Package {climaemet}

Type:	Package
Title:	Tools for AEMET Climate Data
Version:	1.6.0
Description:	Download meteorological and climate data from the Spanish Meteorological Agency (AEMET) directly in R using the AEMET API. Create scientific visualizations, including climate charts, climate time series trend analyses, temperature and precipitation anomaly maps, warming stripes and climatograms.
License:	GPL-3
URL:	https://ropenspain.github.io/climaemet/, https://github.com/rOpenSpain/climaemet
BugReports:	https://github.com/rOpenSpain/climaemet/issues
Depends:	R (≥ 4.1.0)
Imports:	cli (≥ 3.0.0), dplyr (≥ 1.0.0), ggplot2 (≥ 3.5.0), httr2 (≥ 1.0.0), jsonlite (≥ 1.7.0), rappdirs (≥ 0.3.3), readr (≥ 1.4.0), rlang (≥ 0.4.6), tibble (≥ 3.0.3), tidyr (≥ 1.1.0), tools, xml2
Suggests:	climatol (≥ 3.1.2), gganimate (≥ 1.0.5), jpeg (≥ 0.1.8), knitr, lifecycle, lubridate, mapSpain, quarto, scales, sf (≥ 1.0.0), terra (≥ 1.8-10), testthat (≥ 3.2.0), withr (≥ 3.0.0)
VignetteBuilder:	quarto
Config/Needs/website:	cpp11, crosstalk, devtools, geoR, gifski, gstat, leaflet, reactable, scales, tidyterra, tidyverse, usethis, styler
Config/roxygen2/markdown:	TRUE
Config/roxygen2/version:	8.0.0
Config/testthat/edition:	3
Config/testthat/parallel:	false
Copyright:	© AEMET. See file COPYRIGHTS
Encoding:	UTF-8
LazyData:	true
X-schema.org-applicationCategory:	Meteorology
X-schema.org-isPartOf:	https://ropenspain.es/
X-schema.org-keywords:	aemet, climate, cran, data, forecast-api, r, r-package, ropenspain, rstats, science, spain, weather-api
NeedsCompilation:	no
Packaged:	2026-06-03 13:43:26 UTC; diego
Author:	Manuel Pizarro [aut, cph], Diego Hernangómez [aut, cre], Gema Fernández-Avilés [aut], AEMET [cph]
Maintainer:	Diego Hernangómez <diego.hernangomezherrero@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-03 14:20:07 UTC

climaemet: Tools for AEMET Climate Data

Description

Download meteorological and climate data from the Spanish Meteorological Agency (AEMET) directly in R using the AEMET API. Create scientific visualizations, including climate charts, climate time series trend analyses, temperature and precipitation anomaly maps, warming stripes and climatograms.

Author(s)

Maintainer: Diego Hernangómez diego.hernangomezherrero@gmail.com (ORCID)

Authors:

• Diego Hernangómez diego.hernangomezherrero@gmail.com (ORCID)

• Manuel Pizarro (ORCID) [copyright holder]

• Gema Fernández-Avilés (ORCID)

Other contributors:

• AEMET (ROR) [copyright holder]

See Also

Useful links:

• https://ropenspain.github.io/climaemet/

• https://github.com/rOpenSpain/climaemet

• Report bugs at https://github.com/rOpenSpain/climaemet/issues

AEMET alert zones

Description

Get AEMET alert zones.

Usage

aemet_alert_zones(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of each call per session is temporarily cached in tempdir() to avoid unnecessary API calls.

Value

A tibble or a sf object.

Source

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

See Also

aemet_alerts()

AEMET data functions: aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
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(dplyr)
library(ggplot2)

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

# Coast zones are identified by a "C" in COD_Z.
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

Get current meteorological alerts.

Usage

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

Arguments

Value

A tibble or a sf object.

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

aemet_alert_zones(). See also mapSpain::esp_codelist, mapSpain::esp_dict_region_code() to get the names of the autonomous communities.

