sjmisc (version 1.8)

to_factor: Convert variable into factor and keep value labels

Description

This function converts a variable into a factor, but preserves variable and value label attributes. See 'Examples'.

Usage

to_factor(x, add.non.labelled = FALSE, drop.na = TRUE, ref.lvl = NULL)

Arguments

x

Numeric, atomic or character vector or a data frame with such vectors.

add.non.labelled

Logical, if TRUE, non-labelled values also get value labels.

drop.na

Logical, if TRUE, all types of missing value codes are converted into NA before x is converted as factor. If FALSE, missing values will be left as their original codes. See 'Examples' and get_na.

ref.lvl

Numeric, specifies the reference level for the new factor. Use this parameter if a different factor level than the lowest value should be used as reference level. If NULL, lowest value will become the reference level. See ref_lvl for details.

Value

A factor variable, including variable and value labels, respectively a data frame with factor variables (including variable and value labels) if x was a data frame.

Details

See 'Details' in get_na.

See Also

to_value to convert a factor into a numeric value and to_label to convert a value into a factor with labelled factor levels.

Examples

## Not run: 
# data(efc)
# library(sjPlot)
# # normal factor conversion, loses value attributes
# efc$e42dep <- as.factor(efc$e42dep)
# sjt.frq(efc$e42dep)
# 
# # factor conversion, which keeps value attributes
# efc$e42dep <- to_factor(efc$e42dep)
# sjt.frq(efc$e42dep)## End(Not run)

data(efc)
# create parially labelled vector
x <- set_labels(efc$e42dep, c(`1` = "independent", `4` = "severe dependency",
                              `9` = "missing value"))

# only copy existing value labels
to_factor(x)
get_labels(to_factor(x), include.values = "p")

# also add labels to non-labelled values
to_factor(x, add.non.labelled = TRUE)
get_labels(to_factor(x, add.non.labelled = TRUE), include.values = "p")

# create labelled integer, with missing flag
x <- labelled(c(1, 2, 1, 3, 4, 1),
              c(Male = 1, Female = 2, Refused = 3, "N/A" = 4),
              c(FALSE, FALSE, TRUE, TRUE))
# to factor, with missing labels
to_factor(x, drop.na = FALSE)
# to factor, missings removed
to_factor(x, drop.na = TRUE)


# Convert to factor, using different reference level
x <- to_factor(efc$e42dep)
str(x)
table(x)

x <- to_factor(efc$e42dep, ref.lvl = 3)
str(x)
table(x)