Nothing

```
#' Replace missing data with median ± random noise
#'
#' Replace missing data within each numeric column of a data frame with
#' the column median, plus or minus some random noise, in order to train
#' classifiers that do not easily ignore missing data (e.g. random forests or
#' support vector machines).
#'
#' @param dat the data frame to replace missing data in
#' @param noise_pct the standard deviation of the random normal
#' distribution from which to draw added noise, expressed as a
#' percentage of the standard deviation of the non-missing values in each
#' column
#'
#' @return a data frame with missing values in each numeric column replaced
#' by the column median, plus or minus some random noise
#'
#' @importFrom stats rnorm
#'
#' @export
replace_missing_data <- function(dat, noise_pct = 0.05) {
for (col_name in colnames(dat)) {
column <- dat[[col_name]]
if (!is.numeric(column))
next
missing <- is.na(column)
dat[[col_name]][missing] <-
median(column, na.rm = TRUE) + rnorm(sum(missing)) *
sd(column, na.rm = TRUE) * noise_pct
}
return(dat)
}
```

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