AEMET data functions: aemet_alert_zones(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

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 API key

Description

This function stores your AEMET API key on your local machine so it can be called securely without being stored in your code.

Alternatively, you can install the API key manually:

• Run Sys.setenv(AEMET_API_KEY = "Your_Key"). You will need to run this command in each session (similar to install = FALSE).

• Write this line in your .Renviron file: AEMET_API_KEY = "Your_Key" (same behavior as install = TRUE). This stores your API key permanently.

Usage

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

Arguments

Details

You can pass several apikey values as a character vector c(api1, api2). In this case, multiple AEMET_API_KEY values are generated. 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

Invisibly returns NULL.

Note

To locate your API key on your local machine, run tools::R_user_dir("climaemet", "config").

See Also

AEMET API key helpers: 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

Get AEMET beaches.

Usage

aemet_beaches(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of the API call in each session is temporarily cached in tempdir() to avoid unnecessary API calls.

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_forecast_beaches()

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
beaches <- aemet_beaches()
beaches

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

identical(beaches, beaches2)

# Select and map beaches
library(dplyr)
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/annual climatology values

Description

Get climatology values for a station or for all the available stations. Note that aemet_daily_period() and aemet_daily_period_all() are shortcuts of 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

Details

start and end arguments must be:

• For aemet_daily_clim(): A Date object or a string with format YYYY-MM-DD ("2020-12-31") coercible with as.Date().

• For aemet_daily_period() and aemet_daily_period_all(): A string representing the year(s) to be extracted: "2020", "2018".

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_api_key(), as.Date()

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

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

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

glimpse(meta$campos)

Check whether an AEMET API key is present for the current session

Description

Detects whether an API key is available in the current session:

• If an API key is already set as an environment variable, it is preserved.

• If no environment variable is set and an API key has been stored permanently via aemet_api_key(), it is loaded.

Usage

aemet_detect_api_key(...)

aemet_show_api_key(...)

Arguments

Value

TRUE or FALSE. aemet_show_api_key() displays your stored API keys.

See Also

AEMET API key helpers: 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

Get recorded extreme values for a station.

Usage

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

Arguments

Value

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

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_api_key()

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_extremes_clim(c("9434", "3195"))
glimpse(obs)

Beach forecast dataset

Description

Get daily weather forecasts for one or more beaches. Beach codes can be accessed with aemet_beaches().

Usage

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

Arguments

Value

A tibble or a sf object.

API key

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

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Forecast functions: 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 in beaches of Palma (ES)",
    subtitle = "3-day forecast",
    x = "Date",
    y = "Temperature (Celsius)",
    color = "Beach"
  )

Municipality forecast dataset

Description

Get 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

Details

Forecasts provided by the AEMET 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 a simple tibble describing the value of each field of the forecast is returned.

Value

A nested tibble. Forecast 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(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

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

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Forecast functions: aemet_forecast_beaches(), aemet_forecast_fires(), 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)

# 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 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"
  )

AEMET wildfire risk forecast

Description

Get a SpatRaster with the daily wildfire risk level.

Usage

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

Arguments

Details

The SpatRaster provides six factor() levels with the following meaning:

• "1": Very low risk.

• "2": Low risk.

• "3": Moderate risk.

• "4": High risk.

• "5": Very high risk.

• "6": Extreme risk.

The resulting object has several layers, each one representing the forecast for the upcoming 7 days. It also has additional attributes provided by the terra package, such as terra::time() and terra::coltab().

Value

A tibble or a SpatRaster.

Source

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

See Also

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Forecast functions: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_tidy()

Examples

aemet_forecast_fires(extract_metadata = TRUE)

# Extract alerts.
alerts <- aemet_forecast_fires()

alerts

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

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

# 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)

Helper functions for extracting forecasts

Description

Helpers for aemet_forecast_daily() and aemet_forecast_hourly():

• aemet_forecast_vars_available() extracts the values available in the dataset.

• aemet_forecast_tidy() produces a tibble with the forecast for var.

