Package 'climaemet'

AEMET alert zones

Description

Retrieves the AEMET geographical zones used for meteorological alerts.

Usage

aemet_alert_zones(verbose = FALSE, return_sf = FALSE)

Arguments

verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.

Value

A tibble or a sf object.

Caching

The first result retrieved in each session is temporarily cached in tempdir() to avoid unnecessary requests.

Source

https://www.aemet.es/es/eltiempo/prediccion/avisos/ayuda. See also Annex 2 and Annex 3 documents, linked from that page.

See Also

Weather alerts: aemet_alerts()

AEMET locations: aemet_beaches(), aemet_stations()

Examples

library(dplyr)
alert_zones <- aemet_alert_zones()
alert_zones

# Cached during this R session.
alert_zones2 <- aemet_alert_zones(verbose = TRUE)

identical(alert_zones, alert_zones2)

# Select and map alert zones.
library(ggplot2)

# Galicia.
alert_zones_sf <- aemet_alert_zones(return_sf = TRUE) |>
  filter(COD_CCAA == "71")

# Coast zones have codes ending in "C".
alert_zones_sf$type <- ifelse(grepl("C$", alert_zones_sf$COD_Z),
  "Coast", "Mainland"
)

ggplot(alert_zones_sf) +
  geom_sf(aes(fill = NOM_PROV)) +
  facet_wrap(~type) +
  scale_fill_brewer(palette = "Blues")

---

AEMET meteorological alerts

Description

Retrieves current meteorological alerts issued by AEMET.

Usage

aemet_alerts(
  ccaa = NULL,
  lang = c("es", "en"),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

ccaa	A character vector of autonomous community names or NULL to retrieve all autonomous communities.
lang	The language of the results, either "es" (Spanish) or "en" (English).
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Source

https://www.aemet.es/en/eltiempo/prediccion/avisos and https://www.aemet.es/es/eltiempo/prediccion/avisos/ayuda for API status and alerts reference, including Annex 2 and Annex 3 documentation.

See Also

See mapSpain::esp_codelist and mapSpain::esp_dict_region_code() for autonomous community names.

Weather alerts: aemet_alert_zones()

Weather observations: aemet_last_obs()

Examples

# Display CCAA names.
library(dplyr)
aemet_alert_zones() |>
  select(NOM_CCAA) |>
  distinct()

# Base map.
cbasemap <- mapSpain::esp_get_ccaa(ccaa = c(
  "Galicia", "Asturias", "Cantabria",
  "Euskadi"
))

# Alerts.
alerts_north <- aemet_alerts(
  ccaa = c("Galicia", "Asturias", "Cantabria", "Euskadi"),
  return_sf = TRUE
)

# Plot if there are alerts.
if (inherits(alerts_north, "sf")) {
  library(ggplot2)
  library(lubridate)

  alerts_north$day <- date(alerts_north$effective)

  ggplot(alerts_north) +
    geom_sf(data = cbasemap, fill = "grey60") +
    geom_sf(aes(fill = `AEMET-Meteoalerta nivel`)) +
    geom_sf(
      data = cbasemap, fill = "transparent", color = "black",
      linewidth = 0.5
    ) +
    facet_grid(vars(`AEMET-Meteoalerta fenomeno`), vars(day)) +
    scale_fill_manual(values = c(
      "amarillo" = "yellow", naranja = "orange",
      "rojo" = "red"
    ))
}

---

Install an AEMET OpenData API key

Description

Stores an AEMET OpenData API key on your local machine so it can be used without including it in your code.

Usage

aemet_api_key(apikey, overwrite = FALSE, install = FALSE)

Arguments

apikey	A character vector of AEMET OpenData API keys. Acquire a key at https://opendata.aemet.es/centrodedescargas/inicio. You can install multiple API keys at once. See Details.
overwrite	A logical value. If TRUE, overwrites an existing AEMET_API_KEY environment variable.
install	A logical value. If TRUE, installs the key on your local machine for use in future sessions. Defaults to FALSE.

Details

Alternatively, set the key for the current session with Sys.setenv(AEMET_API_KEY = "Your_Key"), equivalent to install = FALSE. To store it permanently, add AEMET_API_KEY = "Your_Key" to .Renviron, equivalent to install = TRUE.

