Package 'brfinance'

Description

A comprehensive database of Brazilian economic and financial time series available through the Central Bank of Brazil's SGS system.

Usage

br_available_series

Format

A tibble with multiple rows and the following columns:

series_id

SGS code from Central Bank of Brazil

source

Data source

source_system

Source system

short_name

Short name of the series

long_name_pt

Full name in Portuguese

long_name_en

Full name in English

description_pt

Description in Portuguese

description_en

Description in English

category_pt

Category in Portuguese

category_en

Category in English

sub_category_pt

Sub-category in Portuguese

sub_category_en

Sub-category in English

frequency

Data frequency

unit

Measurement unit

seasonally_adjusted

Logical

start_date

Series start date

end_date

Series end date

default_transform

Suggested transformation

suggested_scale

Suggested visualization scale

notes_pt

Notes in Portuguese

notes_en

Notes in English

Source

Central Bank of Brazil — SGS

Examples

br_available_series

br_available_series[br_available_series$series_id == "433", ]

br_available_series[br_available_series$series_id == "433", ]

Description

Displays a searchable table of available Brazilian economic series.

Usage

browse_series(
  category = NULL,
  sub_category = NULL,
  source = NULL,
  frequency = NULL,
  search_text = NULL,
  language = c("pt", "en")
)

Arguments

Value

A tibble with available series matching the criteria

Examples

browse_series()
browse_series(category = "Macroeconomic")
browse_series(search_text = "inflação")

Description

Generates an amortization schedule for a loan.

Usage

calc_amortization_schedule(pv, rate, n, type = 0)

Arguments

Value

Data frame with amortization schedule.

Examples

calc_amortization_schedule(pv = 10000, rate = 0.01, n = 12)

Description

Calculates the final value under compound interest.

Usage

calc_compound_interest(pv, rate, n)

Arguments

Value

Final value with compound interest.

Examples

calc_compound_interest(pv = 1000, rate = 0.10, n = 2)

Description

Calculates the future value with continuous compounding.

Usage

calc_continuous_compounding(pv, rate, t)

Arguments

Value

Future value with continuous compounding.

Examples

calc_continuous_compounding(pv = 1000, rate = 0.05, t = 2)

Description

Converts a nominal interest rate into an effective rate.

Usage

calc_effective_rate(nominal_rate, m)

Arguments

Value

Effective interest rate.

Examples

calc_effective_rate(nominal_rate = 0.12, m = 12) # 12% nominal monthly

Description

Calculates the future value of a present amount given an interest rate and number of periods.

Usage

calc_future_value(pv, rate, n)

Arguments

Value

Future value.

Examples

calc_future_value(pv = 1000, rate = 0.10, n = 1)
calc_future_value(pv = 500, rate = 0.05, n = 10)

Description

Calculates the future value with optional periodic payments.

Usage

calc_future_value_ext(pv, rate, n, pmt = 0, type = 0)

Arguments

Value

Future value.

Examples

calc_future_value_ext(pv = 1000, rate = 0.05, n = 10, pmt = 100)
calc_future_value_ext(pv = 0, rate = 0.01, n = 12, pmt = 500, type = 1)

Description

Calculates the future value of a series of equal payments.

Usage

calc_fv_annuity(pmt, rate, n, type = 0)

Arguments

Value

Future value of the annuity.

Examples

calc_fv_annuity(pmt = 100, rate = 0.05, n = 10, type = 0)
calc_fv_annuity(pmt = 100, rate = 0.05, n = 10, type = 1)

Description

Calculates the internal rate of return of a series of cash flows.

Usage

calc_irr(cashflows, guess = 0.1)

Arguments

Value

Internal rate of return.

Examples

calc_irr(cashflows = c(-1000, 300, 400, 500))

Description

Converts an effective interest rate into a nominal rate.

Usage

calc_nominal_rate(effective_rate, m)

Arguments

Value

Nominal interest rate.

Examples

calc_nominal_rate(effective_rate = 0.1268, m = 12)

Description

Calculates the number of periods for an investment.

