tag_um: Tag user-missing values

In sjpierce/piercer: Functions for Research and Statistical Computing

Tag user-missing values

Description

This function converts valid values in an labelled_spss vector that has a non-NULL na_values attribute to tagged NAs, while retaining their value labels. This is most useful for numeric variables that have explicit single-digit user-missing values that are not yet being treated as NAs in R.

Usage

tag_um(x)

Arguments

x	A vector of class haven_labelled_spss, such as created by the labelled_spss() function from the haven package. The vector should have an na_values attribute listing the values that should be treated as NAs.

Details

=============================================================================

This function builds on features from the haven package. It is a convenience function for people who import SPSS data files that used the discrete missing values feature to mark several values as user-missing values. SPSS treats them like missing data while preserving distinct value labels. However, if one reads an SPSS data file in with haven's read_sav() and the source file had a variable with discrete missing values, those are identified by the na_values attribute on the variable in the resulting tibble but are not yet consistently treated as NAs by R. This function converts the to tagged NA values.

Value

A copy of vector x (of class haven_labelled_spss) where the values specified in ums have been converted to tagged NA values and are now treated as NAs by R.

Examples

library(haven)
y <- labelled_spss(x = 1:5, na_values = c(4, 5),
                   labels = c(Good = 1, Bad = 3, Unknown = 4, DK = 5))
y
str(y)

# xtabs() shows only non-missing values (expected given default addNA = FALSE).
xtabs(~y)

# However, setting addNA = TRUE in xtabs() always treats each tagged NA as a
# separate category; there's no easy way to collapse them all into just NA.
xtabs(~y, addNA = TRUE)

# Furthermore, the user-missing values are not correctly handled by other
# functions. For example, mean() gives an incorrect result because it still
# treats 4 & 5 as valid values. The actual mean of the non-missing values is
# 2.
mean(y)
mean(y, na.rm = TRUE)

# Now apply tag_um() to fix these issues.
y2 <- tag_um(y)
y2
print_tagged_na(y2)
str(y2)

# xtabs() shows only non-missing values (expected given default addNA = FALSE).
xtabs(~y2)

# If you want to ignore distinctions between tagged NA categories and treat
# them all as just NA, we get the correct result this way:
xtabs(~y2, addNA = TRUE)

# If you want to preserve distinctions between tagged NA categories, we get
# the correct result this way:
xtabs(~print_tagged_na(y2), addNA = TRUE)

# Now mean() will correctly handle the variable with tagged NAs by treating
# them all as NAs. It returns NA because it defaults to na.rm = FALSE.
mean(y2)

# We can get the omit the tagged NA values and get the correct mean.
mean(y2, na.rm = TRUE)

sjpierce/piercer documentation built on Dec. 30, 2024, 3:28 p.m.