You can pass multiple apikey values as a character vector, such as c(api1, api2). In this case, multiple AEMET_API_KEY values are stored. In each subsequent API call, climaemet chooses the API key with the highest remaining quota.

This is useful when performing batch queries to avoid API throttling.

Value

NULL, invisibly.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

To locate the stored API key, run tools::R_user_dir("climaemet", "config").

See Also

AEMET OpenData API authentication: aemet_detect_api_key()

Examples

# Do not run these examples.

if (FALSE) {
  aemet_api_key("111111abc", install = TRUE)

  # Check it with:
  Sys.getenv("AEMET_API_KEY")
}

if (FALSE) {
  # Overwrite an existing key:
  aemet_api_key("222222abc", overwrite = TRUE, install = TRUE)

  # Check it with:
  Sys.getenv("AEMET_API_KEY")
}

---

AEMET beaches

Description

Retrieves the beaches available from the AEMET OpenData API.

Usage

aemet_beaches(verbose = FALSE, return_sf = FALSE)

Arguments

verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.

Value

A tibble or a sf object.

Caching

The first result retrieved in each session is temporarily cached in tempdir() to avoid unnecessary requests.

See Also

aemet_forecast_beaches().

AEMET locations: aemet_alert_zones(), aemet_stations()

Examples

library(dplyr)
beaches <- aemet_beaches()
beaches

# Cached during this R session.
beaches2 <- aemet_beaches(verbose = TRUE)

identical(beaches, beaches2)

# Select and map beaches.
library(ggplot2)
library(mapSpain)

# Alicante / Alacant.
beaches_sf <- aemet_beaches(return_sf = TRUE) |>
  filter(ID_PROVINCIA == "03")

prov <- mapSpain::esp_get_prov("Alicante")

ggplot(prov) +
  geom_sf() +
  geom_sf(
    data = beaches_sf, shape = 4, size = 2.5,
    color = "blue"
  )

---

Daily and annual climatology values

Description

Retrieves climatology values for one station or all available stations. aemet_daily_period() and aemet_daily_period_all() are shortcuts for aemet_daily_clim().

Usage

aemet_daily_clim(
  station = "all",
  start = Sys.Date() - 7,
  end = Sys.Date(),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period(
  station,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station	A character vector of station identifiers (see aemet_stations()) or "all" for all stations.
start, end	Character strings containing the start and end dates. See Details.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Details

For aemet_daily_clim(), start and end must be Date objects or strings in YYYY-MM-DD format, such as "2020-12-31", that can be coerced with as.Date(). For aemet_daily_period() and aemet_daily_period_all(), they must be strings representing the years to extract, such as "2018" and "2020".

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_stations() for station identifiers.

Climatology: aemet_extremes_clim(), aemet_monthly_clim(), aemet_normal_clim()

Examples

library(dplyr)
obs <- aemet_daily_clim(c("9434", "3195"))
glimpse(obs)

# Metadata.
meta <- aemet_daily_clim(c("9434", "3195"), extract_metadata = TRUE)

glimpse(meta$campos)

---

Check for an AEMET OpenData API key

Description

Detects whether an API key is available in the current session. An existing environment variable is preserved. Otherwise, a key stored permanently with aemet_api_key() is loaded.

Usage

aemet_detect_api_key(...)

aemet_show_api_key(...)

Arguments

...

Ignored.

Value

TRUE if an API key is available and FALSE otherwise. aemet_show_api_key() displays stored API keys.

See Also

AEMET OpenData API authentication: aemet_api_key()

Examples

aemet_detect_api_key()

# Caution: This may reveal API keys.
if (FALSE) {
  aemet_show_api_key()
}

---

Extreme values for a station

Description

Retrieves recorded extreme values for one or more stations.

Usage

aemet_extremes_clim(
  station = NULL,
  parameter = "T",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
parameter	A character string specifying the parameter to retrieve: temperature ("T"), precipitation ("P") or wind ("V").
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Value

A tibble or a sf object. If the function encounters a parsing error, it returns a list.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_stations() for station identifiers.

Climatology: aemet_daily_clim(), aemet_monthly_clim(), aemet_normal_clim()

Examples

obs <- aemet_extremes_clim(c("9434", "3195"))
dplyr::glimpse(obs)

---

Forecast weather at beaches

Description

Retrieves daily weather forecasts for one or more beaches. Use aemet_beaches() to obtain beach codes.