Usage

calc_nper(pv, fv = 0, rate, pmt = 0, type = 0)

Arguments

Value

Number of periods.

Examples

calc_nper(pv = -1000, fv = 2000, rate = 0.05)
calc_nper(pv = 0, fv = 100000, rate = 0.01, pmt = -500)

Description

Calculates the net present value of a series of cash flows.

Usage

calc_npv(rate, cashflows)

Arguments

Value

Net present value.

Examples

calc_npv(rate = 0.1, cashflows = c(-1000, 300, 400, 500))

Description

Calculates the periodic payment for a loan.

Usage

calc_pmt(pv, rate, n, type = 0)

Arguments

Value

Periodic payment amount.

Examples

calc_pmt(pv = 10000, rate = 0.01, n = 12) # Loan de R$10k, 1% ao mês, 12 meses

Description

Calculates the present value of a future amount given an interest rate and number of periods.

Usage

calc_present_value(fv, rate, n)

Arguments

Value

Present value.

Examples

calc_present_value(fv = 1100, rate = 0.10, n = 1)
calc_present_value(fv = 1000, rate = 0.05, n = 5)

Description

Calculates the present value of a series of equal payments.

Usage

calc_pv_annuity(pmt, rate, n, type = 0)

Arguments

Value

Present value of the annuity.

Examples

calc_pv_annuity(pmt = 100, rate = 0.05, n = 10)
calc_pv_annuity(pmt = 100, rate = 0.05, n = 10, type = 1)

Description

Calculates the present value with continuous compounding.

Usage

calc_pv_continuous(fv, rate, t)

Arguments

Value

Present value with continuous compounding.

Examples

calc_pv_continuous(fv = 1105.17, rate = 0.05, t = 2)

Description

Calculates the interest rate per period.

Usage

calc_rate(
  n,
  pv,
  fv = 0,
  pmt = 0,
  type = 0,
  guess = 0.1,
  max_iter = 100,
  tol = 1e-08
)

Arguments

Value

Interest rate per period.

Examples

calc_rate(n = 12, pv = -1000, fv = 2000)
calc_rate(n = 60, pv = -20000, fv = 0, pmt = 386.66)

Description

Calculates the final value under simple interest.

Usage

calc_simple_interest(pv, rate, n)

Arguments

Value

Final value with simple interest.

Examples

calc_simple_interest(pv = 1000, rate = 0.10, n = 2)

Description

Downloads daily CDI (Certificado de Depósito Interbancário) rate from BCB/SGS. This function retrieves the daily CDI rate (SGS series 12), which is the benchmark interest rate for interbank transactions in Brazil.

Usage

get_cdi_rate(
  start_date = NULL,
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with columns:

date

Reference date

value

Daily CDI rate (% per day)

value_annualized

Annualized CDI rate (% per year, 252 business days)

Note

Series information: This function uses SGS series 12, which represents the daily CDI rate (taxa de juros - CDI). The CDI is a key benchmark for fixed income investments and interbank lending in Brazil. Data is available from 1986 onward with daily frequency (business days only).

Examples

# Default: last 30 days of CDI rate
  df <- get_cdi_rate()

  # Specific period
  df2 <- get_cdi_rate("2023-01-01", "2023-03-31")

  # Using year-month format for a specific month
  df3 <- get_cdi_rate("2023-06", "2023-06")

  # Portuguese column names and labels
  df4 <- get_cdi_rate(language = "pt")

  # Complete example with all parameters
  df5 <- get_cdi_rate("2023-01-01", "2023-12-31", language = "pt", labels = TRUE)

  # Historical analysis
  df6 <- get_cdi_rate("2020-03-01", "2020-04-30")  # COVID period

Description

Downloads daily US dollar exchange rate (commercial, selling) from BCB/SGS. This function retrieves the daily exchange rate (SGS series 1) in Brazilian Real (R$).

Usage

get_exchange_rate(
  start_date = NULL,
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with US dollar exchange rate. Columns depend on the language parameter:

• English (language = "eng"): date (Date), exchange_rate (numeric, R$/US$)

• Portuguese (language = "pt"): data_referencia (Date), taxa_cambio (numeric, R$/US$)

Note

Series information: This function uses SGS series 1, which represents the commercial US dollar selling rate (taxa de câmbio livre - dólar americano - venda). Data is available from 1984 onward with daily frequency.

