwl_classified_only)
)

percental_share_high_levels <- rounded_percentage(
  sum(gwl_classified_only$Klassifikation %in% text_high_levels), 
  basis = nrow(gwl_classified_only)
)

title_text <- sprintf(
  "GW level classification (n = %d out of %d have 'classification' data)",
  nrow(gwl_classified_only), 
  nrow(gwl)
)

g1 <- gwl_classified_only %>% 
  dplyr::count(Klassifikation, Grundwasserspannung) %>% 
  dplyr::mutate(percental_share = kwb.utils::percentage(n, nrow(gwl))) %>% 
  ggplot2::ggplot(ggplot2::aes(
    x = Klassifikation,
    y = percental_share,
    fill = Grundwasserspannung
  )) +
  ggplot2::geom_bar(stat = "identity") + 
  ggplot2::labs(
    title = title_text,
    x = "Classification",
    y = "Percental share (%)"
  ) +
  ggplot2::theme_bw()

plotly::ggplotly(g1)

level_colors <- data.frame(
  Klassifikation = levels_ordered, 
  classi_color = c(
    "darkred", 
    "red", 
    "orange", 
    "green", 
    "lightblue", 
    "blue", 
    "darkblue", 
    "grey"
  )
)

rechtswert <- "Rechtswert_UTM_33_N"
hochwert <- "Hochwert_UTM_33_N"

gwl_classified_only_with_coords <- gwl_classified_only %>% 
  dplyr::mutate(
    Messstellennummer = as.character(Messstellennummer),
  ) %>% 
  dplyr::inner_join(
    stations_gwl_master %>%
      tibble::as_tibble() %>% 
      dplyr::select(dplyr::all_of(c("Nummer", rechtswert, hochwert))) %>% 
      dplyr::rename(Messstellennummer = "Nummer"),
    by = "Messstellennummer"
  ) %>% 
  dplyr::left_join(
    level_colors, 
    by = "Klassifikation"
  ) %>% 
  sf::st_as_sf(
    coords = c(rechtswert, hochwert), 
    crs = 25833
  ) %>% 
  sf::st_transform(crs = 4326)

if(nrow(gwl_classified_only_with_coords) > 0) {

# Create a vector of labels for each row in gwl_classified_only_with_coords
labs <- wasserportal::columns_to_labels(
  data = gwl_classified_only_with_coords, 
  columns = c(
    "Messstellennummer", 
    "Grundwasserspannung", 
    "Klassifikation", 
    "Datum"
  ),
  fmt = "<p>%s: %s</p>",
  sep = ""
)

# Print Map
gwlmap <- gwl_classified_only_with_coords %>% 
  leaflet::leaflet() %>%
  leaflet::addTiles() %>% 
  leaflet::addProviderTiles(leaflet::providers$CartoDB.Positron) %>%
  leaflet::addCircles(
    color = ~classi_color,
    label = lapply(labs, htmltools::HTML)
  ) %>% 
  leaflet::addLegend(
    position = "topright",
    colors = level_colors$classi_color,
    labels = level_colors$Klassifikation,
    title = sprintf(
      "Classification (latest data: %s)",
      max(gwl_classified_only_with_coords$Datum)
    )
  )

htmlwidgets::saveWidget(
  gwlmap, 
  "./map_gwl-trend.html", 
  title = "GW level trend"
)

gwlmap
}