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 gdfpd.search.company, 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'
first.date <- '2015-01-01'
last.date  <- '2016-01-01'

df.reports <- gdfpd.GetDFPData(name.companies = name.companies, 
                               first.date = first.date,
                               last.date = last.date,
                               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:

glimpse(df.reports)

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]]

glimpse(df.income.long)

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 gdfpd.convert.to.wide. See an example next:

df.income.wide <- gdfpd.convert.to.wide(df.income.long)

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')

first.date <- '2016-01-01'
last.date  <- '2017-01-01'
type.statements <- 'individual'

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

And now we can check the resulting tibble:

glimpse(df.reports)

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 <- do.call(what = rbind, args = df.reports$fr.assets)
df.liabilities <- do.call(what = rbind, args = df.reports$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)

my.tab <- df.assets.liabilities %>%
  group_by(name.company, ref.date) %>%
  summarise(Liq.Index = acc.value[acc.number == '1.01']/ acc.value[acc.number == '2.01'])

my.tab

Now we can visualize the information using ggplot2:

library(ggplot2)

p <- ggplot(my.tab, aes(x = ref.date, y = Liq.Index, fill = name.company)) +
  geom_col(position = 'dodge' )
print(p)

Exporting financial data

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

my.basename <- 'MyExcelData'
my.format <- 'csv' # only supported so far
gdfpd.export.DFP.data(df.reports = df.reports, 
                      base.file.name = my.basename,
                      type.export = my.format)

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