Usage

aemet_forecast_beaches(
  x,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

x	A character vector of beach codes to extract. See aemet_beaches().
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_beaches() for beach codes.

Forecasts: aemet_forecast_daily(), aemet_forecast_fires(), aemet_forecast_tidy()

Examples

# Forecast for beaches in Palma, Mallorca.
library(dplyr)
library(ggplot2)

palma_b <- aemet_beaches() |>
  filter(ID_MUNICIPIO == "07040")

forecast_b <- aemet_forecast_beaches(palma_b$ID_PLAYA)
glimpse(forecast_b)

ggplot(forecast_b) +
  geom_line(aes(fecha, tagua_valor1, color = nombre)) +
  facet_wrap(~nombre, ncol = 1) +
  labs(
    title = "Water temperature at beaches in Palma (ES)",
    subtitle = "3-day forecast",
    x = "Date",
    y = "Temperature (Celsius)",
    color = "Beach"
  )

---

Forecast weather in municipalities

Description

Retrieves daily or hourly weather forecasts for one or more municipalities.

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 character vector of municipality codes to extract. For convenience, climaemet provides these data in the aemet_munic dataset (see municipio field) as of January 2024.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Details

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

If extract_metadata = TRUE, the function returns a simple tibble describing each forecast field.

Value

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

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

• aemet_munic provides municipality codes.

• mapSpain provides sf objects of municipalities through mapSpain::esp_get_munic(). See also Examples.

Forecasts: aemet_forecast_beaches(), aemet_forecast_fires(), aemet_forecast_tidy()

Examples

# Select cities.
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)

# Variables 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 nested.
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 data.
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"
  )

---

AEMET wildfire risk forecast

Description

Retrieves daily wildfire risk levels as either tabular data or a SpatRaster.

Usage

aemet_forecast_fires(
  area = c("p", "c"),
  verbose = FALSE,
  extract_metadata = FALSE
)

Arguments

area	A character string specifying the forecast area: "p" for mainland Spain and the Balearic Islands or "c" for the Canary Islands.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().

Details

The SpatRaster provides six factor() levels: "1" for very low risk, "2" for low risk, "3" for moderate risk, "4" for high risk, "5" for very high risk and "6" for extreme risk.

The resulting object has several layers, each representing one of the next seven forecast days. It also has additional attributes provided by the terra, such as terra::time() and terra::coltab().

Value

A tibble or a SpatRaster.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Source

https://www.aemet.es/en/eltiempo/prediccion/incendios.

See Also

Forecasts: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_tidy()

Examples

aemet_forecast_fires(extract_metadata = TRUE)

# Extract alerts.
alerts <- aemet_forecast_fires()

alerts

# Plot the raster.
library(terra)
plot(alerts, all_levels = TRUE)

# Zoom in on an area.
cyl <- mapSpain::esp_get_ccaa("Castilla y Leon", epsg = 4326)

# Convert to a SpatVector.
cyl <- vect(cyl)

fires_cyl <- crop(alerts, cyl)
title <- names(fires_cyl)[1]

plot(fires_cyl[[1]], main = title, all_levels = TRUE)
plot(cyl, add = TRUE)

---

Extract values from forecasts

Description

aemet_forecast_vars_available() lists the variables in output from aemet_forecast_daily() or aemet_forecast_hourly(). aemet_forecast_tidy() extracts the forecast for var as a tibble.

Usage

aemet_forecast_tidy(x, var)

aemet_forecast_vars_available(x)

Arguments

x	A dataset extracted with aemet_forecast_daily() or aemet_forecast_hourly().
var	The name of the forecast variable to extract.

Value

A character vector from aemet_forecast_vars_available() or a tibble from aemet_forecast_tidy().

