The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

brfinance 📊🇧🇷

brfinance provides a unified, consistent R interface for accessing Brazilian macroeconomic data and performing core financial math calculations.

Instead of writing custom API wrappers, parsing raw JSON responses, or dealing with inconsistent date formats, brfinance standardizes macroeconomic series into clean data.frame outputs with consistent date and value structure. It also includes built-in ggplot2 visualization functions and essential corporate finance routines.

Key Capabilities

• Macroeconomic Data Access: Query official series from the Central Bank of Brazil (BCB) and IBGE (via SGS), including interest rates (SELIC, CDI), inflation metrics (IPCA), exchange rates (USD/BRL), GDP growth, and unemployment (PNAD Contínua).
• Equity Market Benchmark: Download historical Ibovespa index daily data via Yahoo Finance using the same standardized interface.
• Financial Math Calculators: Perform calculations for NPV, IRR, PV, FV, annuities, loan payments (PMT), amortization schedules, interest rate conversions, and rule estimates.
• Ready-to-Use Visualizations: Generate publication-ready ggplot2 charts with unified color palettes and customizable scaling (raw, indexed, percentage change, or dual-axis).
• Production-Ready Data Engine: Automatic 10-year query chunking for long time series, flexible date inputs (YYYY, YYYY-MM, YYYY-MM-DD), and bilingual metadata support (English and Portuguese).

Looking for individual stock analysis? brfinance focuses on macroeconomic indicators and market benchmarks. For individual B3 equity data, portfolio optimization, and Markowitz analysis, see the companion package {brstocks}.

Installation

Install the stable version from CRAN:

install.packages("brfinance")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("efram2/brfinance")

Load the package:

library(brfinance)

Quick Start

Every data retrieval function (get_*) returns a standardized data.frame with date and value columns (plus specialized columns where appropriate).

1. Macroeconomic Series & Visualizations

Inflation Rate (IPCA)

plot_inflation_rate(
  get_inflation_rate("2020", "2024")
)

SELIC Interest Rate

plot_selic_rate(
  get_selic_rate("2020", "2024")
)

Unemployment Rate (PNAD Contínua)

plot_unemployment(
  get_unemployment("2019", "2024")
)

Ibovespa Index Benchmark

plot_ibovespa(
  get_ibovespa("2020-01-01", "2024-12-31")
)

2. Economic Target Analysis & Series Comparison

IPCA vs. Inflation Target Gap

Evaluate whether inflation is operating within the National Monetary Council (CMN) target parameters:

ipca_gap <- get_ipca_from_target("2015", "2024")
head(ipca_gap)

        date ipca_12m target  gap
1 2015-01-01     7.14    4.5 2.64
2 2015-02-01     7.70    4.5 3.20
3 2015-03-01     8.13    4.5 3.63
4 2015-04-01     8.17    4.5 3.67
5 2015-05-01     8.47    4.5 3.97
6 2015-06-01     8.89    4.5 4.39

Multi-Series Comparison (Indexed)

Compare indicators across different scales by indexing from the first observation (Base = 100):

plot_series_comparison(
  data_list = list(
    "SELIC" = get_selic_rate("2020", "2024"),
    "IPCA"  = get_inflation_rate("2020", "2024"),
    "Unemployment" = get_unemployment("2020", "2024")
  ),
  y_vars = rep("value", 3),
  date_vars = rep("date", 3),
  scale_type = "index",
  title = "Brazilian Economic Indicators",
  subtitle = "Indexed comparison (base = first observation)"
)

Dual-Axis Comparison

Compare two indicators with different original units (e.g., % rate vs. currency rate) without loss of scale resolution:

plot_series_comparison(
  data_list = list(
    "SELIC" = get_selic_rate("2020", "2024"),
    "Exchange Rate" = get_exchange_rate("2020-01-01", "2024-12-31")
  ),
  y_vars = c("value", "value"),
  date_vars = c("date", "date"),
  dual_axis = TRUE,
  title = "SELIC vs. USD/BRL Exchange Rate"
)

3. Financial Mathematics & Cash Flow Analysis

# Net Present Value (NPV)
calc_npv(rate = 0.1, cashflows = c(-1000, 300, 400, 500))
#> [1] -21.03681

# Internal Rate of Return (IRR)
calc_irr(cashflows = c(-1000, 300, 400, 500))
#> [1] 0.08896339

# Periodic Loan Payment (PMT)
calc_pmt(pv = 10000, rate = 0.02, n = 24)
#> [1] 528.711

# Amortization Schedule (First 3 periods)
head(calc_amortization_schedule(pv = 10000, rate = 0.01, n = 12), 3)
#>   Period Beginning_Balance  Payment Interest Principal Ending_Balance
#> 1      1          10000.00 888.4879 100.0000  788.4879       9211.512
#> 2      2           9211.51 888.4879  92.1151  796.3728       8415.139
#> 3      3           8415.14 888.4879  84.1514  804.3365       7610.803

Function Reference

Data Retrieval (get_*)

Query Handling & Features: - Date Flexibility: Accepts "YYYY", "YYYY-MM", or "YYYY-MM-DD". - Automatic Chunking: Queries spanning more than 10 years are automatically partitioned into sub-requests to respect Central Bank API limitations and joined seamlessly.

Visualization (plot_*)

Financial Calculators (calc_* & Rules)

• Present & Future Value: calc_present_value(), calc_future_value(), calc_compound_interest(), calc_future_value_ext()
• Annuities & Payments: calc_pv_annuity(), calc_fv_annuity(), calc_pmt()
• Rate & Term Solving: calc_rate(), calc_nper(), calc_simple_interest(), calc_effective_rate(), calc_nominal_rate()
• Investment Valuation: calc_npv(), calc_irr(), calc_amortization_schedule()
• Continuous Compounding: calc_continuous_compounding(), calc_pv_continuous()
• Quick Estimation Rules: rule_of_72(), rule_of_114()

Technical Specifications

Data Sources & Architecture

Data integrated into brfinance comes from the following providers:

1. Central Bank of Brazil (BCB / SGS): Official macroeconomic indicators including interest rates (SELIC, CDI), exchange rates (USD/BRL), inflation target parameters, and GDP growth.
2. IBGE (via BCB SGS): PNAD Contínua unemployment data, retrieved through the Central Bank’s System for Time Series Management (SGS).
3. Yahoo Finance: Historical daily closing levels for the Ibovespa index (get_ibovespa()).

Language Support

All get_*() functions include a language parameter:

• language = "eng" (default): Returns English variable labels.
• language = "pt": Returns Portuguese variable labels.

Pipeline Consistency: Core data frame column names (date, value, etc.) remain identical regardless of language selection. Changing language alters only the descriptive variable labels (accessible via labelled::var_label()), ensuring existing data transformation pipelines do not break.

Related Packages

• {brstocks}: Companion package by the same author, designed for B3 individual stock analysis, portfolio management, and interactive Markowitz efficient-frontier modeling.

Contributing

Contributions, issue reports, and feature requests are welcome! Feel free to open an issue or pull request on GitHub.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.