---
title: "Get Started with tidyBdE"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Get Started with tidyBdE}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- tidyBdE.Rmd is generated from tidyBdE.Rmd.orig. Please edit that file -->



**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()
```

<div class="figure">
<img src="./chart-1.png" alt="EUR/GBP Exchange Rate (2010-2020)" width="100%" />
<p class="caption">EUR/GBP Exchange Rate (2010-2020)</p>
</div>

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
```

<div class="figure">
<img src="./macroseries-1.png" alt="Spanish Economic Indicators (2010-2019)" width="100%" />
<p class="caption">Spanish Economic Indicators (2010-2019)</p>
</div>

## 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)
```