In ekstroem/cleanR: A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process

#library(knitr)
options(htmltools.dir.version = FALSE, cache=TRUE)
#opts_chunk$set(dev.args=list(bg="transparent"), fig.width=15, fig.height=7)
source("kutheme.R")

library(dataMaid)
library(assertr)
library(dplyr)
library(validate)
data(bigPresidentData)
bpD <- bigPresidentData

Row-wise or column-wise checks?

knitr::include_graphics("pics/colrow1.png")

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Row-wise or column-wise checks?

knitr::include_graphics("pics/colrow2.png")

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Row-wise or column-wise checks?

knitr::include_graphics("pics/colrow3.png")

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Row-wise and column-wise constraints!

• dataMaid performs class dependent checks for each variable in a dataset, one at a time (column-wise)
• Pros: Easy to document what was (not) done, let's you get started without a lot of prior knowledge, easy to share with collaborators
• Shortcomings: Generally cannot detect internal consistency issues or use non-class dependent variable constraints

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Row-wise and column-wise constraints!

An R-packages that performs row-wise checks: validate

library(validate)

Note: Different use of the term "validation" - no longer about format, type and range, but used as synonym to "check".

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validate - overview:

• Splits checking into two steps:
• Define the checking rules in a validator object, using the valdiator() function.
• Confront the data with the rules in a call to confront, thereby obtaining a confrontation object.
• Easy to asses what problems were found and to document what was checked (saved in validator object).
• Beware: Non-standard object storage might cause trouble!

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Make validator object

.footnotesize[

val1 <- 
 validator(
   ageAtDeath := floor((dateOfDeath - birthday)/365.25),  
   `Adult president` = ageAtInauguration >= 18,
   `Alive at inauguration` = ageAtDeath >= ageAtInauguration,
   `Positive first name` = firstName*2 > firstName,
   `Death by assassination` = 
     if (dateOfDeath == presidencyEndDate) 
       assassinationAttempt == 1
)

]

---

Confront data with validator object:

.footnotesize[

con1 <- confront(bpD, val1)
summary(con1)[, 1:6]

]

---

Understand confrontation results

Lots of functions available for inspecting confrontations: summary(): Overview of confrontation results aggregate(): Compute percentage pass/fail/na sort(): Sort results by problem prevalence values(): For each observation and each check: TRUE/FALSE/NA barplot(): Visual overview of check results errors(): What errors were caught? * warnings(): What warnings were caught?

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Warning: modify-by-reference

• validator and confrontation objects imply use of "modify-by-reference" rather than "copy-on-modify" semantics (standard R)
• Other package that uses "modify-by-reference": data.table
• If one is not aware of modify-by-reference, it can have problematic consequences
• Unintentional overwriting of full objects, attributes or parts
• Caching in rmarkdown does not work (but throws no errors)

---

Warning: modify-by-reference

v1 <- validator(check1 = sex == "Male")
v1

v2 <- v1  
names(v2) <- "All males"
v1

---

Warning: modify-by-reference

Make a copy using [TRUE]:

v1 <- validator(check1 = sex == "Male")
v2 <- v1[TRUE]  
names(v2) <- "All males"
v1

ekstroem/cleanR documentation built on June 12, 2025, 11:08 a.m.