Helper functions for extracting forecasts

Helpers for aemet_forecast_daily() and aemet_forecast_hourly():

• aemet_forecast_vars_available() extracts the values available on 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

x

A database extracted with aemet_forecast_daily() or aemet_forecast_hourly().

var

Name of the desired var to extract

Value

A vector of characters (aemet_forecast_vars_available()) or a tibble (aemet_forecast_tidy()).

See also

Other forecasts: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires()

Examples

# Hourly values
hourly <- aemet_forecast_hourly(c("15030", "28080"))

# Vars available
aemet_forecast_vars_available(hourly)
#>  [1] "estadoCielo"       "precipitacion"     "probPrecipitacion"
#>  [4] "probTormenta"      "nieve"             "probNieve"        
#>  [7] "temperatura"       "sensTermica"       "humedadRelativa"  
#> [10] "vientoAndRachaMax"

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

library(dplyr)
# Make hour - Need lubridate to adjust timezones
temp_end <- temp %>%
  mutate(
    forecast_time = lubridate::force_tz(
      as.POSIXct(fecha) + hora,
      tz = "Europe/Madrid"
    )
  )

# Add also 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 = .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
    ))
  )