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The Central Bank of Brazil (BCB) offers access to its SGS system (sistema gerenciador de series temporais) with a official API available here.
With time, I find myself using more and more of the available datasets in my regular research and studies. Over last weekend I decided to write my own API package that would make my life a lot easier.
Package GetBCBData can fetch data efficiently and rapidly:
- Use of a caching system with package
memoiseto speed up repeated requests of data; - Users can utilize all cores of the machine (parallel computing) when fetching a large batch of time series;
- Allows the choice for format output: long (row oriented, tidy data) or wide (column oriented)
- Error handling internally. Even if requested series does not exist, the function will still return all results.
Installation
# CRAN (official release) - IN CHECK
install.packages('GetBCBData')
# Github (dev version)
devtools::install_github('msperlin/GetBCBData')
A simple example
Let’s have a look at unemployment rates around the world. After searching for the ids in the SGS system, we find the ids for 6 countries and set it as input id.
Now, lets download the data with GetBCBData:
#devtools::install_github('msperlin/GetBCBData')
library(GetBCBData)
library(tidyverse)
my.countries <- c('Germany', 'Canada', 'USA',
'France', 'Italy', 'Japan')
my.ids <- c(3785:3790)
names(my.ids) <- paste0('Unemp. rate - ', my.countries)
df.bcb <- gbcbd_get_series(id = my.ids ,
first.date = '2000-01-01',
last.date = Sys.Date())
##
## Fetching Unemp. rate - Germany [3785] from BCB-SGS with cache
## Found 199 observations
## Fetching Unemp. rate - Canada [3786] from BCB-SGS with cache
## Found 200 observations
## Fetching Unemp. rate - USA [3787] from BCB-SGS with cache
## Found 200 observations
## Fetching Unemp. rate - France [3788] from BCB-SGS with cache
## Found 199 observations
## Fetching Unemp. rate - Italy [3789] from BCB-SGS with cache
## Found 199 observations
## Fetching Unemp. rate - Japan [3790] from BCB-SGS with cache
## Found 199 observations
glimpse(df.bcb)
## Observations: 1,196
## Variables: 4
## $ ref.date <date> 2000-01-01, 2000-02-01, 2000-03-01, 2000-04-01, 200…
## $ value <dbl> 8.2, 8.1, 8.1, 8.0, 8.0, 8.0, 7.9, 7.9, 7.9, 7.8, 7.…
## $ id.num <int> 3785, 3785, 3785, 3785, 3785, 3785, 3785, 3785, 3785…
## $ series.name <chr> "Unemp. rate - Germany", "Unemp. rate - Germany", "U…
p <- ggplot(df.bcb, aes(x = ref.date, y = value) ) +
geom_line() +
labs(title = 'Unemploymnent Rates Around the World',
subtitle = paste0(min(df.bcb$ref.date), ' to ', max(df.bcb$ref.date)),
x = '', y = 'Percentage*100') + facet_wrap(~series.name)
print(p)
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