See Also

Forecasts: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires()

Examples

# Hourly values.
hourly <- aemet_forecast_hourly(c("15030", "28079"))

# Variables available.
aemet_forecast_vars_available(hourly)

# Get temperature.
temp <- aemet_forecast_tidy(hourly, "temperatura")

library(dplyr)
# Create a forecast time and adjust its time zone.
temp_end <- temp |>
  mutate(
    forecast_time = lubridate::force_tz(
      as.POSIXct(fecha) + hora,
      tz = "Europe/Madrid"
    )
  )

# Add sunset and sunrise.
suns <- temp_end |>
  select(nombre, fecha, orto, ocaso) |>
  distinct_all() |>
  group_by(nombre) |>
  mutate(
    ocaso_end = lubridate::force_tz(
      as.POSIXct(fecha) + ocaso,
      tz = "Europe/Madrid"
    ),
    orto_end = lubridate::force_tz(
      as.POSIXct(fecha) + orto,
      tz = "Europe/Madrid"
    ),
    orto_lead = lead(orto_end)
  ) |>
  tidyr::drop_na()

# Plot.

library(ggplot2)

ggplot(temp_end) +
  geom_rect(data = suns, aes(
    xmin = ocaso_end, xmax = orto_lead,
    ymin = min(temp_end$temperatura),
    ymax = max(temp_end$temperatura)
  ), alpha = 0.4) +
  geom_line(aes(forecast_time, temperatura), color = "blue4") +
  facet_wrap(~nombre, nrow = 2) +
  scale_x_datetime(labels = scales::label_date_short()) +
  scale_y_continuous(labels = scales::label_number(suffix = "º")) +
  labs(
    x = "", y = "",
    title = "Forecast: temperature",
    subtitle = paste("Forecast produced on", format(temp_end$elaborado[1],
      usetz = TRUE
    ))
  )

---

Latest observations from weather stations

Description

Retrieves the latest observations for one or more weather stations.

Usage

aemet_last_obs(
  station = "all",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station	A character vector of station identifiers (see aemet_stations()) or "all" for all stations.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_stations() for station identifiers.

Weather observations: aemet_alerts()

Examples

obs <- aemet_last_obs(c("9434", "3195"))
dplyr::glimpse(obs)

---

Monthly and annual climatology values

Description

Retrieves monthly or annual climatology values for one or more stations. aemet_monthly_period() and aemet_monthly_period_all() allow requests that span several years.

Usage

aemet_monthly_clim(
  station = NULL,
  year = as.integer(format(Sys.Date(), "%Y")),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period(
  station = NULL,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
year	A numeric year in YYYY format.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.
start	A numeric value specifying the start year in YYYY format.
end	A numeric value specifying the end year in YYYY format.

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_stations() for station identifiers.

Climatology: aemet_daily_clim(), aemet_extremes_clim(), aemet_normal_clim()

Examples

obs <- aemet_monthly_clim(station = c("9434", "3195"), year = 2000)
dplyr::glimpse(obs)

---

Municipalities of Spain

Description

A tibble containing all municipalities of Spain as defined by the INE (Instituto Nacional de Estadistica) as of January 2025.

Format

A tibble with 8,132 rows and fields:

municipio

INE code of the municipality.

municipio_nombre

INE name of the municipality.

cpro

INE code of the province.

cpro_nombre

INE name of the province.

codauto

INE code of the autonomous community.

codauto_nombre

INE name of the autonomous community.

Source

INE municipality codes by province: https://www.ine.es/daco/daco42/codmun/diccionario25.xlsx.

See Also

• aemet_forecast_daily() retrieves daily municipality forecasts.

• aemet_forecast_hourly() retrieves hourly municipality forecasts.

Examples

data(aemet_munic)

aemet_munic

---

Climatological normal values

Description

Retrieves climatological normal values for a station or for all stations with aemet_normal_clim_all(). The standard normal period is 1981–2010.

Usage

aemet_normal_clim(
  station = NULL,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_normal_clim_all(
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

station	A character vector of station identifiers (see aemet_stations()) or "all" for all stations.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.
extract_metadata	A logical value. If TRUE, returns a tibble describing the response fields. See get_metadata_aemet().
progress	A logical value. If TRUE, displays a cli::cli_progress_bar() unless verbose = TRUE.

Value

A tibble or a sf object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

Code modified from project https://github.com/SevillaR/aemet.

See Also

aemet_stations() for station identifiers.

Climatology: aemet_daily_clim(), aemet_extremes_clim(), aemet_monthly_clim()

Examples

obs <- aemet_normal_clim(c("9434", "3195"))
dplyr::glimpse(obs)

---

AEMET stations

Description

Retrieves the weather stations available from the AEMET OpenData API.

Usage

aemet_stations(verbose = FALSE, return_sf = FALSE)

Arguments

verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
return_sf	A logical value. If TRUE, the function returns an sf spatial object. If FALSE (the default), it returns a tibble. sf must be installed.

