Find errors in data

Intro

Errorlocate uses validation rules from package validate to locate faulty values in observations (or in database slang: erronenous fields in records).

It follows this simple recipe (Felligi-Holt):

Check if a record is valid (using supplied validation rules)
If not valid then adjust the minimum number of values to make it valid.

errorlocate does this by translating the validation rules and record values into a mixed integer problem (see vignette("inspect_mip", package="errorlocate") and solving it using the R package lpSolveAPI.

Workflow

Locating errors is part of a data cleaning workflow, see e.g. Loo and Jonge (2018), where after locating errors, these errors are removed (set to NA) and subsequently imputed. The input for errorlocate are validation rules, which can be formulated using package validate (van der Loo and de Jonge 2019).

A typical workflow for using errorlocate is:

Formulate validation rules using validate::validator()
Check which records are invalid using validate::confront()
Use validate::summary() to get an overview of invalid records and the rules they violate.
(Optional) Correct obvious data errors which correction rules, for example using dcmodify::modify
(Optional) Determine weights for variables, indicating how likely they are to be faulty. Higher weights indicate more trust in the variable.
Use locate_errors to find which values are faulty for the invalid records.
Use replace_errors to set faulty values to NA.
Use deductive imputation to fill in missing values that follow from the rules, e.g. deductive::deduImpute()
Impute the remaining missing values using your favorite imputation method, e.g. simputation
(Optional) Re-validate the data using validate::confront() to check if all records are now valid.

Post hoc:

Analyze the output of locate_errors to get insights in which variables are more often faulty than others, which can be used to improve future data collection processes.

Main methods

errorlocate has two main functions to be used:

locate_errors for detecting errors
replace_errors for replacing faulty values with NA

library(validate)
library(errorlocate)

Let’s start with a simple example:

We have a rule that age cannot be negative:

rules <- validator(age > 0)

And we have the following data set

"age, income
 -10,    0  
  15, 2000
  25, 3000
  NA, 1000
" -> csv
d <- read.csv(textConnection(csv), strip.white = TRUE)

#>   age income
#> 1 -10      0
#> 2  15   2000
#> 3  25   3000
#> 4  NA   1000

le <- locate_errors(d, rules)
summary(le)
#> Variable:
#>     name errors missing
#> 1    age      1       1
#> 2 income      0       0
#> Errors per record:
#>   errors records
#> 1      0       3
#> 2      1       1

summary(le) gives an overview of the errors found in this data set. The complete error listing can be found with:

le$errors
#>        age income
#> [1,]  TRUE  FALSE
#> [2,] FALSE  FALSE
#> [3,] FALSE  FALSE
#> [4,]    NA  FALSE

Which says that record 1 has a faulty value for age.

Suppose we expand our rules

rules <- validator( r1 = age > 0
                  , r2 = if (income > 0) age > 16
                  )

With validate::confront we can see that rule r2 is violated (record 2).

d |> 
  confront(rules) |> 
  summary()
#>   name items passes fails nNA error warning               expression
#> 1   r1     4      2     1   1 FALSE   FALSE                  age > 0
#> 2   r2     4      2     1   1 FALSE   FALSE income <= 0 | (age > 16)

What errors will be found by locate_errors?

set.seed(1)
le <- locate_errors(d, rules)
le$errors
#>        age income
#> [1,]  TRUE  FALSE
#> [2,]  TRUE  FALSE
#> [3,] FALSE  FALSE
#> [4,]    NA  FALSE

It now detects that age in observation 2 is also faulty, since it violates the second rule. Note that we use set.seed. This is needed because in this example, either age or income can be considered faulty. set.seed assures that the procedure is reproducible.

With replace_errors we can remove the errors (which still need to be imputed).

d_fixed <- replace_errors(d, le)
d_fixed |> confront(rules) |>summary()
#>   name items passes fails nNA error warning               expression
#> 1   r1     4      1     0   3 FALSE   FALSE                  age > 0
#> 2   r2     4      2     0   2 FALSE   FALSE income <= 0 | (age > 16)

In which replace_errors set all faulty values to NA.

d_fixed
#>   age income
#> 1  NA      0
#> 2  NA   2000
#> 3  25   3000
#> 4  NA   1000

Weights

locate_errors allows for supplying weights for the variables. It is common that the quality of the observed variables differs. When we have more trust in age because it was retrieved from the official population register, we can give it more weight so it chooses income when it has to decide between the two (record 2):

set.seed(1) # good practice, see later in this document
weight <- c(age = 2, income = 1) 
le <- locate_errors(d, rules, weight)
le$errors
#>        age income
#> [1,]  TRUE  FALSE
#> [2,] FALSE   TRUE
#> [3,] FALSE  FALSE
#> [4,]    NA  FALSE

Weights can be specified in different ways: (see also errorlocate::expand_weights):

not specifying: all variables will have weight 1
named vector: all records will have same set of weights. Unspecified columns will have weight 1.
named matrix or data.frame, same dimension as the data: specify weights per record.
Use Inf weights to fixate a variable, so it won’t be changed.

Reproducability (`set.seed`)

The error location procedure can have, in some cases, multiple optimal solutions. For example given the rule:

if (married == TRUE) age > 16

and the following data:

#>   age married
#> 1   4    TRUE

Then either age or married can be considered faulty. When no weights are specified, locate_errors will randomly choose one of the two. It does this by adding internally a small amount of random noise to the weights. To make sure that the results are reproducible, it is good practice to use set.seed before calling locate_errors.

Note that using set.seed only makes the exact indentical input reproducible. Whenever the input data.frame have a different order, different number of rows or columns, the generated noise for the weights of some of the records will be different. This level of reproducibility can be achieved by generating the noise beforehand using errorlocate::add_noise, storing the weights together with the records and supplying the noisy weights to locate_errors.

Performance / Parallelisation

locate_errors solves a mixed integer problem. When the number of interactions between validation rules is large, finding an optimal solution can become computationally intensive. Both locate_errors as well as replace_errors have a parallization option: Ncpus making use of multiple processors. The $duration (s) property of each solution indicates the time spent to find a solution for each record. This can be restricted using the argument timeout (s).

# restrict time per record to max 30 seconds
le <- locate_errors(d, rules, timeout=30)
# duration is in seconds.
le$duration
#> [1] 0.002124786 0.001615286 0.000000000 0.001486301

Loo, Mark van der, and Edwin de Jonge. 2018. Statistical Data Cleaning with Applications in . John Wiley & Sons.