Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.

You only need to type 'why pie charts are bad' on Google to find
    thousands of articles full of (valid) reasons why other types of charts 
    should be preferred over this one.
    Therefore, because of the little use due to the reasons already mentioned,
    making pie charts (and related) in R is not straightforward, so other 
    functions are needed to simplify things.
    In this R package there are useful functions to make 'tasty' pie charts 
    immediately by exploiting the many cool templates provided.

A collection of recipe datasets scraped from <https://www.allrecipes.com/>, containing two complementary datasets: 'allrecipes' with 14,426 general recipes, and 'cuisines' with 2,218 recipes categorized by country of origin. Both datasets include comprehensive recipe information such as ingredients, nutritional facts (calories, fat, carbs, protein), cooking times (preparation and cooking), ratings, and review metadata. All data has been cleaned and standardized, ready for analysis.

The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.

Functions to combine data.frames in ways that require
    additional effort in base R, and to add metadata (id, title, ...) that
    can be used for printing and xlsx export. The 'Tatoo_report' class is
    provided as a convenient helper to write several such tables to a
    workbook, one table per worksheet. Tatoo is built on top of
    'openxlsx', but intimate knowledge of that package is not required to
    use tatoo.