R/corr_rm.R

In meantrix/corrP: corrp (Compute correlations type analysis in parallel)

#' @title corr_rm filter data.frame by correlation values.
#'
#' @description Remove highly correlated variables from a data.frame using the
#' corrp functions outputs and the caret package function \code{\link[caret]{findCorrelation}}.
#'
#'
#' @param df \[\code{data.frame(1)}]\cr input data frame.
#' @param c \[\code{clist(1)} | \code{cmatrix(1)}]\cr correlation list  output from \code{\link{corrp}} or
#' correlation matrix output from \code{\link{corr_matrix}}.
#' @param cutoff \[\code{numeric(1)}]\cr A numeric value for the pair-wise absolute correlation cutoff.
#' The default values is 0.75.
#' @param col \[\code{character(1)}]\cr choose the column to be used in the correlation matrix
#' @param isig \[\code{logical(1)}]\cr values that are not statistically significant will
#'be represented by NA or FALSE in the correlation matrix.
#' @param ... Additional arguments (TODO).
#'
#'
#' @author Igor D.S. Siciliani
#'
#' @keywords highly correlated , cmatrix , clist
#'
#' @examples
#' \dontrun{
#'
#' air_cor = corrp(airquality)
#' corr_rm(df=airquality,c=air_cor,cutoff=0.75,col = 'infer.value',isig = F)
#'
#'}
#' @export
corr_rm = function(df, c,...) {
  UseMethod('corr_rm',c)
}

#' @export
#' @rdname corr_rm
corr_rm.clist =  function(df, c, col = c('infer.value','stat.value'),
                          isig = TRUE, cutoff = 0.75,...){

  col = match.arg(col)

  m = corr_matrix(c = c, col = col , isig = isig , ...)
  rm  = .corr_rm(df = df, m = m, cutoff = cutoff)

  return(rm)

}

#' @export
#' @rdname corr_rm
corr_rm.list = function(df, c, col = c('infer.value','stat.value'),
                        isig = TRUE, cutoff = 0.75,...) {

  warning("it is not an object of the 'clist' class some results may go wrong.")

  col = match.arg(col)

  m = corr_matrix(c = c, col = col , isig = isig , ...)
  rm  = .corr_rm(df = df, m = m, cutoff = cutoff)

  return(rm)

}


#' @export
#' @rdname corr_rm
corr_rm.cmatrix = function(df, c, cutoff=0.75,...) {

  rm  = .corr_rm(df = df,m = c,cutoff = cutoff)

  return(rm)

}

#' @export
#' @rdname corr_rm
corr_rm.matrix = function(df, c, cutoff=0.75,...) {

  warning("it is not an object of the 'cmatrix' class some results may go wrong.")

  rm  = .corr_rm(df,c,cutoff)

  return(rm)

}

.corr_rm = function(df,m,cutoff=0.75){
  checkmate::assertDataFrame(df)

  #the index of the  highly correlated columns (>cutoff)
  rh.idx = caret::findCorrelation(m, cutoff = cutoff)

  #the name of the highly correlated columns (>cutoff)
  cols.rem = colnames(m)[rh.idx]

  #subset original df to remove highly correlated columns (>cutoff)
  df=df[!names(df) %in% cols.rem]

  return(df)
}