Usage

aemet_forecast_tidy(x, var)

aemet_forecast_vars_available(x)

Arguments

Value

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

See Also

Forecast functions: 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. This needs lubridate to adjust time zones.
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
    ))
  )

Last observation values for a station

Description

Get last observation values for a station.

Usage

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

Arguments

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_last_obs(c("9434", "3195"))
glimpse(obs)

Monthly/annual climatology values

Description

Get monthly/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

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

AEMET data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_normal, aemet_stations()

Examples

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

Municipalities of Spain

Description

A tibble with 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(), aemet_forecast_hourly()

Included datasets: climaemet_9434_climatogram, climaemet_9434_temp, climaemet_9434_wind

Examples

data(aemet_munic)

aemet_munic

Normal climatology values

Description

Get normal climatology values for a station, or for all stations with aemet_normal_clim_all(). Standard climatology covers 1981 to 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

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). 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 data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_stations()

Examples

library(tibble)
obs <- aemet_normal_clim(c("9434", "3195"))
glimpse(obs)

AEMET stations

Description

Get AEMET stations.

Usage

aemet_stations(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of the API call in each session is temporarily cached in tempdir() to avoid unnecessary API calls.

Value

A tibble or a sf object.

API key

You need to set your API key globally using aemet_api_key(). 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 data functions: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal

Examples

library(tibble)
stations <- aemet_stations()
stations

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

identical(stations, stations2)

Climatogram data for Zaragoza Airport ("9434"), 1981-2010

Description

Normal data for Zaragoza Airport (1981-2010). This is an example dataset used to plot climatograms.

Format

A data.frame with columns 1 to 12 (months) and rows:

p_mes_md

Precipitation (mm).

tm_max_md

Maximum temperature (Celsius).

tm_min_md

Minimum temperature (Celsius).

ta_min_md

Absolute monthly minimum temperature (Celsius).

Source

AEMET.

See Also

ggclimat_walter_lieth(), climatogram_period(), climatogram_normal()

Included datasets: aemet_munic, climaemet_9434_temp, climaemet_9434_wind

Climatogram functions: climatogram_normal(), climatogram_period(), ggclimat_walter_lieth()

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

Included datasets: aemet_munic, climaemet_9434_climatogram, climaemet_9434_wind

Warming stripes functions: climatestripes_station(), ggstripes()

Examples

data(climaemet_9434_temp)

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

Description

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

Format

A tibble with columns:

fecha

Date of observation.

dir

Wind directions (0-360).

velmedia

Average wind speed (km/h).

Source

AEMET.

See Also

Included datasets: aemet_munic, climaemet_9434_climatogram, climaemet_9434_temp

Wind functions: ggwindrose(), windrose_days(), windrose_period()

Examples

data(climaemet_9434_wind)

Show the latest news of climaemet

Description

Open the NEWS file of the climaemet package.

Usage

climaemet_news()

Value

Nothing, this function is called by its side effects.

See Also

Helper functions: dms2decdegrees(), first_day_of_year()

Examples

## Not run: 
climaemet_news()

## End(Not run)

Station climate stripes plot

Description

Plot a climate stripes graph for a station.

Usage

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

Arguments

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

"Warming stripes" charts are a conceptual idea of Professor Ed Hawkins (University of Reading) and are specifically designed to be as simple as possible and to warn about climate change risks. For more details, see ShowYourStripes.

See Also

ggstripes()

Plotting functions: climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Warming stripes functions: climaemet_9434_temp, 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 climatic diagram from normal climatology values

Description

Plot a Walter & Lieth climatic diagram from normal climatology values for a station. This climatogram is a great way to show a summary of climate conditions for a place over a time period (1981-2010).

Usage

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

Arguments

Value

A plot.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

The code is based on code from the CRAN package climatol.

