tidyBdE is an R 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 point for searching time series is the time series catalog (index). You can search for any 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()| Numero_secuencial | Descripcion_de_la_serie |
|---|---|
| 573214 | Tipo de cambio. Libras esterlinas por euro (GBP/EUR).Datos diarios |
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) as follows:
seq_number <- xr_gbp |>
# Select the first record.
slice(1) |>
# Get the ID.
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 rowsPlot 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 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") # Custom package palette.
Figure 2: Spanish Economic Indicators (2010-2019)
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)