R/nanull.R

In oliver-wyman-actuarial/easyR: Helpful Functions from Oliver Wyman Actuarial Consulting

#' NA / NULL Check
#' 
#' Facilitates checking for missing values which may cause errors later in code. 
#' NULL values can cause errors on is.na checks, and is.na can cause warnings if it is inside if() and is passed multiple values.
#' This function makes it easier to check for missing values before trying to operate on a variable. 
#' It will NOT check for strings like ""  or "NA". Only NULL and NA values will return TRUE.
#' Author: Bryce Chamberlain. Tech Review: Maria Gonzalez.
#'
#' @param x Vector to check. In the case of a data frame or vector, it will check the first (non-NULL) value.
#' @param na_strings (Optional) Set the strings you want to consider NA. These will be applied after stringr::str_trim on x.
#' @param do.test.each Return a vector of results to check each element instead of checking the entire object.
#'
#' @return True/false indicating if the argument is NA, NULL, or an empty/NA string/vector. For speect, only the first value is checked.
#' @export
#'
#' @examples
#' nanull( NULL )
#' nanull( NA )
#' nanull( c( NA , NULL ) )
#' nanull( c( 1, 2, 3 ) )
#' nanull( c( NA, 2, 3 ) )
#' nanull( c( 1, 2, NA ) ) # only the first values is checked, so this will come back FALSE.
#' nanull( c( NULL, 2, 3 ) ) # NULL values get skipped in a vector.
#' nanull( data.frame() )
#' nanull( dplyr::group_by( dplyr::select( cars, speed, dist ), speed ) ) # test a tibble.
nanull <- function( x, na_strings = easyr::nastrings, do.test.each = FALSE ) !isval(x, na_strings, do.test.each = do.test.each )