Get started with 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.

Search time series

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)

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()

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, 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.
  pull(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 time series

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

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

The package also provides convenience functions for selected Spanish macroeconomic indicators, so you do not need to search 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.
Figure 2: Spanish Economic Indicators (2010-2019)

Figure 2: Spanish Economic Indicators (2010-2019)

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)