tidyBdE

tidyBdE is an R package that retrieves time series data from Banco de España bulk CSV files and the Statistics web service (API). Data are returned as tibble objects. The package infers date, character and numeric column types where possible. Bulk CSV functions use stable sequential numbers (Numero_secuencial), while Statistics web service functions use Nombre_de_la_serie API series codes.

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) publishes numerous time series produced by the institution or compiled from other sources, such as Eurostat or INE.

Catalog metadata is the main entry point for discovering time series. 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 metadata.
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

After finding a time series, you can load the GBP/EUR exchange rate from bulk CSV files using its stable sequential number (Numero_secuencial):

seq_number <- xr_gbp |>
  # Select the first record.
  slice(1) |>
  # Get the stable sequential number.
  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 tidyBdE palette.

Palettes

Three custom palettes based on colors used in selected BdE publications are available.

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

Set the bde_cache_dir option to create a local cache:

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

When this option is set, tidyBdE looks for cached bulk CSV 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(573214, 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 Banco de España (BdE) time series data as tibble objects from bulk CSV files and the Statistics web service (API). Bulk CSV functions use stable BdE sequential numbers, while API functions use API series codes. Catalog functions support discovery and local caching. Plotting helpers provide ggplot2 palettes, scales and themes. 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.},
}