Replace erroneous fields with NA or a suggested value

Find erroneous fields using locate_errors() and replace these fields automatically with NA or a suggestion that is provided by the error detection algorithm.

Usage

replace_errors(
  data,
  x,
  ref = NULL,
  ...,
  cl = NULL,
  Ncpus = getOption("Ncpus", 1),
  value = c("NA", "suggestion")
)

# S4 method for data.frame,validator
replace_errors(
  data,
  x,
  ref = NULL,
  ...,
  cl = NULL,
  Ncpus = getOption("Ncpus", 1),
  value = c("NA", "suggestion")
)

# S4 method for data.frame,ErrorLocalizer
replace_errors(
  data,
  x,
  ref = NULL,
  ...,
  cl = NULL,
  Ncpus = getOption("Ncpus", 1),
  value = c("NA", "suggestion")
)

# S4 method for data.frame,errorlocation
replace_errors(
  data,
  x,
  ref = NULL,
  ...,
  cl = NULL,
  Ncpus = 1,
  value = c("NA", "suggestion")
)

Arguments

data

data to be checked

x

validator() or errorlocation object. If an errorlocation is already available (through locate_errors()) this is more efficient.

ref

optional reference data set

...

these parameters are handed over to locate_errors()

cl

optional cluster for parallel execution (see details)

Ncpus

number of nodes to use. (see details)

value

NA

Value

data with erroneous values removed.

Details

Note that you can also use the result of locate_errors() with replace_errors. When the procedure takes a long time and locate_errors was called previously this is the preferred way, because otherwise locate_errors will be executed again. The errors that were removed from the data.frame can be retrieved with the function errors_removed(). For more control over error localization see locate_errors().

replace_errors has the same parallelization options as locate_errors() (see there).

Note

In general it is better to replace the erroneous fields with NA and apply a proper imputation method. Suggested values from the error localization method may introduce an undesired bias.

See also

errorlocation-class()

Other error finding: errorlocation-class, errors_removed(), expand_weights(), locate_errors()

Examples

rules <- validator( profit + cost == turnover
              , cost - 0.6*turnover >= 0
              , cost>= 0
              , turnover >= 0
)
data <- data.frame(profit=755, cost=125, turnover=200)

data_no_error <- replace_errors(data,rules)

# faulty data was replaced with NA
data_no_error
#>   profit cost turnover
#> 1     NA  125      200

errors_removed(data_no_error)
#> call:  locate_errors(data, x, ref, ..., cl = cl, Ncpus = Ncpus) 
#> located  1  error(s).
#> located  0  missing value(s).
#> Use 'summary', 'values', '$errors' or '$weight', to explore and retrieve the errors.

# a bit more control, you can supply the result of locate_errors
# to replace_errors, which is a good thing, otherwise replace_errors will call
# locate_errors internally.
error_locations <- locate_errors(data, rules)
replace_errors(data, error_locations)
#>   profit cost turnover
#> 1     NA  125      200