---
title: "Get Started with tidyBdE"
output: rmarkdown::html_vignette
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%\VignetteIndexEntry{Get Started with tidyBdE}
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---
**tidyBdE** is an API package that helps to retrieve data from [Banco de
España](https://www.bde.es/webbe/en/estadisticas/recursos/descargas-completas.html).
The data is returned as a [`tibble`](https://tibble.tidyverse.org/) and the
package tries to guess the format of every time-series (dates, characters and
numbers).
## Search series
Banco de España (**BdE**) provides several time-series, either produced by the
institution itself or compiled for another sources, as
[Eurostat](https://ec.europa.eu/eurostat) or [INE](https://www.ine.es/).
The basic entry point for searching time-series are the catalogs (*indexes*) of
information. You can search any series by name:
``` r
library(tidyBdE)
library(ggplot2)
library(dplyr)
library(tidyr)
# Search GBP on "TC" (exchange rate) catalog
XR_GBP <- bde_catalog_search("GBP", catalog = "TC")
XR_GBP %>%
select(Numero_secuencial, Descripcion_de_la_serie) %>%
# To table on document
knitr::kable()
```
| Numero_secuencial|Descripcion_de_la_serie |
|-----------------:|:------------------------------------------------------------------|
| 573214|Tipo de cambio. Libras esterlinas por euro (GBP/EUR).Datos diarios |
**Note that BdE files are only provided in Spanish, for the time being**, the
organism is working on the English version. By now, search terms should be
provided in Spanish in order to get search results.
After we have found our series, we can load the series for the GBP/EUR exchange
rate using the sequential number reference (`Numero_Secuencial`) as:
``` r
seq_number <- XR_GBP %>%
# First record
slice(1) %>%
# Get id
select(Numero_secuencial) %>%
# Convert to num
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()
```
## Plot series
The package also provides a custom **ggplot2** theme based on the publications
of BdE:
``` r
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 = .95,
label = "Brexit"
)) +
coord_cartesian(ylim = c(0.7, 1)) +
theme_tidybde()
```
The package provides also several "shortcut" functions for a selection of the
most relevant macroeconomic series, so there is no need to look for them in
advance:
``` r
# 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 palette on the package
```
## A note on caching
You can use **tidyBdE** to create your own local repository at a given local
directory passing the following option:
``` r
options(bde_cache_dir = "./path/to/location")
```
When this option is set, **tidyBdE** would look for the cached file on the
`bde_cache_dir` directory and it will load it, speeding up the process.
It is possible to update the data (i.e. after every monthly or quarterly data
release) with the following commands:
``` r
bde_catalog_update()
# On most of the functions using the option update_cache = TRUE
bde_series_load("SOME ID", update_cache = TRUE)
```
