Get started with tidyBdE
Description: Introduction to tidyBdE

tidyBdE is an API package that retrieves data from Banco de España. The data is returned as a tibble, and the package automatically detects the format of each time series (dates, characters, and numbers).

## Search series

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

The basic entry points for searching time series are the time series catalogs (indexes). You can search for 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) |>
      # Display table in the document
      knitr::kable()

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

Table 1: Search results

Note: BdE metadata is currently only provided in Spanish, as the institution is working on an English version. Search terms must be provided in Spanish to retrieve results.

Once you have found a series, load the GBP/EUR exchange rate using the sequential number reference (`Numero_Secuencial`):

    seq_number <- xr_gbp |>
      # First record
      slice(1) |>
      # Get the ID
      select(Numero_secuencial) |>
      # Convert to numeric
      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()

    time_series
    #> # A tibble: 2,816 × 2
    #>    Date       EUR_GBP_XR
    #>    <date>          <dbl>
    #>  1 2010-01-04      0.891
    #>  2 2010-01-05      0.900
    #>  3 2010-01-06      0.899
    #>  4 2010-01-07      0.900
    #>  5 2010-01-08      0.893
    #>  6 2010-01-11      0.899
    #>  7 2010-01-12      0.897
    #>  8 2010-01-13      0.895
    #>  9 2010-01-14      0.890
    #> 10 2010-01-15      0.881
    #> # ℹ 2,806 more rows

## Plot series

The package also provides a custom ggplot2 theme based on BdE’s publications:

    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 = 0.95,
        label = "Brexit"
      )) +
      coord_cartesian(ylim = c(0.7, 1)) +
      theme_tidybde()

Figure 1: EUR/GBP Exchange Rate (2010-2020)

The package also provides convenience functions for a selection of the most relevant macroeconomic series, eliminating the need for manual searching:

    # 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

Figure 2: Spanish Economic Indicators (2010-2019)

## A note on caching

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

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

When this option is set, tidyBdE will look for cached files in the `bde_cache_dir` directory and load them, speeding up data retrieval.

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

    bde_catalog_update()

    # Or use update_cache = TRUE in most functions

    bde_series_load("SOME ID", update_cache = TRUE)