Value

A tibble or a sf object.

Caching

The first result retrieved in each session is temporarily cached in tempdir() to avoid unnecessary requests.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

Code modified from project https://github.com/SevillaR/aemet.

See Also

AEMET locations: aemet_alert_zones(), aemet_beaches()

Examples

library(dplyr)
stations <- aemet_stations()
stations

# Cached during this R session.
stations2 <- aemet_stations(verbose = TRUE)

identical(stations, stations2)

---

Climatological normals for Zaragoza Airport ("9434"), 1981–2010

Description

Climatological normal data for Zaragoza Airport (1981–2010). This example dataset is used to create Walter-Lieth climate diagrams.

Format

A data frame with four rows and 12 columns. Columns 1 through 12 represent months from January through December. Rows contain:

• p_mes_md: precipitation (mm).

• tm_max_md: maximum temperature (Celsius).

• tm_min_md: minimum temperature (Celsius).

• ta_min_min: absolute monthly minimum temperature (Celsius).

Source

AEMET.

See Also

• climatogram_normal() and climatogram_period() retrieve climatology data.

• ggclimat_walter_lieth() creates Walter-Lieth climate diagrams.

Examples

data(climaemet_9434_climatogram)

---

Average annual temperatures for Zaragoza Airport ("9434"), 1950–2020

Description

Yearly observations of average temperature for Zaragoza Airport (1950–2020). This is an example dataset.

Format

A tibble with columns:

year

Year of reference.

indicativo

Identifier of the station.

temp

Average temperature (Celsius).

Source

AEMET.

See Also

• climatestripes_station() retrieves annual temperature data.

• ggstripes() creates warming stripe plots.

Examples

data(climaemet_9434_temp)

---

Wind conditions for Zaragoza Airport ("9434"), 2000–2020

Description

Daily observations of wind speed and direction for Zaragoza Airport (2000–2020). This is an example dataset.

Format

A tibble with columns:

fecha

Date of observation.

dir

Wind direction (0-360 degrees).

velmedia

Average wind speed (km/h).

Source

AEMET.

See Also

• windrose_days() and windrose_period() retrieve wind observations.

• ggwindrose() creates wind rose plots.

Examples

data(climaemet_9434_wind)

---

Show the latest climaemet news

Description

Opens the NEWS file for climaemet.

Usage

climaemet_news()

Value

NULL, invisibly. This function is called for its side effect.

See Also

Helper functions: dms2decdegrees(), first_day_of_year()

Examples

## Not run: 
climaemet_news()

## End(Not run)

---

Warming stripes for a weather station

Description

Plots warming stripes for a weather station over a specified period.

Usage

climatestripes_station(
  station,
  start = 1950,
  end = 2020,
  with_labels = "yes",
  verbose = FALSE,
  ...
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
start	A numeric value specifying the start year in YYYY format.
end	A numeric value specifying the end year in YYYY format.
with_labels	A character string indicating whether to display plot labels, either "yes" or "no".
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
...	Arguments passed on to ggstripes n_temp The number of colors in the palette. Defaults to 11. col_pal A character string specifying an hcl.pals() color palette.

Value

A ggplot2::ggplot() object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

Professor Ed Hawkins of the University of Reading developed the "warming stripes" concept to communicate climate change risks as simply as possible. For more details, see ShowYourStripes.

See Also

climaemet_9434_temp.

Warming stripes: ggstripes()

Examples

# Do not run this example.
if (FALSE) {
  # Downloading data may take a few minutes.
  climatestripes_station(
    "9434",
    start = 2020,
    end = 2024,
    with_labels = "yes",
    col_pal = "Inferno"
  )
}

---

Walter-Lieth climate diagram from climatological normals

Description

Plots a Walter-Lieth climate diagram from climatological normal values for a station. The diagram summarizes local climate conditions for 1981–2010.

Usage

climatogram_normal(
  station,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
labels	A character string specifying the language for the x-axis month labels, such as "en" (English), "es" (Spanish) or "fr" (French).
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
ggplot2	A logical value. If TRUE, the function uses ggclimat_walter_lieth(). If FALSE, it uses climatol::diagwl().
...	Further arguments passed to climatol::diagwl() or ggclimat_walter_lieth(), depending on the value of ggplot2.

