Contains functions to help with generating tables with
    descriptive statistics. In addition, the package can display results
    of statistical tests for group comparisons. A wide range of test
    procedures is supported, and user-defined test functions can be
    incorporated.

Visualizes variables from descriptive tables produced by
    'descsuppR::buildDescrTbl()' using 'ggstatsplot'. It automatically
    maps each variable to a suitable 'ggstatsplot' plotting function
    based on the applied or suggested statistical test. Users can override
    the automatic mapping via a named list of plot specifications. The
    package supports grouped and ungrouped tables, and forwards additional
    arguments to the underlying 'ggstatsplot' functions, providing quick,
    reproducible, and customizable default visualizations for descriptive
    summaries.

Easily create descriptive and comparative tables.
    It makes use and integrates directly with the tidyverse family of packages, and pipes.
    Tables are produced as (nested) dataframes for easy manipulation.

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.