April 2020 daily data
We are gonna download daily data for April, 2020 for all services providing this information, and combine them in one object:
Don’t forget to store the keys for AEMET and MeteoCat if not done already (see code above)
aemet_daily <- get_meteo_from(
'aemet', aemet_options(
'daily', start_date = as.Date('2020-04-16'), end_date = as.Date('2020-04-30'),
api_key = keyring::key_get('aemet')
)
)
#> Warning: Unable to connect to API at <https://opendata.aemet.es/opendata/sh/b3aa9d28>:
#> Error in curl::curl_fetch_memory(url, handle = handle): Failure when receiving
#> data from the peer [opendata.aemet.es]: OpenSSL SSL_read: OpenSSL/3.5.3:
#> error:0A000126:SSL routines::unexpected eof while reading, errno 0
#> ℹ This usually happens when connection with
#> <https://opendata.aemet.es/opendata/sh/b3aa9d28> is not possible.
#> ℹ Data received
#> Trying again in 60 seconds (retry 1 of 3)
#> ℹ © AEMET. Autorizado el uso de la información y su reproducción citando a
#> AEMET como autora de la misma.
#> https://www.aemet.es/es/nota_legal
meteocat_daily <- get_meteo_from(
'meteocat',
meteocat_options('daily', start_date = as.Date('2020-04-16'), api_key = keyring::key_get('meteocat'))
)
meteogalicia_daily <- get_meteo_from(
'meteogalicia',
meteogalicia_options('daily', start_date = as.Date('2020-04-16'), end_date = as.Date('2020-04-30'))
)
#> ℹ A información divulgada a través deste servidor ofrécese gratuitamente aos
#> cidadáns para que poida ser
#> utilizada libremente por eles, co único compromiso de mencionar expresamente a
#> MeteoGalicia e á
#> Consellería de Medio Ambiente, Territorio e Vivenda da Xunta de Galicia como
#> fonte da mesma cada vez
#> que as utilice para os usos distintos do particular e privado.
#> https://www.meteogalicia.gal/aviso-legal
ria_daily <- get_meteo_from(
'ria',
ria_options('daily', start_date = as.Date('2020-04-16'), end_date = as.Date('2020-04-30'))
)
#> Some stations didn't return data for some dates:
#> 11-3
#> 11-8
#> 11-9
#> 14-3
#> 18-4
#> 18-9
#> 21-1
#> 21-104
#> 21-106
#> 21-107
#> 21-12
#> 23-10
#> 23-13
#> 23-9
#> 29-3
#> 29-5
#> 4-3
#> 4-9
#> 41-1
#> 41-14
#> 41-4
#> 41-6
#> ℹ Data provided by Red de Información Agroclimática de Andalucía (RIA)
#> https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/Now we have all daily data for April, lets join them. We are gonna
use the purrr package to do it in one pipe.
Here we convert the data to tibble before the join, that way we are not
joining by the spatial data, but by timestamp and the stations metadata.
After the join we convert back to sf.
april_2020_spain <- list(
dplyr::as_tibble(aemet_daily),
dplyr::as_tibble(meteocat_daily),
dplyr::as_tibble(meteogalicia_daily),
dplyr::as_tibble(ria_daily)
) |>
purrr::reduce(dplyr::full_join) |>
sf::st_as_sf()
#> Joining with `by = join_by(timestamp, service, station_id, station_name,
#> station_province, altitude, mean_temperature, min_temperature, max_temperature,
#> mean_relative_humidity, min_relative_humidity, max_relative_humidity,
#> precipitation, mean_wind_speed, max_wind_speed, max_atmospheric_pressure,
#> min_atmospheric_pressure, geometry)`
#> Joining with `by = join_by(timestamp, service, station_id, station_name,
#> station_province, altitude, mean_temperature, min_temperature, max_temperature,
#> mean_relative_humidity, min_relative_humidity, max_relative_humidity,
#> precipitation, mean_wind_speed, max_wind_speed, insolation, geometry,
#> mean_wind_direction, max_wind_direction, mean_atmospheric_pressure,
#> reference_evapotranspiration)`
#> Joining with `by = join_by(timestamp, service, station_id, station_name,
#> station_province, altitude, mean_temperature, min_temperature, max_temperature,
#> mean_relative_humidity, min_relative_humidity, max_relative_humidity,
#> precipitation, mean_wind_speed, max_wind_speed, geometry, mean_wind_direction,
#> max_wind_direction)`
april_2020_spain
#> Simple feature collection with 20984 features and 43 fields (with 75 geometries empty)
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -18.115 ymin: 27.71889 xmax: 4.323889 ymax: 43.78611
#> Geodetic CRS: WGS 84
#> # A tibble: 20,984 × 44
#> timestamp service station_id station_name station_province altitude
#> <dttm> <chr> <chr> <chr> <chr> [m]
#> 1 2020-04-16 00:00:00 aemet 0009X ALFORJA TARRAGONA 406
#> 2 2020-04-16 00:00:00 aemet 0016A REUS AEROPU… TARRAGONA 71
#> 3 2020-04-16 00:00:00 aemet 0016B REUS (CENTR… TARRAGONA 118
#> 4 2020-04-16 00:00:00 aemet 0034X VALLS TARRAGONA 233
#> 5 2020-04-16 00:00:00 aemet 0061X PONTONS BARCELONA 632
#> 6 2020-04-16 00:00:00 aemet 0073X SITGES BARCELONA 58
#> 7 2020-04-16 00:00:00 aemet 0076 BARCELONA A… BARCELONA 4
#> 8 2020-04-16 00:00:00 aemet 0092X BERGA BARCELONA 682
#> 9 2020-04-16 00:00:00 aemet 0106X BALSARENY BARCELONA 361
#> 10 2020-04-16 00:00:00 aemet 0114X PRATS DE LL… BARCELONA 700
#> # ℹ 20,974 more rows
#> # ℹ 38 more variables: mean_temperature [°C], min_temperature [°C],
#> # max_temperature [°C], mean_relative_humidity [%],
#> # min_relative_humidity [%], max_relative_humidity [%],
#> # precipitation [L/m^2], wind_direction [°], mean_wind_speed [m/s],
#> # max_wind_speed [m/s], insolation [h], max_atmospheric_pressure [hPa],
#> # min_atmospheric_pressure [hPa], geometry <POINT [°]>, …We can visualize the data, only one day.
