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eLTER_eu_networks <- readr::read_csv("./data/eLTER_eu_networks.csv", show_col_types = FALSE) %>%
  tibble::as_tibble()

related_resources <- purrr::map_dfr(
  as.list(
    eLTER_eu_networks$DEIMS.iD
  ),
  function (x) {
    ReLTER::get_network_related_resources(x) %>%
      dplyr::filter(!is.na(uri)) %>%
      dplyr::mutate(
        networkID = x,
        type = stringr::str_replace(uri, "https://deims.org/(.*)/.*","\\1")
      )
  }
) %>%
  dplyr::inner_join(eLTER_eu_networks, by = c("networkID" = "DEIMS.iD")) %>%
  dplyr::select(
    title = relatedResourcesTitle,
    uri,
    lastChanged = relatedResourcesChanged,
    networkID,
    type,
    network = eLTER_EU_Networks,
    country
  )
library(dplyr)
tbl_resources <- related_resources %>%
  dplyr::count(country, type) %>%
  tidyr::pivot_wider(names_from = type, values_from = n, values_fill = 0)

knitr::kable(
  tbl_resources,
  caption = "eLTER EU networks related resources"
)
eLTER EU networks related resources
country activity dataset sensors
Austria 36 172 36
Belgium 1 7 0
Bulgaria 0 17 0
Denmark 0 2 0
Finland 0 18 4
France 0 2 0
Germany 1 95 0
Greece 0 3 0
Hungary 0 19 0
Israel 0 18 0
Italy 11 223 20
Latvia 0 14 0
Lithuania 0 2 0
Netherlands 0 2 0
Norway 0 2 0
Poland 0 11 0
Portugal 0 44 0
Romania 7 8 0
Serbia 0 4 0
Slovakia 0 6 0
Slovenia 0 3 0
Spain 15 56 0
Sweden 0 21 0
Switzerland 0 18 0
United Kingdom 0 243 0
# Get the world map
worldMap <- rworldmap::getMap()

library(rnaturalearth)
world_map <- rnaturalearth::ne_countries(scale = 50, returnclass = 'sf')
europe_map <- world_map %>%
  filter(name %in% related_resources$country)

elter_map <- europe_map %>%
  dplyr::select(
    name,
    geometry
  ) %>%
  dplyr::left_join(tbl_resources, by = c("name" = "country"))

map_datasets <- ggplot2::ggplot() +
  ggplot2::geom_sf(data = elter_map, ggplot2::aes(fill = dataset)) +
  ggplot2::coord_sf(xlim = c(-20, 45), ylim = c(28, 73), expand = FALSE)
map_activities <- ggplot2::ggplot() +
  ggplot2::geom_sf(data = elter_map, ggplot2::aes(fill = activity)) +
  ggplot2::coord_sf(xlim = c(-20, 45), ylim = c(28, 73), expand = FALSE)
map_sensors <- ggplot2::ggplot() +
  ggplot2::geom_sf(data = elter_map, ggplot2::aes(fill = sensors)) +
  ggplot2::coord_sf(xlim = c(-20, 45), ylim = c(28, 73), expand = FALSE)

gridExtra::grid.arrange(
  map_datasets,
  map_activities,
  map_sensors,
  nrow = 1,
  top = "Distribution of resources in eLTER countries"#,
  # bottom = grid::textGrob(
  #   "this footnote is right-justified",
  #   gp = grid::gpar(fontface = 3, fontsize = 9),
  #   hjust = 1,
  #   x = 1
  # )
)

dataset_uploaded <- related_resources %>%
  dplyr::arrange(lastChanged) %>%
  dplyr::mutate(
    lastChanged = as.Date(
        lastChanged
    ),
    value = 1
  ) %>%
  tidyr::complete(
    lastChanged = tidyr::full_seq(
      as.Date(lastChanged),
      period = 1
    ), 
    fill = list(value = 0)
  ) %>%
  dplyr::group_by(lastChanged) %>%
  dplyr::summarise(frequency = n()) %>%
  dplyr::mutate(
    cumsum = cumsum(frequency)
  )

fig <- 
  ggplot2::ggplot(dataset_uploaded, ggplot2::aes(x = lastChanged, y = cumsum)) +
  ggplot2::geom_line(color = "#00AFBB", size = 1) +
  ggplot2::scale_x_date(date_labels = "%b-%Y") +
  ggplot2::theme_minimal()

fig