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Check and Adjust Missing Tags

Source: R/checkMissings.R
checkMissings.Rd

Functions to check if missings are tagged and labeled correctly in a GADSdat object.

Usage

checkMissings(
  GADSdat,
  missingLabel = "missing",
  addMissingCode = TRUE,
  addMissingLabel = FALSE
)

checkMissingsByValues(GADSdat, missingValues = -50:-99, addMissingCode = TRUE)

Arguments

GADSdat

GADSdat object imported via eatGADS.

missingLabel

Single regular expression indicating how missing labels are commonly named in the value labels.

addMissingCode

If TRUE, missing tags are added according to missingLabel or missingValues.

addMissingLabel

If TRUE, "generic missing" is added according to occurrence of "miss" in "missings". As often various value labels for missings are used, this argument should be used with great care.

missingValues

Numeric vector of values which are commonly used for missing values.

Value

Returns a GADSdat object with - if specified - modified missing tags.

Details

checkMissings() compares value labels (valLabels) and missing tags (missings) of a GADSdat object and its meta data information. checkMissingsByValues() compares labeled values (value) and missing tags (missings) of a GADSdat object and its meta data information. Mismatches are reported and can be automatically adjusted. Note that all checks are only applied to the meta data information, not the actual data. For detecting missing value labels, see checkMissingValLabels.

Functions

  • checkMissings(): compare missing tags and value labels

  • checkMissingsByValues(): compare missing tags and values in a certain range

Examples

# checkMissings
pisa2 <- changeValLabels(pisa, varName = "computer_age",
                        value = 5, valLabel = "missing: No computer use")

pisa3 <- checkMissings(pisa2)
#> The following variables have value labels including the term 'missing' which are not coded as missing:
#> computer_age
#> 'miss' is inserted into column missings for 1 rows.

# checkMissingsByValues
pisa4 <- changeValLabels(pisa, varName = "computer_age",
                        value = c(-49, -90, -99), valLabel = c("test1", "test2", "test3"))

pisa5 <- checkMissingsByValues(pisa4, missingValues = -50:-99)
#> The following variables have values in the 'missingValues' range which are not coded as missing:
#> computer_age
#> 'miss' is inserted into column missings for 2 rows.