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Forecast database by municipality

Source: R/aemet_forecast_daily.R, R/aemet_forecast_hourly.R
aemet_forecast.Rd

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.

Value

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

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.

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)
#> Rows: 23
#> Columns: 5
#> $ id          <chr> "id", "version", "elaborado", "nombre", "provincia", "fech…
#> $ descripcion <chr> "Indicativo de municipio", "Versión", "Fecha de elaboració…
#> $ tipo_datos  <chr> "string", "float", "dataTime", "string", "string", "date",…
#> $ requerido   <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, …
#> $ unidad      <chr> NA, NA, NA, NA, NA, NA, "Tanto por ciento (%)", "metros (m…

# Vars available
aemet_forecast_vars_available(daily)
#> [1] "probPrecipitacion" "cotaNieveProv"     "estadoCielo"      
#> [4] "viento"            "rachaMax"          "temperatura"      
#> [7] "sensTermica"       "humedadRelativa"  


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)
#> # A tibble: 14 × 4
#>    municipio fecha      nombre                 temperatura$maxima $minima $dato 
#>    <chr>     <date>     <chr>                               <int>   <int> <list>
#>  1 15078     2024-07-17 Santiago de Compostela                 26      13 <df>  
#>  2 15078     2024-07-18 Santiago de Compostela                 27      14 <df>  
#>  3 15078     2024-07-19 Santiago de Compostela                 28      14 <df>  
#>  4 15078     2024-07-20 Santiago de Compostela                 24      17 <df>  
#>  5 15078     2024-07-21 Santiago de Compostela                 23      15 <df>  
#>  6 15078     2024-07-22 Santiago de Compostela                 27      14 <df>  
#>  7 15078     2024-07-23 Santiago de Compostela                 27      16 <df>  
#>  8 27028     2024-07-17 Lugo                                   29      13 <df>  
#>  9 27028     2024-07-18 Lugo                                   29      14 <df>  
#> 10 27028     2024-07-19 Lugo                                   30      16 <df>  
#> 11 27028     2024-07-20 Lugo                                   26      16 <df>  
#> 12 27028     2024-07-21 Lugo                                   22      14 <df>  
#> 13 27028     2024-07-22 Lugo                                   28      13 <df>  
#> 14 27028     2024-07-23 Lugo                                   25      16 <df>  

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

# This is not
daily_temp
#> # A tibble: 14 × 14
#>    elaborado           municipio nombre provincia id    version uvMax fecha     
#>    <dttm>              <chr>     <chr>  <chr>     <chr>   <dbl> <int> <date>    
#>  1 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1     9 2024-07-17
#>  2 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1     8 2024-07-18
#>  3 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1     8 2024-07-19
#>  4 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1     9 2024-07-20
#>  5 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1     9 2024-07-21
#>  6 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1    NA 2024-07-22
#>  7 2024-07-17 12:33:08 15078     Santi… A Coruña  15078       1    NA 2024-07-23
#>  8 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1     8 2024-07-17
#>  9 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1     8 2024-07-18
#> 10 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1     8 2024-07-19
#> 11 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1     9 2024-07-20
#> 12 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1     9 2024-07-21
#> 13 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1    NA 2024-07-22
#> 14 2024-07-17 12:33:08 27028     Lugo   Lugo      27028       1    NA 2024-07-23
#> # ℹ 6 more variables: temperatura_maxima <int>, temperatura_minima <int>,
#> #   temperatura_6 <int>, temperatura_12 <int>, temperatura_18 <int>,
#> #   temperatura_24 <int>

# 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)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

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"
  )