Rbcb
R interface to Brazilian Central Bank web services
Install / Use
/learn @wilsonfreitas/RbcbREADME
rbcb <img src="man/figures/logo.png" align="right" width="120" />
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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
Usage
Load the package:
library(rbcb)
The get_series function
<a name="single-series"></a> 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.
<a name="tibble-objects"></a>
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
<a name="download-types"></a> 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.
<a name="xts-objects"></a>
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.
<a name="ts-objects"></a>
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
<a name="multiple-series"></a> 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
#>
#> $IGPM
#> Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
#> 2021 0.78 0.66 -0.64 0.64 0.02 0.87
#> 2022 1.82 1.83 1.74 1.41 0.52 0.59
Market expectations
<a name="market-expectations"></a> The function
get_market_expectations returns market expectations discussed in the
Focus Report that summarizes the statistics calculated from expectations
collected from market practitioners.
The first argument type accepts the following values:
annual: annual expectationsquarterly: quarterly expectationsmonthly: monthly expectationstop5s-monthly: monthly expectations for top 5 indicatorstop5s-annual: annual expectations for top 5 indicatorsinflation-12-months: inflation expectations for the next 12 monthsinstitutions: market expectations informed by financial institutions
The example below shows how to download IPCA’s monthly expectations.
rbcb::get_market_expectations("monthly", "IPCA", end_date = "2018-01-31", `$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
<a name="olinda-currency-rates"></a> 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
<a name="currency-rates"></a> 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
