# use eval = NOT_CRAN in the chunks connecting to API, to avoid errors or warnings in CRAN checks NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true") knitr::opts_chunk$set( collapse = TRUE, comment = "#>", purl = NOT_CRAN ) # env keyring withr::local_options(list("keyring_backend" = "env"))
library(meteospain) library(dplyr) library(ggplot2) library(ggforce) library(units) library(sf)
RIA service offers the data of the andalucian automatic meteorological stations network. This network is supported and assessed by the Junta de Andalucía and the data should be trustworthy.
RIA API offers data at different temporal resolutions:
In both, "daily" and "monthly", a start_date
(and optionally an end_date
) arguments must be provided,
indicating the period from which retrieve the data.
RIA API needs station codes and province codes to retrieve the data. Sadly, RIA doesn't provide unique
station codes, and the uniqueness comes with the province id and station code together. So, to narrow the
data retrieving to the desired stations they must be provided as a character vector of
"province_id-station_code" values (i.e. "14-2") for the stations
argument. Calling
get_stations_info_from('ria', ria_options)
will show the station correct codes in the station_id
column
to take as reference.
# default, daily for yesterday api_options <- ria_options() api_options # daily, only some stations api_options <- ria_options( resolution = 'daily', stations = c('14-2', '4-2') ) api_options # monthly, some stations api_options <- ria_options( resolution = 'monthly', start_date = as.Date('2020-04-01'), end_date = as.Date('2020-08-01'), stations = c('14-2', '4-2') ) api_options
Accessing station metadata for RIA is simple:
get_stations_info_from('ria', api_options)
api_options <- ria_options( resolution = 'monthly', start_date = as.Date('2020-01-01'), end_date = as.Date('2020-12-31') ) andalucia_2020 <- get_meteo_from('ria', options = api_options) andalucia_2020
Visually:
andalucia_2020 |> units::drop_units() |> mutate(month = lubridate::month(timestamp, label = TRUE)) |> ggplot() + geom_sf(aes(colour = max_temperature)) + facet_wrap(vars(month), ncol = 4) + scale_colour_viridis_c() andalucia_2020 |> mutate(month = lubridate::month(timestamp, label = TRUE)) |> ggplot() + geom_histogram(aes(x = precipitation)) + facet_wrap(vars(month), ncol = 4)
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