Value

A plot produced by ggclimat_walter_lieth() or climatol::diagwl(), depending on ggplot2.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

References

Walter H, Lieth H (1967). Klimadiagramm-Weltatlas. VEB Gustav Fischer Verlag, Jena. Published in three installments, 1960-1967, https://hdl.handle.net/2268.1/7079.

Guijarro JA (2026). climatol: Climate Tools (Series Homogenization and Derived Products). doi:10.32614/CRAN.package.climatol. R package version 4.5-0, https://CRAN.R-project.org/package=climatol.

See Also

climaemet_9434_climatogram.

Walter-Lieth climate diagrams: climatogram_period(), ggclimat_walter_lieth()

Examples

climatogram_normal("9434")

---

Walter-Lieth climate diagram for a time period

Description

Plots a Walter-Lieth climate diagram from monthly climatology values for a station over a specified time period.

Usage

climatogram_period(
  station = NULL,
  start = 1990,
  end = 2020,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
start	A numeric value specifying the start year in YYYY format.
end	A numeric value specifying the end year in YYYY format.
labels	A character string specifying the language for the x-axis month labels, such as "en" (English), "es" (Spanish) or "fr" (French).
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.
ggplot2	A logical value. If TRUE, the function uses ggclimat_walter_lieth(). If FALSE, it uses climatol::diagwl().
...	Further arguments passed to climatol::diagwl() or ggclimat_walter_lieth(), depending on the value of ggplot2.

Value

A plot produced by ggclimat_walter_lieth() or climatol::diagwl(), depending on ggplot2.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

References

Walter H, Lieth H (1967). Klimadiagramm-Weltatlas. VEB Gustav Fischer Verlag, Jena. Published in three installments, 1960-1967, https://hdl.handle.net/2268.1/7079.

Guijarro JA (2026). climatol: Climate Tools (Series Homogenization and Derived Products). doi:10.32614/CRAN.package.climatol. R package version 4.5-0, https://CRAN.R-project.org/package=climatol.

See Also

Walter-Lieth climate diagrams: climatogram_normal(), ggclimat_walter_lieth()

Examples

climatogram_period("9434", start = 2015, end = 2020, labels = "en")

---

Convert DMS coordinates to decimal degrees

Description

Converts degrees, minutes and seconds to decimal degrees.

Usage

dms2decdegrees(input = NULL)

dms2decdegrees_2(input = NULL)

Arguments

input

A character string containing DMS coordinates.

Value

A numeric value.

Note

Code for dms2decdegrees() was modified from the project at https://github.com/SevillaR/aemet.

See Also

Helper functions: climaemet_news(), first_day_of_year()

Examples

dms2decdegrees("055245W")
dms2decdegrees_2("-3º 40' 37\"")

---

First and last day of a year

Description

Returns the first or last calendar day of a year.

Usage

first_day_of_year(year = NULL)

last_day_of_year(year = NULL)

Arguments

year	A numeric year in YYYY format.

Value

A character string containing a date in YYYY-MM-DD format.

See Also

Helper functions: climaemet_news(), dms2decdegrees()

Examples

first_day_of_year(2000)
last_day_of_year(2020)

---

Query the AEMET OpenData API

Description

Retrieves data and metadata from AEMET and converts JSON responses to a tibble when possible.

Usage

get_data_aemet(apidest, verbose = FALSE)

get_metadata_aemet(apidest, verbose = FALSE)

Arguments

apidest	A character string containing the destination URL. See https://opendata.aemet.es/dist/index.html.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.

Value

A tibble (if possible) or the results of the query as provided by httr2::resp_body_raw() or httr2::resp_body_string().

Source

https://opendata.aemet.es/dist/index.html.

See Also

• aemet_api_key() configures API authentication.

• vignette("extending-climaemet", package = "climaemet") provides usage examples.

Examples

# Run only when AEMET_API_KEY is detected.

url <- "/api/valores/climatologicos/inventarioestaciones/todasestaciones"

get_data_aemet(url)

# Metadata.

get_metadata_aemet(url)

# Get data from any API endpoint.

# Plain text.

plain <- get_data_aemet("/api/prediccion/nacional/hoy")

cat(plain)

# An image.

image <- get_data_aemet("/api/mapasygraficos/analisis")

# Write and read.
tmp <- tempfile(fileext = ".gif")

writeBin(image, tmp)

gganimate::gif_file(tmp)

---

Walter-Lieth climate diagram with ggplot2

Description

Plots a Walter-Lieth climate diagram for a station using ggplot2.

