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Forecast database by municipality

Source: R/aemet_forecast_daily.R, R/aemet_forecast_hourly.R
aemet_forecast.Rd

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

Usage

aemet_forecast_daily(x, verbose = FALSE)

aemet_forecast_hourly(x, verbose = 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.

Value

A nested tibble. Forecasted values can be extracted with aemet_forecast_tidy(). See also Details

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.

API Key

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

See also

aemet_munic for municipality codes.

Other aemet_api_data: aemet_daily_clim(), aemet_extremes_clim(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Other forecasts: aemet_forecast_tidy()

Examples


# Select a city
data("aemet_munic")
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
munis <- aemet_munic %>%
  filter(municipio_nombre %in% c(
    "Santiago de Compostela",
    "Lugo"
  )) %>%
  pull(municipio)

daily <- aemet_forecast_daily(munis)

# Vars available
aemet_forecast_vars_available(daily)
#> [1] "probPrecipitacion" "cotaNieveProv"     "estadoCielo"      
#> [4] "viento"            "rachaMax"          "temperatura"      
#> [7] "sensTermica"       "humedadRelativa"  


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)
#> # A tibble: 14 × 4
#>    municipio fecha      nombre                 temperatura$maxima $minima $dato 
#>    <chr>     <date>     <chr>                               <int>   <int> <list>
#>  1 15078     2023-03-29 Santiago de Compostela                 20      12 <df>  
#>  2 15078     2023-03-30 Santiago de Compostela                 17      13 <df>  
#>  3 15078     2023-03-31 Santiago de Compostela                 15      11 <df>  
#>  4 15078     2023-04-01 Santiago de Compostela                 16       7 <df>  
#>  5 15078     2023-04-02 Santiago de Compostela                 15       4 <df>  
#>  6 15078     2023-04-03 Santiago de Compostela                 18       6 <df>  
#>  7 15078     2023-04-04 Santiago de Compostela                 21       6 <df>  
#>  8 27028     2023-03-29 Lugo                                   24      12 <df>  
#>  9 27028     2023-03-30 Lugo                                   21      10 <df>  
#> 10 27028     2023-03-31 Lugo                                   16       9 <df>  
#> 11 27028     2023-04-01 Lugo                                   16       4 <df>  
#> 12 27028     2023-04-02 Lugo                                   14       2 <df>  
#> 13 27028     2023-04-03 Lugo                                   17       3 <df>  
#> 14 27028     2023-04-04 Lugo                                   20       2 <df>  

# Select and unnest
daily_temp <- aemet_forecast_tidy(daily, "temperatura")

# This is not
daily_temp
#> # A tibble: 14 × 14
#>    elaborado           municipio nombre provincia id    version uvMax fecha     
#>    <dttm>              <chr>     <chr>  <chr>     <chr>   <dbl> <int> <date>    
#>  1 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1     6 2023-03-29
#>  2 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1     5 2023-03-30
#>  3 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1     5 2023-03-31
#>  4 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1     5 2023-04-01
#>  5 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1     6 2023-04-02
#>  6 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1    NA 2023-04-03
#>  7 2023-03-29 12:09:32 15078     Santi… A Coruña  15078       1    NA 2023-04-04
#>  8 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1     6 2023-03-29
#>  9 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1     5 2023-03-30
#> 10 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1     5 2023-03-31
#> 11 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1     5 2023-04-01
#> 12 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1     6 2023-04-02
#> 13 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1    NA 2023-04-03
#> 14 2023-03-29 12:09:43 27028     Lugo   Lugo      27028       1    NA 2023-04-04
#> # ℹ 6 more variables: temperatura_maxima <int>, temperatura_minima <int>,
#> #   temperatura_6 <int>, temperatura_12 <int>, temperatura_18 <int>,
#> #   temperatura_24 <int>

# 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)
    )
  )