# Get Started with tidyBdE

tidyBdE is an API package that helps to retrieve data from Banco de España. The data is returned as a `tibble` and the package tries to guess the format of every time-series (dates, characters and numbers).

## Search series

Banco de España (BdE) provides several time-series, either produced by the institution itself or compiled for another sources, as Eurostat or INE.

The basic entry point for searching time-series are the catalogs (indexes) of information. You can search any series by name:

    library(tidyBdE)

    library(ggplot2)
    library(dplyr)
    library(tidyr)

    # Search GBP on "TC" (exchange rate) catalog
    XR_GBP <- bde_catalog_search("GBP", catalog = "TC")

    XR_GBP %>%
      select(Numero_secuencial, Descripcion_de_la_serie) %>%
      # To table on document
      knitr::kable()

Numero_secuencial | Descripcion_de_la_serie
--- | ---
573214 | Tipo de cambio. Libras esterlinas por euro (GBP/EUR).Datos diarios

Note that BdE files are only provided in Spanish, for the time being, the organism is working on the English version. By now, search terms should be provided in Spanish in order to get search results.

After we have found our series, we can load the series for the GBP/EUR exchange rate using the sequential number reference (`Numero_Secuencial`) as:

    seq_number <- XR_GBP %>%
      # First record
      slice(1) %>%
      # Get id
      select(Numero_secuencial) %>%
      # Convert to num
      as.double()

    seq_number
    #> [1] 573214

    time_series <- bde_series_load(seq_number, series_label = "EUR_GBP_XR") %>%
      filter(Date >= "2010-01-01" & Date <= "2020-12-31") %>%
      drop_na()

## Plot series

The package also provides a custom ggplot2 theme based on the publications of BdE:

    ggplot(time_series, aes(x = Date, y = EUR_GBP_XR)) +
      geom_line(colour = bde_tidy_palettes(n = 1)) +
      geom_smooth(method = "gam", colour = bde_tidy_palettes(n = 2)[2]) +
      labs(
        title = "EUR/GBP Exchange Rate (2010-2020)",
        subtitle = "%",
        caption = "Source: BdE"
      ) +
      geom_vline(
        xintercept = as.Date("2016-06-23"),
        linetype = "dotted"
      ) +
      geom_label(aes(
        x = as.Date("2016-06-23"),
        y = .95,
        label = "Brexit"
      )) +
      coord_cartesian(ylim = c(0.7, 1)) +
      theme_tidybde()

EUR/GBP Exchange Rate (2010-2020)

The package provides also several “shortcut” functions for a selection of the most relevant macroeconomic series, so there is no need to look for them in advance:

    # Data in "long" format

    plotseries <- bde_ind_gdp_var("GDP YoY", out_format = "long") %>%
      bind_rows(
        bde_ind_unemployment_rate("Unemployment Rate", out_format = "long")
      ) %>%
      drop_na() %>%
      filter(Date >= "2010-01-01" & Date <= "2019-12-31")

    ggplot(plotseries, aes(x = Date, y = serie_value)) +
      geom_line(aes(color = serie_name), linewidth = 1) +
      labs(
        title = "Spanish Economic Indicators (2010-2019)",
        subtitle = "%",
        caption = "Source: BdE"
      ) +
      theme_tidybde() +
      scale_color_bde_d(palette = "bde_vivid_pal") # Custom palette on the package

Spanish Economic Indicators (2010-2019)

## A note on caching

You can use tidyBdE to create your own local repository at a given local directory passing the following option:

    options(bde_cache_dir = "./path/to/location")

When this option is set, tidyBdE would look for the cached file on the `bde_cache_dir` directory and it will load it, speeding up the process.

It is possible to update the data (i.e. after every monthly or quarterly data release) with the following commands:

    bde_catalog_update()

    # On most of the functions using the option update_cache = TRUE

    bde_series_load("SOME ID", update_cache = TRUE)