tidyBdE

tidyBdE is an R package that retrieves data from Banco de España. Data are returned as tibble objects. The package automatically detects the format of each time series field, including dates, character fields and numeric fields.

Important

This package is not sponsored, endorsed or administered by Banco de España.

Installation

Install tidyBdE from CRAN:

install.packages("tidyBdE")

Install the development version of tidyBdE from GitHub with:

pak::pak("ropenspain/tidyBdE")

Alternatively, install tidyBdE from 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 or compiled from other sources, such as Eurostat or INE.

The basic entry point for searching time series is the catalog. You can search for time 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 available in Spanish only, so search terms must be in Spanish to retrieve results. The institution is working on an English version.

After finding a time series, you can load the GBP/EUR exchange rate using the sequential number reference (Numero_secuencial):

seq_number <- xr_gbp |>
  # Select the first record.
  slice(1) |>
  # Get the series code.
  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 Spanish macroeconomic indicators, so you do not need to search for them manually:

# 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 are available. They are based on colors used by BdE in some publications.

Apply these palettes to ggplot2 plots with the scale functions provided by the package. See help("scale_color_bde_d", package = "tidyBdE").

A note on caching

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 to speed up data retrieval.

Update cached 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)

Citation

H. Herrero D (2026). tidyBdE: Retrieve Time Series 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 Time Series Data from Banco de España},
  doi = {10.32614/CRAN.package.tidyBdE},
  author = {Diego {H. Herrero}},
  year = {2026},
  version = {0.6.1.9000},
  url = {https://ropenspain.github.io/tidyBdE/},
  abstract = {Tools for retrieving time series data from Banco de España (BdE) 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 not sponsored, endorsed or administered by Banco de España.},
}