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.

A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.

Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <https://rapidxml.sourceforge.net/> is used to parse the XML data.

The goal of 'readsdr' is to bridge the design capabilities from
    specialised System Dynamics software with the powerful numerical tools 
    offered by 'R' libraries. The package accomplishes this goal by parsing 
    'XMILE' files ('Vensim' and 'Stella') models into 'R' objects to construct 
    networks (graph theory); 'ODE' functions for 'Stan'; and inputs to simulate
    via 'deSolve' as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

Read and write labelled sparse matrices in text format as used by
    software such as 'SVMLight', 'LibSVM', 'ThunderSVM', 'LibFM', 'xLearn', 'XGBoost', 'LightGBM',
    and others. Supports labelled data for regression, classification (binary, multi-class, multi-label),
    and ranking (with 'qid' field), and can handle header metadata and comments in files.