Examples

# Default: last 30 days of exchange rate
  df <- get_exchange_rate()

  # Specific period
  df2 <- get_exchange_rate("2023-01-01", "2023-03-31")

  # Using year-month format for a specific month
  df3 <- get_exchange_rate("2023-06", "2023-06")

  # Portuguese column names and labels
  df4 <- get_exchange_rate(language = "pt")

  # Complete example with all parameters
  df5 <- get_exchange_rate("2023-01-01", "2023-12-31", language = "pt", labels = TRUE)

Description

Downloads quarterly GDP growth data (% change) from BCB/SGS (Brazilian Central Bank). This function retrieves nominal GDP values (SGS series 2010) and calculates the quarter-over-quarter growth rate.

Usage

get_gdp_growth(
  start_date = "2000-01-01",
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with GDP growth rate. Columns depend on the language parameter:

• English (language = "eng"): date (Date), gdp_growth (numeric, %)

• Portuguese (language = "pt"): data_referencia (Date), crescimento_pib (numeric, %)

Note

Important limitation: The nominal GDP series (SGS 2010) is currently available only until 2014. Requests for periods after 2014 will return empty results or a warning.

Examples

# Default: data from 2000 to current date (but limited to 2014)
  df <- get_gdp_growth()

  # Specific period (within available range)
  df2 <- get_gdp_growth("2010", "2014")

  # Using year-month format
  df3 <- get_gdp_growth("2012-06", "2013-12")

  # End date only (from earliest available to 2020-12-31)
  df4 <- get_gdp_growth(end_date = "2020-12-01")

  # Portuguese column names and labels
  df5 <- get_gdp_growth(language = "pt")

  # Complete example with all parameters
  df6 <- get_gdp_growth("2011-01-01", "2014-12-31", language = "pt", labels = TRUE)

Description

Downloads monthly IPCA (Broad National Consumer Price Index) inflation data from the Brazilian Central Bank's SGS API and calculates accumulated inflation rates. IPCA is Brazil's official inflation index, calculated monthly by IBGE.

Usage

get_inflation_rate(
  start_date = "2012-01-01",
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with inflation metrics. Columns depend on the language parameter:

• English (language = "eng"):

  • date (Date): Reference month

  • monthly_inflation (numeric): Monthly IPCA variation (%)

  • ytd_inflation (numeric): Year-to-date accumulated inflation (%)

  • twelve_month_inflation (numeric): 12-month accumulated inflation (%)

• Portuguese (language = "pt"):

  • data_referencia (Date): Mes de referencia

  • inflacao_mensal (numeric): Variacao mensal do IPCA (%)

  • inflacao_acumulada_ano (numeric): Inflacao acumulada no ano (%)

  • inflacao_12_meses (numeric): Inflacao acumulada nos ultimos 12 meses (%)

Note

Default Period: When start_date = NULL, defaults to "2020-01-01", providing data from the start of 2020. This period covers significant economic events including the COVID-19 pandemic and recent inflationary pressures in Brazil.

Data Processing: This function automatically downloads an extra 12 months of historical data to calculate 12-month accumulated inflation correctly. The final output is filtered to show only the requested period.

Calculation Details:

• Year-to-date inflation: Cumulative product of monthly rates within each calendar year

• 12-month inflation: Rolling 12-month cumulative product of monthly rates

Examples

# Default: from 2020 to current date (aligned with SELIC function)
  df <- get_inflation_rate()

  # Specific period with year-only format
  df2 <- get_inflation_rate("2021", "2023")

  # Using year-month format for precise month selection
  df3 <- get_inflation_rate("2022-03", "2023-06")

  # Portuguese column names and labels
  df4 <- get_inflation_rate(language = "pt")

  # Without variable labels
  df5 <- get_inflation_rate("2020-01-01", "2022-12-31", labels = FALSE)

  # Current year analysis
  current_year <- format(Sys.Date(), "%Y")
  df6 <- get_inflation_rate(start_date = current_year)

  # Compare with SELIC rate (same default period)
  selic_data <- get_selic_rate()  # Also starts at 2020-01-01
  inflation_data <- get_inflation_rate()  # Same start date

Description

Downloads the annual SELIC rate series from the Central Bank of Brazil's SGS API. The SELIC rate (Special System for Settlement and Custody) is Brazil's benchmark overnight interest rate, used as the primary monetary policy instrument.

