wilsonfreitas/rbcb

rbcb

An interface to structure the information provided by the Brazilian Central Bank. This package interfaces the Brazilian Central Bank web services to provide data already formatted into R’s data structures.

Install

From CRAN:

install.packages("rbcb")

From github using remotes:

remotes::install_github('wilsonfreitas/rbcb')

Features

• rbcb
  • Install
  • Features
    • Usage
      • The get_series function
      • Market expectations
      • OLINDA API for currency rates
      • Currency rates
      • Cross currency rates

Usage

Load the package:

library(rbcb)

The get_series function

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: 9,434 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 9,424 more rows

Note 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 2022-07-12   5.41
#>  2 2022-07-13   5.40
#>  3 2022-07-14   5.46
#>  4 2022-07-15   5.40
#>  5 2022-07-18   5.37
#>  6 2022-07-19   5.39
#>  7 2022-07-20   5.43
#>  8 2022-07-21   5.48
#>  9 2022-07-22   5.45
#> 10 2022-07-25   5.41

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
#> 2021-07-01 0.96
#> 2021-08-01 0.87
#> 2021-09-01 1.16
#> 2021-10-01 1.25
#> 2021-11-01 0.95
#> 2021-12-01 0.73
#> 2022-01-01 0.54
#> 2022-02-01 1.01
#> 2022-03-01 1.62
#> 2022-04-01 1.06
#> 2022-05-01 0.47
#> 2022-06-01 0.67

or 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  Dec
#> 2021                               0.96 0.87 1.16 1.25 0.95 0.73
#> 2022 0.54 1.01 1.62 1.06 0.47 0.67

Multiple 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  Dec
#> 2021                               0.96 0.87 1.16 1.25 0.95 0.73
#> 2022 0.54 1.01 1.62 1.06 0.47 0.67                              
#> 
#> $IGPM
#>        Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov   Dec
#> 2021                                      0.78  0.66 -0.64  0.64  0.02  0.87
#> 2022  1.82  1.83  1.74  1.41  0.52  0.59

Market expectations

The function get_market_expectations returns market expectations discussed in the Focus Report that summarizes the statistics calculated from expectations collected from market practitioners.

The first argument type accepts the following values:

• annual: annual expectations
• quarterly: quarterly expectations
• monthly: monthly expectations
• top5s-monthly: monthly expectations for top 5 indicators
• top5s-annual: annual expectations for top 5 indicators
• inflation-12-months: inflation expectations for the next 12 months
• institutions: market expectations informed by financial institutions

The example below shows how to download IPCA’s monthly expectations.

rbcb::get_market_expectations("monthly", "IPCA", end_date = "2018-01-31", `$top` = 5)
#> # A tibble: 5 x 10
#>   Indicador Data       DataReferencia Media Mediana DesvioPadrao Minimo Maximo numeroRespondentes baseCalculo
#>   <chr>     <date>     <chr>          <dbl>   <dbl>        <dbl>  <dbl>  <dbl>              <int>       <int>
#> 1 IPCA      2018-01-31 06/2019         0.21    0.2          0.07   0.13   0.36                 14           1
#> 2 IPCA      2018-01-31 06/2019         0.2     0.2          0.1   -0.3    0.36                 43           0
#> 3 IPCA      2018-01-31 05/2019         0.31    0.29         0.06   0.22   0.43                 19           1
#> 4 IPCA      2018-01-31 05/2019         0.31    0.3          0.06   0.15   0.45                 55           0
#> 5 IPCA      2018-01-31 04/2019         0.38    0.39         0.1    0.16   0.61                 20           1

OLINDA API for currency rates

Use currency functions to download currency rates from the BCB OLINDA API.

olinda_list_currencies()
#>    symbol                     name currency_type
#> 1     AUD        Dólar australiano             B
#> 2     CAD          Dólar canadense             A
#> 3     CHF             Franco suíço             A
#> 4     DKK       Coroa dinamarquesa             A
#> 5     EUR                     Euro             B
#> 6     GBP          Libra Esterlina             B
#> 7     JPY                     Iene             A
#> 8     NOK         Coroa norueguesa             A
#> 9     SEK              Coroa sueca             A
#> 10    USD Dólar dos Estados Unidos             A

Use olinda_get_currency function to download data from specific currency by the currency symbol.

olinda_get_currency("USD", "2017-03-01", "2017-03-03")
#> # A tibble: 13 x 3
#>    datetime              bid   ask
#>    <dttm>              <dbl> <dbl>
#>  1 2017-03-01 14:37:41  3.10  3.10
#>  2 2017-03-01 15:37:01  3.10  3.10
#>  3 2017-03-01 15:37:01  3.10  3.10
#>  4 2017-03-02 10:04:33  3.11  3.11
#>  5 2017-03-02 11:07:36  3.10  3.10
#>  6 2017-03-02 12:10:41  3.12  3.12
#>  7 2017-03-02 13:06:27  3.12  3.12
#>  8 2017-03-02 13:06:27  3.11  3.11
#>  9 2017-03-03 10:10:38  3.13  3.13
#> 10 2017-03-03 11:10:48  3.13  3.13
#> 11 2017-03-03 12:07:35  3.14  3.14
#> 12 2017-03-03 13:07:10  3.14  3.14
#> 13 2017-03-03 13:07:10  3.14  3.14

The rates come quoted in BRL, so 3.10 is worth 1 USD in BRL.

Parity values

Type A currencies have parity values quoted in USD (1 CURRENCY in USD).

olinda_get_currency("CAD", "2017-03-01", "2017-03-01")
#> # A tibble: 3 x 3
#>   datetime              bid   ask
#>   <dttm>              <dbl> <dbl>
#> 1 2017-03-01 14:37:41  2.32  2.32
#> 2 2017-03-01 15:37:01  2.32  2.32
#> 3 2017-03-01 15:37:01  2.32  2.32

olinda_get_currency("CAD", "2017-03-01", "2017-03-01", parity = TRUE)
#> # A tibble: 3 x 3
#>   datetime              bid   ask
#>   <dttm>              <dbl> <dbl>
#> 1 2017-03-01 14:37:41  1.33  1.33
#> 2 2017-03-01 15:37:01  1.33  1.33
#> 3 2017-03-01 15:37:01  1.33  1.33

Type B currencies have parity values as 1 USD in CURRENCY, see AUD, for example.

olinda_get_currency("AUD", "2017-03-01", "2017-03-01")
#> # A tibble: 3 x 3
#>   datetime              bid   ask
#>   <dttm>              <dbl> <dbl>
#> 1 2017-03-01 14:37:41  2.38  2.38
#> 2 2017-03-01 15:37:01  2.38  2.38
#> 3 2017-03-01 15:37:01  2.38  2.38

olinda_get_currency("AUD", "2017-03-01", "2017-03-01", parity = TRUE)
#> # A tibble: 3 x 3
#>   datetime              bid   ask
#>   <dttm>              <dbl> <dbl>
#> 1 2017-03-01 14:37:41 0.768 0.768
#> 2 2017-03-01 15:37:01 0.767 0.768
#> 3 2017-03-01 15:37:01 0.767 0.768

Currency rates

Use 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  3.16

The 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.

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 0.0275

To 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 rows

There are 216 currencies available.

Cross currency rates

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 1.0000000

The rates are quoted by its columns labels, so the numbers in the BRL column are worth one currency unit in BRL.