tidyBdE

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

Installation

Install tidyBdE from CRAN:

install.packages("tidyBdE")

You can install the development version of tidyBdE with:

pak::pak("ropenspain/tidyBdE")

Alternatively, you can install tidyBdE from the r-universe:

# Install tidyBdE in R:
install.packages(
  "tidyBdE",
  repos = c(
    "https://ropenspain.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

Examples

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 point for searching series is the time series catalog. You can search for any series by name:

library(tidyBdE)

# Load packages for data handling and plotting.
library(ggplot2)
library(dplyr)
library(tidyr)

# Search for GBP in the "TC" (exchange rate) catalog.
xr_gbp <- bde_catalog_search("GBP", catalog = "TC")

xr_gbp |>
  select(Numero_secuencial, Descripcion_de_la_serie) |>
  # Display the 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, so search terms must be provided in Spanish to retrieve results. The institution is working on an English version.

Once you have found a series, you can load the GBP/EUR exchange rate using the sequential number reference (Numero_secuencial) as follows:

seq_number <- xr_gbp |>
  # Select the first record.
  slice(1) |>
  # Get the series ID.
  select(Numero_secuencial) |>
  # Convert to numeric.
  as.double()

# Extract the series.
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

Plots

The package also provides a custom ggplot2 theme based on BdE 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()

The package also provides convenience functions for selected macroeconomic series, so you do not need to search 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") # Use a custom package palette.

Palettes

Three custom palettes, based on those used by BdE in some publications, are available.

Apply these palettes to ggplot2 plots using the scale functions provided in the package (see help("scale_color_bde_d", package = "tidyBdE")).

A note on caching

You can use tidyBdE to create a local cache by setting the following option:

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

When this option is set, tidyBdE looks for cached files in the bde_cache_dir directory and loads them, speeding up data retrieval.

You can update the data after monthly or quarterly releases with the following commands:

bde_catalog_update()

# Or use `update_cache = TRUE` in most functions.

bde_series_load("SOME ID", update_cache = TRUE)

Disclaimer

This package is in no way sponsored, endorsed or administered by Banco de España.

Citation

H. Herrero D (2026). tidyBdE: Retrieve Data from Banco de España. doi:10.32614/CRAN.package.tidyBdE. https://ropenspain.github.io/tidyBdE/.

A BibTeX entry for LaTeX users is:

@Manual{R-tidyBdE,
  title = {{tidyBdE}: Retrieve Data from Banco de España},
  doi = {10.32614/CRAN.package.tidyBdE},
  author = {Diego {H. Herrero}},
  year = {2026},
  version = {0.6.0.9000},
  url = {https://ropenspain.github.io/tidyBdE/},
  abstract = {Tools for retrieving time series data from Banco de España (BdE) and returning results as tibble objects. Banco de España is the national central bank and, within the framework of the Single Supervisory Mechanism (SSM), the supervisor of the Spanish banking system alongside the European Central Bank. This package is in no way sponsored, endorsed or administered by Banco de España.},
}