Systematic data validation with the validate package
"Checking the quality of data is a task that pervades data analyses. It does not matter whether you are working with raw data, cleaned data, or with the results of an analyses. It is always important to convince yourself that the data you are using is fit for its intended purpose.
Since it is such a common task, why not automate it? The 'validate' package is designed for exactly this task: it implements a domain specific language for data checking that aims to encompass any check you might wish to perform. In this course you will will learn to define and measure data quality in a precise way with the validate package. We will focus on the main workflow, and show you how you can involve domain experts directly with your work, even if they do not know R. You will learn the main principles of data validation, both from the point of view of organizing a data processing work flow, as well as from a more formal perspective. You will exercise data validation tasks that range from checking input format and types to complex checks that involve data from multiple sources. You will learn how to follow the evolution of data quality as it is processed using the lumberjack package. And you will learn how to flush out redundant or contradictory quality demands using the validatetools package. The course will consist of hands-on work, based on a prepared tutorial that will be published on GitHub.
There will be break-out sessions with assignments where you can discuss the materials with other course participants. The presentations will include some Kahoot quizzes to keep things interactive, fun, and focused."