NBDCtools (version 1.0.2)

convert_names_file: Convert column names in a file

Description

This function replaces all matched column names in a file with another type of column name specified in the data dictionary.

For example, this function can be used to convert script files that specified previously used column names to the the ABCD column names introduced in the 6.0 release. If you instead want to convert the column names in a data frame, use convert_names_data().

Note: Please use this function with caution and make sure that the data in the converted column is equivalent to the data in the original column. Also, please make sure that the names can be mapped one-to-one. Some variables in the ABCD data dictionary have been collapsed from previous releases and thus might have multiple names in the name_from column that map to a single name (see skip_sep_check argument below).

Usage

convert_names_file(
  file_in,
  file_out = NULL,
  dd,
  name_from,
  name_to,
  skip_sep_check = FALSE
)

Value

character. The path to the output file with converted names, invisible.

Arguments

file_in

character. The input file path.

file_out

character. The output file path. If not provided, defaults to the input file path with a "_converted" suffix.

dd

tibble. The data dictionary table. One can use get_dd() family of functions to get the data dictionary for a given study and release or provide a custom data dictionary.

name_from

character. The column name type in the data dictionary that the columns in data currently use (Default: "name"). This column must exist in the data dictionary.

name_to

character. The column name type in the data dictionary that the columns in data should be renamed to. This column must exist in the data dictionary.

skip_sep_check

logical. Whether to skip the check for name_from and name_to columns' separators validation. In our official data dictionaries, some columns have multiple names separated by a "|" in the same cell.

For columns with multiple names, it the recommended to use functions like tidyr::separate_rows() to split the names into multiple rows and decide which name to use for the renaming by filtering the rows, so that the name_from and name_to columns are one-to-one mapping.

If skip_sep_check = FALSE (default), the function will check if the name_from or name_to columns have the "|" separator and will throw an error if the separator is found. If skip_sep_check = TRUE, it means you understand that character strings with "|" inside will be used for rename mapping, and the function will not check for the separator.

Details

Word matching

The function uses word boundaries to match the names in the file. It Uses regex word boundaries (\\b) to ensure exact word matches. This prevents partial matches within larger words. For example, matching "age" will not match "cage" or "page".

Speed

The data dictionary is big from get_dd(), so the function would loop through all the names in the data dictionary. If there are only a few names to replace, it is the best to trim the data dictionary to only those names before using this function.

Examples

if (FALSE) {
convert_names_file(
  file_in = "analysis_script.R",
  dd = get_dd("abcd"),
  name_from = "name_nda",
  name_to = "name"
)

# Specify custom output file
convert_names_file(
  file_in = "analysis_script.py",
  file_out = "analysis_script_new.py",
  dd = get_dd("abcd"),
  name_from = "name_nda",
  name_to = "name"
)
}