aemet_forecast: Forecast database by municipality
In climaemet: Climate AEMET Tools

aemet_forecast_daily

R Documentation

Forecast database by municipality

Description

Get a database of daily or hourly weather forecasts for a given municipality.

Usage

aemet_forecast_daily(x, verbose = FALSE, extract_metadata = FALSE)

aemet_forecast_hourly(x, verbose = FALSE, extract_metadata = FALSE)

Arguments

`x`	A vector of municipality codes to extract. For convenience, climaemet provides this data on the dataset aemet_munic (see `municipio` field) as of January 2020.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.

Details

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

If extract_metadata = TRUE a simple tibble describing the value of each field of the forecast is returned.

Value

A nested tibble. Forecasted 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().

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)

# Vars 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
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)
    )
  )

climaemet documentation built on Aug. 30, 2023, 9:06 a.m.