bacenR

In bacenR: Access Data from Brazilian Central Bank: IFdata, Active Institutions, Balance Sheets and Normative Acts

The {bacenR} package provides tools to download and process data from the Brazilian Central Bank (Banco Central do Brasil — Bacen) in a simple and efficient way. Currently, the package includes the following functions:

• get_balance_sheets(): download balance sheets from financial institutions

get_balance_sheets(
  institution = c("BANCOS", "COOPERATIVAS"),
  months = c(6, 12),
  first_year = 2022,
  final_year = 2023,
  out_dir = tempdir(),
  overwrite = FALSE
)

• tidy_balance_sheets(): process downloaded balance sheets with get_balance_sheets() and combine them into a single, unified file

# First, download balance sheets
get_balance_sheets(
  institution = c("BANCOS", "COOPERATIVAS"),
  months = 12,
  first_year = 2022,
  final_year = 2023,
  out_dir = tempdir(),
  overwrite = FALSE
)

# Now, tidy the files
tidy_balance_sheets(
  path_raw = tempdir(),
  out_dir = tempdir(),
  doc_filter = 4010,
  save = FALSE
)

• get_normative_data(): download regulatory metadata

normas <- get_normative_data(
 terms = c("Cooperativas", "Cooperativa"), 
 ini_date = "2025-01-01",
 end_date = "2025-12-12"
 )

• get_normative_txt(): download the full texts of regulatory instructions

# First, download normative data
normative_data <- get_normative_data(
   terms = "Cooperativa",
   ini_date = "2023-08-01",
   end_date = "2023-12-10"
)

# Then, download the full texts for the retrieved normatives
normative_txt <- get_normative_txt(normative_data)

• get_institutions(): download information about institutions regulated by Bacen in activity

# Download multiple institution types
get_institutions(
  institution = c("CONGLOMERADOS"),
  years = 2011:2025,
  months = 1:12,
  out_dir = tempdir(),
  cleanup_zip = TRUE,
  verbose = TRUE
)

• tidy_institutions(): precess the data downloaded with get_institutions() and combine them into a single, unified file

# First, download institution data
get_institutions(
   institution = "COOPERATIVAS",
   years = 2023,
   months = 11:12,
  out_dir = tempdir()
)

# Process institution files from a directory
institutions <- tidy_institutions(
  path_dir = tempdir(),
  out_dir = tempdir(),
  verbose = TRUE
)

• get_ifdata_reports(): download IFdata Reports for specific periods and institution types. See also IFdata Reports interface.

# Multiple periods
cc_ativa_pj_modalidade_20142024 <- bacenR::get_ifdata_reports(
  year = c(2014:2024),
  month = 12,
  report = 13,
  type_institution = 2
)

• tidy_ifdata_reports(): tidy the data.frames downloaded with get_ifdata_reports().

tidy_cc_ativa_pj_modalidade_20142024 <- bacenR::tidy_ifdata_reports(
  cc_ativa_pj_modalidade_20142024
)

• get_ifdata_registry(): download IFdata Registry data for specified years and months.

# Multiple years and months
data <- get_ifdata_registry(
  year = c(2023, 2024), 
  month = c(6, 12)
)

For more details and examples, see the project on GitHub: https://github.com/rtheodoro/bacenR

bacenR documentation built on Oct. 24, 2026, 1:07 a.m.