Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.

Tools to read various file types into one list of data structures, usually, but not limited to, data frames.
  Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal
  separators are determined automatically.

Functions for reading mass spectrometry data in mzXML format.

Loads 'Blackrock' <https://blackrockneurotech.com> neural signal 
    data files into the memory, provides utility tools to extract the data into 
    common formats such as plain-text 'tsv' and 'HDF5'.

Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.

The goal of 'readr' is to provide a fast and friendly way to
    read rectangular data (like 'csv', 'tsv', and 'fwf').  It is designed
    to flexibly parse many types of data found in the wild, while still
    cleanly failing when data unexpectedly changes.

Download up-to-date data from the Reserve Bank of Australia 
    in a tidy data frame. Package includes functions to download current and 
    historical statistical tables 
    (<https://www.rba.gov.au/statistics/tables/>) and forecasts 
    (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data
    includes a broad range of Australian macroeconomic and financial time
    series.