Usage

get_selic_rate(
  start_date = "2020-01-01",
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with SELIC rate. Columns depend on the language parameter:

• English (language = "eng"): date (Date), selic_rate (numeric, % per year)

• Portuguese (language = "pt"): data_referencia (Date), taxa_selic (numeric, % ao ano)

Note

IMPORTANT API LIMITATION: The BCB API imposes a 10-year maximum window for daily frequency series like SELIC. Requests spanning more than 10 years will fail. For longer historical analyses, split your request into multiple 10-year periods.

DEFAULT PERIOD: When start_date = NULL, defaults to "2020-01-01" (start of 2020), providing recent data while avoiding the 10-year API limit with current dates.

Examples

# Default: from 2020 to current date
  df <- get_selic_rate()

  # Specific period within 10-year limit
  df2 <- get_selic_rate("2020-01-01", "2023-12-31")

  # Last 5 years (respecting 10-year limit)
  df3 <- get_selic_rate(start_date = "2019")

  # Portuguese column names and labels
  df4 <- get_selic_rate(language = "pt")

  # Complete year analysis
  df5 <- get_selic_rate("2018", "2023")

Description

Returns detailed metadata about a specific economic series.

Usage

get_series_info(series_id, field = NULL, language = c("pt", "en"))

Arguments

Value

A list with series metadata or a single value

Examples

get_series_info("433")
get_series_info("433", field = "description", language = "en")

Description

Downloads monthly unemployment rate data from the Brazilian Central Bank's SGS (Sistema Gerenciador de Series Temporais). The series corresponds to the unemployment rate from IBGE's Continuous PNAD survey (PNAD Continua), replicated and made available by the Central Bank.

Usage

get_unemployment(
  start_date = "2020-01-01",
  end_date = NULL,
  language = "eng",
  labels = TRUE
)

Arguments

Value

A data.frame with:

• date (Date): Reference month

• value (numeric): Unemployment rate (%)

Note

Data Source: Brazilian Central Bank (SGS), series 24369. The data originates from IBGE's Continuous National Household Sample Survey (PNAD Continua) and is published with monthly frequency.

Although published monthly, the unemployment rate follows IBGE's moving-quarter methodology.

Examples

# Default: from 2020 to current date (aligned with other functions)
  df <- get_unemployment()

  # Specific period with year-only format
  df2 <- get_unemployment("2018", "2023")

  # Portuguese column names and labels
  df3 <- get_unemployment(language = "pt")

  # Without variable labels
  df4 <- get_unemployment("2020-01-01", "2022-12-31", labels = FALSE)

Description

Generates a time series plot of the CDI (Certificado de Depósito Interbancário) rate. The CDI is the benchmark interest rate for interbank deposits in Brazil and serves as a reference for many fixed income investments.

Usage

plot_cdi_rate(data, language = "eng")

Arguments

Value

A ggplot2 object showing the CDI rate over time.

Examples

# Example 1: English version
cdi_data <- get_cdi_rate(2020, 2024)
cdi_plot <- plot_cdi_rate(cdi_data)
print(cdi_plot)

# Example 2: Portuguese version
dados_cdi <- get_cdi_rate(2020, 2024, language = "pt")
grafico_cdi <- plot_cdi_rate(dados_cdi, language = "pt")
print(grafico_cdi)

Description

Generates a time series plot of the USD/BRL exchange rate using data from get_exchange_rate(). Shows the commercial exchange rate for US Dollar to Brazilian Real.

Usage

plot_exchange_rate(data, language = "eng")

Arguments

Value

A ggplot2 object showing the exchange rate over time.

