An interface to structure the information provided by the Brazilian Centra Bank. This package interfaces the Brazilian Central Bank web services to provide data already formatted into R's data structures and download currency data from Brazilian Centra Bank web site.
This package is only available to install from github using devtools:
devtools::install_github('wilsonfreitas/rbcb')- Download single series
- Download multiple series
- Different download types
- Download
tibbleobjects - Download
xtsobjects - Download
tsobjects - Download currency rates
- Download cross currency rates
- Download market expectations
Download the series by calling rbcb::get_series and pass the time series code is as the first argument.
For example, let's download the USDBRL time series which code is 1.
rbcb::get_series(c(USDBRL = 1))
#> # A tibble: 8,412 x 2
#> date USDBRL
#> * <date> <dbl>
#> 1 1984-11-28 2828
#> 2 1984-11-29 2828
#> 3 1984-11-30 2881
#> 4 1984-12-03 2881
#> 5 1984-12-04 2881
#> 6 1984-12-05 2923
#> 7 1984-12-06 2923
#> 8 1984-12-07 2923
#> 9 1984-12-10 2965
#> 10 1984-12-11 2965
#> # ... with 8,402 more rowsNote that this series starts at 1984 and has approximately 8000 rows.
Also note that you can name the downloaded series by passing a named vector in the code argument.
To download recent values you should use the argument last = N, see below.
rbcb::get_series(c(USDBRL = 1), last = 10)
#> # A tibble: 10 x 2
#> date USDBRL
#> * <date> <dbl>
#> 1 2018-06-18 3.75
#> 2 2018-06-19 3.76
#> 3 2018-06-20 3.73
#> 4 2018-06-21 3.79
#> 5 2018-06-22 3.77
#> 6 2018-06-25 3.78
#> 7 2018-06-26 3.77
#> 8 2018-06-27 3.84
#> 9 2018-06-28 3.85
#> 10 2018-06-29 3.86
The series can be downloaded in many different types: tibble, xts, ts or data.frame, but the default is tibble.
See the next example where the Brazilian Broad Consumer Price Index (IPCA) is downloaded as xts object.
rbcb::get_series(c(IPCA = 433), last = 12, as = "xts")
#> IPCA
#> 2017-06-01 -0.23
#> 2017-07-01 0.24
#> 2017-08-01 0.19
#> 2017-09-01 0.16
#> 2017-10-01 0.42
#> 2017-11-01 0.28
#> 2017-12-01 0.44
#> 2018-01-01 0.29
#> 2018-02-01 0.32
#> 2018-03-01 0.09
#> 2018-04-01 0.22
#> 2018-05-01 0.40or as a ts object.
rbcb::get_series(c(IPCA = 433), last = 12, as = "ts")
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
#> 2017 -0.23 0.24 0.19 0.16 0.42 0.28
#> 2018 0.29 0.32 0.09 0.22 0.40
#> Dec
#> 2017 0.44
#> 2018Multiple series can be downloaded at once by passing a named vector with the series codes. The return is a named list with the downloaded series.
rbcb::get_series(c(IPCA = 433, IGPM = 189), last = 12, as = "ts")
#> $IPCA
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
#> 2017 -0.23 0.24 0.19 0.16 0.42 0.28
#> 2018 0.29 0.32 0.09 0.22 0.40
#> Dec
#> 2017 0.44
#> 2018
#>
#> $IGPM
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov
#> 2017 -0.67 -0.72 0.10 0.47 0.20 0.52
#> 2018 0.76 0.07 0.64 0.57 1.38
#> Dec
#> 2017 0.89
#> 2018Use currency functions to download currency rates from the BCB web site.
rbcb::get_currency("USD", "2017-03-01", "2017-03-10")
#> # A tibble: 8 x 3
#> date bid ask
#> <date> <dbl> <dbl>
#> 1 2017-03-01 3.10 3.10
#> 2 2017-03-02 3.11 3.11
#> 3 2017-03-03 3.14 3.14
#> 4 2017-03-06 3.11 3.11
#> 5 2017-03-07 3.12 3.12
#> 6 2017-03-08 3.15 3.15
#> 7 2017-03-09 3.17 3.17
#> 8 2017-03-10 3.16 3.16The rates come quoted in BRL, so 3.0970 is worth 1 USD in BRL.