Usage

ggclimat_walter_lieth(
  dat,
  est = "",
  alt = NA,
  per = NA,
  mlab = "es",
  pcol = "#002F70",
  tcol = "#ff0000",
  pfcol = "#9BAEE2",
  sfcol = "#3C6FC4",
  shem = FALSE,
  p3line = FALSE,
  ...
)

Arguments

dat	A data frame containing monthly climatology data.
est	A character string with the climatological station name.
alt	A numeric value with the station altitude in meters.
per	A character string describing the averaging period.
mlab	Month labels for the x-axis. Use a two-letter language code, such as "en" or "es". See readr::locale() for details.
pcol	A color for precipitation.
tcol	A color for temperature.
pfcol	A fill color for probable frosts.
sfcol	A fill color for certain frosts.
shem	A logical value. If TRUE, plots a Southern Hemisphere station.
p3line	Set to TRUE to draw a supplementary precipitation line relative to three times the temperature (as suggested by Bogdan Rosca).
...	Further graphic arguments.

Details

See the details in climatol::diagwl().

Climatology data must be passed as a 4 by 12 matrix or data frame of monthly data from January to December. Rows must contain mean precipitation, mean maximum daily temperature, mean minimum daily temperature and absolute monthly minimum temperature, in that order.

See climaemet_9434_climatogram for a sample dataset.

Value

A ggplot2::ggplot() object.

References

Walter H, Lieth H (1967). Klimadiagramm-Weltatlas. VEB Gustav Fischer Verlag, Jena. Published in three installments, 1960-1967, https://hdl.handle.net/2268.1/7079.

Guijarro JA (2026). climatol: Climate Tools (Series Homogenization and Derived Products). doi:10.32614/CRAN.package.climatol. R package version 4.5-0, https://CRAN.R-project.org/package=climatol.

See Also

• climatol::diagwl() provides the original diagram implementation.

• readr::locale() provides language-specific month labels.

Walter-Lieth climate diagrams: climatogram_normal(), climatogram_period()

Examples

library(ggplot2)

wl <- ggclimat_walter_lieth(
  climaemet::climaemet_9434_climatogram,
  alt = "249",
  per = "1981-2010",
  est = "Zaragoza Airport"
)

wl

# Since it is a ggplot object, we can modify it.

wl + theme(
  plot.background = element_rect(fill = "grey80"),
  panel.background = element_rect(fill = "grey70"),
  axis.text.y.left = element_text(
    colour = "black",
    face = "italic"
  ),
  axis.text.y.right = element_text(
    colour = "black",
    face = "bold"
  )
)

---

Plot warming stripes

Description

Plots warming stripes with ggplot2. These graphics represent temperature change at a location over at least 70 years. Each stripe shows the annual average temperature at that station.

Usage

ggstripes(
  data,
  plot_type = "stripes",
  plot_title = "",
  n_temp = 11,
  col_pal = "RdBu",
  ...
)

Arguments

data	A data frame with date (year) and temperature (temp) variables.
plot_type	A character string specifying the plot type: "background", "stripes", "trend" or "animation".
plot_title	A character string for the plot title.
n_temp	The number of colors in the palette. Defaults to 11.
col_pal	A character string specifying an hcl.pals() color palette.
...	Further arguments passed to ggplot2::theme().

Value

A ggplot2::ggplot() object.

Note

Professor Ed Hawkins of the University of Reading developed the "warming stripes" concept to communicate climate change risks as simply as possible. For more details, see ShowYourStripes.

See Also

ggplot2::theme() for additional arguments to ggstripes() and climaemet_9434_temp.

Warming stripes: climatestripes_station()

Examples

library(ggplot2)

data <- climaemet::climaemet_9434_temp

ggstripes(data, plot_title = "Zaragoza Airport") +
  labs(subtitle = "(1950-2020)")

ggstripes(data, plot_title = "Zaragoza Airport", plot_type = "trend") +
  labs(subtitle = "(1950-2020)")

---

Plot a wind rose

Description

Plots a wind rose showing wind speed and direction with ggplot2.

