aemet_forecast_daily {climaemet}

Forecast database by municipality

Description

Get a database of daily or hourly weather forecasts for a given municipality.

Usage

aemet_forecast_daily(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_forecast_hourly(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

x

A vector of municipality codes to extract. For convenience, climaemet provides this data on the dataset aemet_munic (see municipio field) as of January 2024.

verbose

Logical TRUE/FALSE. Provides information about the flow of information between the client and server.

extract_metadata

Logical TRUE/FALSE. On TRUE the output is a tibble with the description of the fields. See also get_metadata_aemet().

progress

Logical, display a cli::cli_progress_bar() object. If verbose = TRUE won't be displayed.

Details

Forecasts format provided by the AEMET API have a complex structure. Although climaemet returns a tibble, each forecasted value is provided as a nested tibble. aemet_forecast_tidy() helper function can unnest these values an provide a single unnested tibble for the requested variable.

If extract_metadata = TRUE a simple tibble describing the value of each field of the forecast is returned.

Value

A nested tibble. Forecasted values can be extracted with aemet_forecast_tidy(). See also Details.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_munic for municipality codes and mapSpain package for working with sf objects of municipalities (see mapSpain::esp_get_munic() and Examples).

Other aemet_api_data: aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Other forecasts: aemet_forecast_beaches(), aemet_forecast_tidy()

Examples

# Select a city
data("aemet_munic")
library(dplyr)
munis <- aemet_munic %>%
  filter(municipio_nombre %in% c("Santiago de Compostela", "Lugo")) %>%
  pull(municipio)

daily <- aemet_forecast_daily(munis)

# Metadata
meta <- aemet_forecast_daily(munis, extract_metadata = TRUE)
glimpse(meta$campos)

# Vars available
aemet_forecast_vars_available(daily)


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)

# Select and unnest
daily_temp <- aemet_forecast_tidy(daily, "temperatura")

# This is not
daily_temp

# Wrangle and plot
daily_temp_end <- daily_temp %>%
  select(
    elaborado, fecha, municipio, nombre, temperatura_minima,
    temperatura_maxima
  ) %>%
  tidyr::pivot_longer(cols = contains("temperatura"))

# Plot
library(ggplot2)
ggplot(daily_temp_end) +
  geom_line(aes(fecha, value, color = name)) +
  facet_wrap(~nombre, ncol = 1) +
  scale_color_manual(
    values = c("red", "blue"),
    labels = c("max", "min")
  ) +
  scale_x_date(
    labels = scales::label_date_short(),
    breaks = "day"
  ) +
  scale_y_continuous(
    labels = scales::label_comma(suffix = "º")
  ) +
  theme_minimal() +
  labs(
    x = "", y = "",
    color = "",
    title = "Forecast: 7-day temperature",
    subtitle = paste(
      "Forecast produced on",
      format(daily_temp_end$elaborado[1], usetz = TRUE)
    )
  )

# Spatial with mapSpain
library(mapSpain)
library(sf)

lugo_sf <- esp_get_munic(munic = "Lugo") %>%
  select(LAU_CODE)

daily_temp_end_lugo_sf <- daily_temp_end %>%
  filter(nombre == "Lugo" & name == "temperatura_maxima") %>%
  # Join by LAU_CODE
  left_join(lugo_sf, by = c("municipio" = "LAU_CODE")) %>%
  st_as_sf()

ggplot(daily_temp_end_lugo_sf) +
  geom_sf(aes(fill = value)) +
  facet_wrap(~fecha) +
  scale_fill_gradientn(
    colors = c("blue", "red"),
    guide = guide_legend()
  ) +
  labs(
    main = "Forecast: 7-day max temperature",
    subtitle = "Lugo, ES"
  )