All currency time series have an attribute called symbol that stores its own
currency name.
attr(rbcb::get_currency("USD", "2017-03-01", "2017-03-10"), "symbol")
#> [1] "USD"Trying another currency.
library(rbcb)
library(magrittr)
get_currency("JPY", "2017-03-01", "2017-03-10") %>% Ask()
#> # A tibble: 8 x 2
#> date JPY
#> <date> <dbl>
#> 1 2017-03-01 0.0273
#> 2 2017-03-02 0.0272
#> 3 2017-03-03 0.0274
#> 4 2017-03-06 0.0274
#> 5 2017-03-07 0.0274
#> 6 2017-03-08 0.0274
#> 7 2017-03-09 0.0276
#> 8 2017-03-10 0.0275To see the avaliable currencies call list_currencies.
rbcb::list_currencies()
#> # A tibble: 218 x 5
#> name code symbol country_name country_code
#> * <chr> <dbl> <chr> <chr> <dbl>
#> 1 AFEGANE AFEGANIST 5 AFN AFEGANISTAO 132
#> 2 RANDE/AFRICA SUL 785 ZAR AFRICA DO SUL 7560
#> 3 LEK ALBANIA REP 490 ALL ALBANIA, REPUBLICA DA 175
#> 4 EURO 978 EUR ALEMANHA 230
#> 5 KWANZA/ANGOLA 635 AOA ANGOLA 400
#> 6 DOLAR CARIBE ORIENTAL 215 XCD ANGUILLA 418
#> 7 DOLAR CARIBE ORIENTAL 215 XCD ANTIGUA E BARBUDA 434
#> 8 RIAL/ARAB SAUDITA 820 SAR ARABIA SAUDITA 531
#> 9 DINAR ARGELINO 95 DZD ARGELIA 590
#> 10 PESO ARGENTINO 706 ARS ARGENTINA 639
#> # ... with 208 more rowsThere are 216 currencies available.
The API provides a matrix with the relations between exchange rates, this is the matrix of cross currency rates. This is a square matrix with the all exchange rates between all currencies.
x <- rbcb::get_currency_cross_rates("2017-03-10")
dim(x)
#> [1] 156 156
# Since there are many currencies it is interesting to subset the matrix.
cr <- c("USD", "BRL", "EUR", "CAD")
x[cr, cr]
#> USD BRL EUR CAD
#> USD 1.0000000 3.1623 0.9380896 1.3465764
#> BRL 0.3162255 1.0000 0.2966479 0.4258218
#> EUR 1.0659963 3.3710 1.0000000 1.4354454
#> CAD 0.7426240 2.3484 0.6966479 1.0000000The rates are quoted by its columns labels, so the numbers in the BRL column are worth one currency unit in BRL.
There are six functions to get market expectations data.
get_monthly_market_expectationsget_quarterly_market_expectationsget_annual_market_expectationsget_top5s_monthly_market_expectationsget_top5s_annual_market_expectationsget_twelve_months_inflation_expectations
rbcb::get_monthly_market_expectations("IPCA", end_date = "2018-01-31", `$top` = 5)
#> # A tibble: 5 x 9
#> indic date reference_month mean median sd coefvar min max
#> * <chr> <date> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 IPCA 2018-01-31 2019-01 0.48 0.5 0.07 14.6 0.33 0.65
#> 2 IPCA 2018-01-31 2019-02 0.46 0.46 0.07 15.2 0.26 0.68
#> 3 IPCA 2018-01-31 2018-09 0.290 0.28 0.06 20.7 0.14 0.45
#> 4 IPCA 2018-01-31 2019-04 0.38 0.4 0.08 21.0 0.16 0.61
#> 5 IPCA 2018-01-31 2018-08 0.19 0.2 0.08 42.1 0.05 0.47