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Recode common missing-value sentinels to NA

Source: R/api-estimation.R
recode_missing_codes.Rd

Convenience helper that replaces the standard non-NA missing-code sentinels used in SPSS / SAS / FACETS exports (99, 999, -1, "N", "NA", "n/a", ".", "") with NA across the columns you select. It is useful before calling fit_mfrm() on data exported with those conventions. A sentinel is a value used to mean "missing" instead of an actual score. Only recode values that your data documentation defines as missing. For custom score markers, specify both columns = "Score" and codes; this preserves a person or rater identifier such as 99.

Usage

recode_missing_codes(
  data,
  columns = NULL,
  codes = c("99", "999", "-1", "N", "NA", "n/a", ".", ""),
  numeric_codes = TRUE,
  verbose = FALSE
)

Arguments

data

A data frame.

columns

Character vector of column names to recode. Defaults to NULL, in which case all columns are scanned.

codes

Character vector of code values to convert to NA. Defaults to the FACETS / SPSS / SAS conventions; override when your instrument uses different sentinels.

numeric_codes

Logical; if TRUE (default), numeric columns are also compared against the numeric conversion of codes.

verbose

Logical; if TRUE, emits a message() summary of per-column replacement counts.

Value

The input data with the specified missing sentinels replaced by NA. A mfrm_missing_recoding attribute records the per-column replacement counts for traceability logs.

Details

Save the returned data, for example cleaned <- recode_missing_codes(...). The original object is unchanged. This helper replaces cells and retains every row; later, describe_mfrm_data() and fit_mfrm() exclude rating rows with missing scores or required identifiers. Inspect the replacement counts with attr(cleaned, "mfrm_missing_recoding"), then review and fit cleaned.

The defaults differ across entry points: this helper scans all columns when columns is omitted. In describe_mfrm_data() and fit_mfrm(), missing_codes = TRUE uses the conventional code set on the score column only; an explicit missing_codes vector applies to the person, facet, and score columns. Use this helper with an explicit score column when your custom code could also be a legitimate identifier.

See also

describe_mfrm_data(), fit_mfrm().

Examples

library(mfrmr)

# A small input example: 99 and . mean missing only in the Score column
ratings <- data.frame(
  Person = c("001", "001", "002", "002"),
  Rater = c("R1", "99", "R1", "99"),
  Score = c("3", "99", ".", "2")
)
cleaned <- recode_missing_codes(
  ratings,
  columns = "Score",
  codes = c("99", ".")
)
cleaned # Two scores become NA; rater ID 99 and all four rows remain
#>   Person Rater Score
#> 1    001    R1     3
#> 2    001    99  <NA>
#> 3    002    R1  <NA>
#> 4    002    99     2
attr(cleaned, "mfrm_missing_recoding") # Score: Replaced = 2
#>   Column Replaced
#> 1  Score        2
# Use the returned cleaned data for subsequent data review and fitting