Statistical Data Cleaning with Applications in R by Mark van der Loo & Edwin de Jonge

Author:Mark van der Loo & Edwin de Jonge
Language: eng
Format: epub
ISBN: 9781118897133
Publisher: Wiley
Published: 2018-01-25T00:00:00+00:00

6.5.2 Validating in the Pipeline

The pipe operator (%>%) of the magrittr package (Bache and Wickham, 2014) makes it easy to perform consecutive data manipulations on a dataset. The functions check_that and confront have been designed to conform to the pipe operator. For example, we can do

retailers %>% check_that(turnover >= 0, staff >= 0) %>% summary() ## rule items passes fails nNA error warning expression ## 1 V1 60 56 0 4 FALSE FALSE (turnover - 0) >= -1e-08 ## 2 V2 60 54 0 6 FALSE FALSE (staff - 0) >= -1e-08

For more involved checks, it is more convenient to define a validator object first.

v <- validator(turnover>= 0, staff >= 0) retailers %>% confront(v) %>% summary() ## rule items passes fails nNA error warning expression ## 1 V1 60 56 0 4 FALSE FALSE (turnover - 0) >= -1e-08 ## 2 V2 60 54 0 6 FALSE FALSE (staff - 0) >= -1e-08

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