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

• Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

See Also

Plotting functions: climatestripes_station(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Climatogram functions: climaemet_9434_climatogram, climatogram_period(), ggclimat_walter_lieth()

Examples

climatogram_normal("9434")

Walter & Lieth climatic diagram for a time period

Description

Plot a Walter & Lieth climatic diagram from monthly climatology values for a station. This climatogram is a great way to show a summary of climate conditions for a place over a specific time period.

Usage

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

Arguments

Value

A plot.

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

The code is based on code from the CRAN package climatol.

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

• Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

See Also

Plotting functions: climatestripes_station(), climatogram_normal(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Climatogram functions: climaemet_9434_climatogram, climatogram_normal(), ggclimat_walter_lieth()

Examples

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

Convert dms format to decimal degrees

Description

Convert degrees, minutes and seconds to decimal degrees.

Usage

dms2decdegrees(input = NULL)

dms2decdegrees_2(input = NULL)

Arguments

Value

A numeric value.

Note

Code for dms2decdegrees() was modified from project 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

Get the first and last day of a year.

Usage

first_day_of_year(year = NULL)

last_day_of_year(year = NULL)

Arguments

Value

Character string with date (format: YYYY-MM-DD).

See Also

Helper functions: climaemet_news(), dms2decdegrees()

Examples

first_day_of_year(2000)
last_day_of_year(2020)

Client tool for the AEMET API

Description

Client tool to retrieve data and metadata from AEMET and convert JSON to a tibble.

Usage

get_data_aemet(apidest, verbose = FALSE)

get_metadata_aemet(apidest, verbose = FALSE)

Arguments

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

See examples of how to use these functions in vignette("extending-climaemet").

Examples

# Run this example only if 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 climatic diagram with ggplot2

Description

Plot a Walter & Lieth climatic diagram for a station. This function is an updated version of climatol::diagwl(), by Jose A. Guijarro.

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

Details

See the details in climatol::diagwl().

Climate data must be passed as a 4 x 12 matrix or data.frame of monthly data (January to December) in the following order:

• Row 1: Mean precipitation.

• Row 2: Mean maximum daily temperature.

• Row 3: Mean minimum daily temperature.

• Row 4: Absolute monthly minimum temperature.

See climaemet_9434_climatogram for a sample dataset.

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

• Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

See Also

climatol::diagwl(), readr::locale()

Plotting functions: climatestripes_station(), climatogram_normal(), climatogram_period(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Climatogram functions: climaemet_9434_climatogram, 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"
  )
)

Warming stripes graph

Description

Plot different "climate stripes" or "warming stripes" using ggplot2. These graphics are visual representations of the change in temperature as measured in each location over the past 70-100+ years. Each stripe represents the temperature in that station averaged over a year.

Usage

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

Arguments

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

Note

"Warming stripes" charts are a conceptual idea of Professor Ed Hawkins (University of Reading) and are specifically designed to be as simple as possible and to warn about climate change risks. For more details, see ShowYourStripes.

See Also

climatestripes_station(), ggplot2::theme() for more possible arguments to pass to ggstripes().

Plotting functions: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggwindrose(), windrose_days(), windrose_period()

Warming stripes functions: climaemet_9434_temp, 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)")

Windrose (speed/direction) diagram

Description

Plot a windrose showing the wind speed and direction using 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

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

ggplot2::theme() for more possible arguments to pass to ggwindrose().

Plotting functions: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), windrose_days(), windrose_period()

Wind functions: climaemet_9434_wind, 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"
  )

Windrose (speed/direction) diagram of a station over a period of days

Description

Plot a windrose showing the wind speed and direction for 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

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_daily_clim()

Plotting functions: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_period()

Wind functions: climaemet_9434_wind, ggwindrose(), windrose_period()

Examples

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

Windrose (speed/direction) diagram of a station over a time period

Description

Plot a windrose showing the wind speed and direction for 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

Value

A ggplot2 object. See help("ggplot2").

API key

You need to set your API key globally using aemet_api_key(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

See Also

aemet_daily_period()

Plotting functions: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days()

Wind functions: climaemet_9434_wind, 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
  )
}