![]() We first load the package, and create a list ![]() Let’s do a quick check on variables in theĬars data set. Validating data is all about checking whether a data set meets presumptions orĮxpectations you have about it, and the validate package makes it easy for you We’ll use the built-inĬars data set, which contains 50 cases of speed and stopping distances of Here’s an example demonstrating the typical workflow. Section with Biblographical Notes lists some references and points out some Chapterġ0 discusses how to compare two or more versions of a Chapters 7 andĨ treat working with validate in-depth. Part of the book and discuss many different ways to check your data by example.Ĭhapter 6 is devoted to deriving plausibility measures Necessities to be able to follow the rest of the book. Showing how to analyze data validation results. The validate package, giving examples of common data validation tasks, and The purposes of this book include demonstrating the main tools and workflows of The latest release of validate can be installedįrom CRAN as follows. This version of the book was rendered with validate versionġ.1.3. This book is about checking data with the
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