Usage

ggwindrose(
  speed,
  direction,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  facet = NULL,
  plot_title = "",
  stack_reverse = FALSE,
  ...
)

Arguments

speed	A numeric vector of wind speeds.
direction	A numeric vector of wind directions.
n_directions	The number of direction bins (petals) to plot. Valid values are 4, 8 or 16.
n_speeds	The number of equally spaced wind speed bins to plot when speed_cuts is NA. Defaults to 5.
speed_cuts	A numeric vector with the cut points for the wind speed intervals or NA (default).
col_pal	A character string specifying an hcl.pals() color palette.
legend_title	A character string or expression for the legend title.
calm_wind	The upper wind speed limit considered calm. Defaults to 0.
n_col	The number of plot columns. Defaults to 1.
facet	A character or factor vector of facets used to plot wind roses.
plot_title	A character string for the plot title.
stack_reverse	A logical value. If TRUE, reverses the stack order of speed cuts. See Examples.
...	Further arguments (ignored).

Value

A ggplot2::ggplot() object.

See Also

ggplot2::theme() for additional arguments to pass to ggwindrose() and climaemet_9434_wind.

Wind roses: windrose_days(), windrose_period()

Examples

library(ggplot2)

speed <- climaemet::climaemet_9434_wind$velmedia
direction <- climaemet::climaemet_9434_wind$dir

rose <- ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport"
)
rose + labs(
  subtitle = "2000-2020",
  caption = "Source: AEMET"
)

# Reverse the stack.

ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport",
  stack_reverse = TRUE
) +
  labs(
    subtitle = "2000-2020",
    caption = "Source: AEMET"
  )

---

Wind rose for a range of days

Description

Plots a wind rose showing wind speed and direction at a station over a period of days.

Usage

windrose_days(
  station,
  start = "2000-12-01",
  end = "2000-12-31",
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind speed (m/s)",
  verbose = FALSE
)

Arguments

station	A character vector of station identifiers (see aemet_stations()) or "all" for all stations.
start	A character string containing the start date in YYYY-MM-DD format.
end	A character string containing the end date in YYYY-MM-DD format.
n_directions	The number of direction bins (petals) to plot. Valid values are 4, 8 or 16.
n_speeds	The number of equally spaced wind speed bins to plot when speed_cuts is NA. Defaults to 5.
speed_cuts	A numeric vector with the cut points for the wind speed intervals or NA (default).
col_pal	A character string specifying an hcl.pals() color palette.
calm_wind	The upper wind speed limit considered calm. Defaults to 0.
legend_title	A character string or expression for the legend title.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.

Value

A ggplot2::ggplot() object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

• aemet_daily_clim() retrieves daily climatology data.

• climaemet_9434_wind provides example wind observations.

Wind roses: ggwindrose(), windrose_period()

Examples

windrose_days("9434",
  start = "2000-12-01",
  end = "2000-12-31",
  speed_cuts = 4
)

---

Wind rose for a range of years

Description

Plots a wind rose showing wind speed and direction at a station over a time period.

Usage

windrose_period(
  station,
  start = 2000,
  end = 2010,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind speed (m/s)",
  verbose = FALSE
)

Arguments

station	A character vector of station identifiers. See aemet_stations().
start	A numeric value specifying the start year in YYYY format.
end	A numeric value specifying the end year in YYYY format.
n_directions	The number of direction bins (petals) to plot. Valid values are 4, 8 or 16.
n_speeds	The number of equally spaced wind speed bins to plot when speed_cuts is NA. Defaults to 5.
speed_cuts	A numeric vector with the cut points for the wind speed intervals or NA (default).
col_pal	A character string specifying an hcl.pals() color palette.
calm_wind	The upper wind speed limit considered calm. Defaults to 0.
legend_title	A character string or expression for the legend title.
verbose	A logical value. If TRUE, displays information about the exchange between the client and server.

Value

A ggplot2::ggplot() object.

API key

Queries to the AEMET OpenData API require an API key. Use aemet_api_key() to set it globally. Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

• aemet_daily_period() retrieves daily climatology data by period.

• climaemet_9434_wind provides example wind observations.

Wind roses: ggwindrose(), windrose_days()

Examples

# Do not run this example.
if (FALSE) {
  # Downloading data may take a few minutes.
  windrose_period("9434",
    start = 2000, end = 2010,
    speed_cuts = 4
  )
}

---