CRAN Recipes: DPLYR, Stringr, Lubridate, and RegEx in R

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2021

W. YarberryCRAN Recipeshttps://doi.org/10.1007/978-1-4842-6876-6_19

19. Validation of Data

William Yarberry¹  

(1)

Kingwood, TX, USA

 

After spending five minutes doing data science, everyone knows that data preparation, including validation, is the most time-consuming step of any analysis. Several cleanup packages have been developed, including janitor and validate. Figure 19-1, from the validate package, shows a convenient graphic of three mtcars variables. It meets the data science trifecta: simple, quick, and handy.

In this code, the horsepower criteria of “more than 250” becomes validation rule V1. Most cars from this dataset fail that test, so the V1 graphic ...