encode_binary_cats {eHDPrep}

Encode categorical variables as binary factors

Description

In a data frame, converts binary categories to factors. Ordering of levels is standardised to: negative_finding, positive_finding. This embeds a standardised numeric relationship between the binary categories while preserving value labels.

Usage

encode_binary_cats(data, ..., values = NULL)

Arguments

data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr).

...

<tidy-select> One or more unquoted expressions separated by commas. Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables.

values

Optional named vector of user-defined values for binary values using binary_label_1 = binary_label_2 syntax (e.g. c("No" = "Yes") would assign level 1 to "No" and 2 to "Yes").

Details

Binary categories to convert can be specified with a named character vector, specified in values. The syntax of the named vector is: negative_finding = positive_finding. If values is not provided, the default list will be used: "No"="Yes", "No/unknown" = "Yes", "no/unknown" = "Yes", "Non-user" = "User", "Never" = "Ever", "WT" = "MT".

Value

dataset with specified binary categories converted to factors.

Examples

# use built-in values. Note: rural_urban is not modified
# Note: diabetes is not modified because "missing" is interpreted as a third category.
# strings_to_NA() should be applied first
encode_binary_cats(example_data, hypertension, rural_urban)

# use custom values. Note: rural_urban is now modified as well.
encoded_data <- encode_binary_cats(example_data, hypertension, rural_urban,
                   values = c("No"= "Yes", "rural" = "urban"))

# to demonstrate the new numeric encoding:
dplyr::mutate(encoded_data, hypertension_num = as.numeric(hypertension), .keep = "used")