Package GetDFPData

Downloading Annual Financial Reports and Corporate Events from B3 (formerly Bovespa)

Package GetDFPData is being substituted by GetDFPData2. See this blog post for details.

Financial statements of companies traded at B3 (formerly Bovespa), the Brazilian stock exchange, are available in its website. Accessing the data for a single company is straightforward. In the website one can find a simple interface for accessing this dataset. An example is given here. However, gathering and organizing the data for a large scale research, with many companies and many dates, is painful. Financial reports must be downloaded or copied individually and later aggregated. Changes in the accounting format thoughout time can make this process slow, unreliable and irreproducible.

Package GetDFPData provides a R interface to all annual financial statements available in the website and more. It not only downloads the data but also organizes it in a tabular format and allows the use of inflation indexes. Users can select companies and a time period to download all available data. Several information about current companies, such as sector and available quarters are also at reach. The main purpose of the package is to make it easy to access financial statements in large scale research, facilitating the reproducibility of corporate finance studies with B3 data.

The positive aspects of GetDFDData are:

  • Easy and simple R and web interface
  • Changes in accounting format are internally handled by the software
  • Access to corporate events in the FRE system such as dividend payments, changes in stock holder composition, changes in governance listings, board composition and compensation, debt composition, and a lot more!
  • The output data is automatically organized using tidy data principles (long format)
  • A cache system is employed for fast data acquisition
  • Completely free and open source!


The package is available in CRAN (release version) and in Github (development version). You can install any of those with the following code:

# Release version in CRAN
install.packages('GetDFPData') # not in CRAN yet

# Development version in Github

Shinny interface

The web interface of GetDFPData is available at

How to use GetDFPData

The starting point of GetDFPData is to find the official names of companies in B3. Function serves this purpose. Given a string (text), it will search for a partial matches in companies names. As an example, let’s find the official name of Petrobras, one of the largest companies in Brazil:

library(tibble)'petrobras',cache.folder = tempdir())

Its official name in Bovespa records is PETRÓLEO BRASILEIRO S.A. - PETROBRAS. Data for quarterly and annual statements are available from 1998 to 2017. The situation of the company, active or canceled, is also given. This helps verifying the availability of data.

The content of all available financial statements can be accessed with function It will read and parse a .csv file from my github repository. This will be periodically updated for new information. Let’s try it out: <- = 'companies', cache.folder = tempdir())


This file includes several information that are gathered from Bovespa: names of companies, official numeric ids, listing segment, sectors, traded tickers and, most importantly, the available dates. The resulting dataframe can be used to filter and gather information for large scale research such as downloading financial data for a specific sector.

Downloading financial information for ONE company

All you need to download financial data with GetDFPData are the official names of companies, which can be found with, the desired starting and ending dates and the type of financial information (individual or consolidated). Let’s try it for PETROBRAS:

name.companies <- 'PETRÓLEO BRASILEIRO  S.A.  - PETROBRAS' <- '2015-01-01'  <- '2016-01-01'

df.reports <- gdfpd.GetDFPData(name.companies = name.companies, 
                               cache.folder = tempdir())

The resulting object is a tibble, a data.frame type of object that allows for list columns. Let’s have a look in its content:


Object df.reports only has one row since we only asked for data of one company. The number of rows increases with the number of companies, as we will soon learn with the next example. All financial statements for the different years are available within df.reports. For example, the assets statements for all desired years of PETROBRAS are:

df.income.long <- df.reports$fr.income[[1]]


The resulting dataframe is in the long format, ready for processing. In the long format, financial statements of different years are stacked. In the wide format, we have the year as columns of the table.

If you want the wide format, which is the most common way that financial reports are presented, you can use function See an example next:

df.income.wide <-

knitr::kable(df.income.wide )

Downloading financial information for SEVERAL companies

If you are doing serious research, it is likely that you need financial statements for more than one company. Package GetDFPData is specially designed for handling large scale download of data. Let’s build a case with two selected companies:

my.companies <- c('PETRÓLEO BRASILEIRO  S.A.  - PETROBRAS',
                  'BANCO DO ESTADO DO RIO GRANDE DO SUL SA') <- '2016-01-01'  <- '2017-01-01'
type.statements <- 'individual'

df.reports <- gdfpd.GetDFPData(name.companies = my.companies, 
                               cache.folder = tempdir())

And now we can check the resulting tibble:


Every row of df.reports will provide information for one company. Metadata about the corresponding dataframes such as min/max dates is available in the first columns. Keeping a tabular structure facilitates the organization and future processing of all financial data. We can use tibble df.reports for creating other dataframes in the long format containing data for all companies. See next, where we create dataframes with the assets and liabilities of all companies:

df.assets <- = rbind, args = df.reports$fr.assets)
df.liabilities <- = rbind, args = df.reports$fr.liabilities)

df.assets.liabilities <- rbind(df.assets, df.liabilities)

As an example, let’s use the resulting dataframe for calculating and analyzing a simple liquidity index of a company, the total of current (liquid) assets (Ativo circulante) divided by the total of current short term liabilities (Passivo Circulante), over time.

library(dplyr) <- df.assets.liabilities %>%
  group_by(, %>%
  summarise(Liq.Index = acc.value[acc.number == '1.01']/ acc.value[acc.number == '2.01'])

Now we can visualize the information using ggplot2:


p <- ggplot(, aes(x =, y = Liq.Index, fill = +
  geom_col(position = 'dodge' )

Exporting financial data

The package includes function for exporting the financial data to an Excel or zipped csv files. See next:

my.basename <- 'MyExcelData'
my.format <- 'csv' # only supported so far = df.reports, 
             = my.basename,
                      type.export = my.format)

The resulting Excel file contains all data available in df.reports.

Marcelo S. Perlin
Marcelo S. Perlin
Associate Professor

My research interests include data analysis, finance and cientometrics.

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