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#' Profile missing values
#'
#' Analyze missing value profile
#' @param data input data
#' @keywords profile_missing
#' @return missing value profile, such as frequency, percentage and suggested action.
#' @import data.table
#' @export profile_missing
#' @seealso \link{plot_missing}
#' @examples
#' profile_missing(airquality)
profile_missing <- function(data) {
## Declare variable first to pass R CMD check
feature <- num_missing <- pct_missing <- group <- NULL
## Check if input is data.table
is_data_table <- is.data.table(data)
## Detect input data class
data_class <- class(data)
## Set data to data.table
if (!is_data_table) data <- data.table(data)
## Extract missing value distribution
missing_value <- data.table(
"feature" = names(data),
"num_missing" = sapply(data, function(x) {sum(is.na(x))})
)
missing_value[, feature := factor(feature, levels = feature[order(-rank(num_missing))])]
missing_value[, pct_missing := num_missing / nrow(data)][]
## Set data class back to original
if (!is_data_table) class(missing_value) <- data_class
missing_value
}
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