DDIwR (version 0.9)

recodeValues: Recode missing values

Description

A function to recode all missing values to either SPSS or Stata types, uniformly (re)using the same codes across all variables.

Usage

recodeValues(dataset, to = c("SPSS", "Stata"), dictionary = NULL, chartonum = TRUE, ...)

Arguments

dataset

A data frame

to

Software to recode missing values for

dictionary

A named vector, with corresponding SPSS values and Stata codes.

chartonum

Logical, replace character values with numbers.

...

Other internal arguments.

Value

A data frame with all missing values recoded consistently.

Details

When a dictionary is not provided, it is automatically constructed from the available data and metadata, using negative numbers starting from -91 and up to 27 letters starting with "a".

If the dataset contains mixed variables with SPSS and Stata style missing values, unless otherwise specified in a dictionary it uses other codes than the existing ones.

For the SPSS type of missing values, the resulting variables are coerced to a declared labelled format.

Unlike SPSS, Stata does not allow labels for character values. Both cannot be transported from SPSS to Stata, it is either one or another. If labels are more important to preserve than original values (especially the information about the missing values), the argument chartonum replaces all character values with suitable, non-overlapping numbers and adjusts the labels accordingly.

If no labels are found in the metadata, the original values are preserved.

Examples

# NOT RUN {
x <- data.frame(
    A = declared(
        c(1:5, -92),
        labels = c(Good = 1, Bad = 5, NR = -92),
        na_values = -92
    ),
    B = labelled(
        c(1:5, tagged_na('a')),
        labels = c(DK = tagged_na('a'))
    ),
    C = declared(
        c(1, -91, 3:5, -92),
        labels = c(DK = -91, NR = -92),
        na_values = c(-91, -92)
    )
)

#         A     B       C
# 1       1     1       1
# 2       2     2 NA(-91)
# 3       3     3       3
# 4       4     4       4
# 5       5     5       5
# 6 NA(-92) NA(a) NA(-92)


xrec <- recodeValues(x, to = "Stata")

#       A     B     C
# 1     1     1     1
# 2     2     2 NA(b)
# 3     3     3     3
# 4     4     4     4
# 5     5     5     5
# 6 NA(c) NA(a) NA(c)


attr(xrec, "dictionary")
#   b   c 
# -91 -92


recodeValues(x, to = "Stata", dictionary = c(a = -91, b = -92))
#       A     B     C
# 1     1     1     1
# 2     2     2 NA(a)
# 3     3     3     3
# 4     4     4     4
# 5     5     5     5
# 6 NA(b) NA(a) NA(b)


recodeValues(x, to = "SPSS")
#         A       B       C
# 1       1       1       1
# 2       2       2 NA(-91)
# 3       3       3       3
# 4       4       4       4
# 5       5       5       5
# 6 NA(-92) NA(-93) NA(-92)


recodeValues(x, to = "SPSS", dictionary = c(a = -91))
#         A       B       C
# 1       1       1       1
# 2       2       2 NA(-91)
# 3       3       3       3
# 4       4       4       4
# 5       5       5       5
# 6 NA(-92) NA(-91) NA(-92)
# }