Examples

# Example 1: English version
exchange_data <- get_exchange_rate("2023-01-01", "2023-12-31")
exchange_plot <- plot_exchange_rate(exchange_data)
print(exchange_plot)

# Example 2: Portuguese version
dados_cambio <- get_exchange_rate("2023-01-01", "2023-12-31", language = "pt")
grafico_cambio <- plot_exchange_rate(dados_cambio, language = "pt")
print(grafico_cambio)

Description

Generates a time series plot of Brazil's monthly inflation rate measured by the IPCA (Índice Nacional de Preços ao Consumidor Amplo). The IPCA is Brazil's official inflation index and is widely used for monetary policy decisions, contract indexation, and economic analysis.

Usage

plot_inflation_rate(data, language = "eng")

Arguments

Value

A ggplot2 object showing the monthly inflation rate over time.

Examples

# Example 1: English version
inflation_data <- get_inflation_rate(2020, 2024)
inflation_plot <- plot_inflation_rate(inflation_data)
print(inflation_plot)

# Example 2: Portuguese version
dados_inflacao <- get_inflation_rate(2020, 2024, language = "pt")
grafico_inflacao <- plot_inflation_rate(dados_inflacao, language = "pt")
print(grafico_inflacao)

Description

Generates a time series plot of the SELIC interest rate using data from get_selic(). The SELIC rate ("Sistema Especial de Liquidação e de Custódia") represents the effective annualized rate (252-business-day basis) for overnight interbank loans and is the main instrument of Brazil’s monetary policy.

Usage

plot_selic_rate(data, language = "eng")

Arguments

Value

A ggplot2 object showing the SELIC rate over time.

Examples

# Example 1: English version
selic_data <- get_selic_rate(2020, 2024)
selic_plot <- plot_selic_rate(selic_data)
print(selic_plot)

# Example 2: Portuguese version
dados_selic <- get_selic_rate(2020, 2024, language = "pt")
grafico_selic <- plot_selic_rate(dados_selic, language = "pt")
print(grafico_selic)

Description

Plots multiple time series on the same chart for comparison.

Usage

plot_series_comparison(
  data_list,
  y_vars,
  date_vars,
  language = "eng",
  scale_type = c("none", "index", "percent_change"),
  title = NULL,
  subtitle = NULL,
  y_label = NULL,
  caption = NULL,
  colors = NULL,
  line_types = NULL,
  show_legend = TRUE,
  legend_position = "bottom"
)

Arguments

Value

A ggplot2 object

Examples

# Example comparing multiple series
selic <- get_selic_rate(2020, 2024)
ipca <- get_ipca(2020, 2024)
igpm <- get_igpm(2020, 2024)

comparison_plot <- plot_series_comparison(
  data_list = list(SELIC = selic, IPCA = ipca, IGPM = igpm),
  y_vars = c("value", "value", "value"),
  date_vars = c("date", "date", "date"),
  scale_type = "index",
  title = "Comparison of Brazilian Economic Indicators",
  y_label = "Index (2020-01 = 100)",
  language = "eng"
)
print(comparison_plot)

Description

Generates a ggplot2 line chart of Brazil's unemployment rate (PNAD Contínua) using data returned by get_unemployment().

Usage

plot_unemployment(data, language = "eng")

Arguments

Value

A ggplot2 object.

Examples

# Example 1: English version
unemployment_data <- get_unemployment("2020", "2024")
unemployment_plot <- plot_unemployment(unemployment_data)
print(unemployment_plot)

# Example 2: Portuguese version
dados_desemprego <- get_unemployment("2020", "2024")
grafico_desemprego <- plot_unemployment(dados_desemprego, language = "pt")
print(grafico_desemprego)

Description

Estimates the number of years required to triple an investment.

Usage

rule_of_114(rate)

Arguments

Value

Approximate years to triple.

Examples

rule_of_114(rate = 0.08)

Description

Estimates the number of years required to double an investment.

Usage

rule_of_72(rate)

Arguments

Value

Approximate years to double.

Examples

rule_of_72(rate = 0.08)