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R/transform_log.R

@@ -0,0 +1,20 @@
+#' transform_log
+#'
+#' log-transformation of a numeric vector. For details about log-transformation please see you basic school math textbook.
+#' @param x a numeric vector
+#' @return log-transformed vector \code{x}
+#' @examples
+#' example_vector=c(1,2,3,4,5,6,7,8,9,10)
+#' transform_log(example_vector)
+#' @export
+
+transform_log <- function(x){
+
+  if( is.null(x) )    stop("Input vector is not allowed to be NULL.")
+  if( any(is.na(x)) ) stop("There is at least one NA value in input vector.")
+  if( any(x <= 0)  )  stop("There is at least one negative value.")
+  if( any(is.numeric(x) == FALSE) )  stop("There is at least one non-numeric value.")
+  y<-log(x)
+  return(y)
+
+}

R/windsorize.R

@@ -1,10 +1,36 @@
-#' Windsorize
-#'
-#' Do some windsorization.
-#' @export
-windsorize <- function(x, p = .90) {
-  q <- quantile(x, p)
-  x[x >= q] <- q
-  x
-}
-
+#' Windsorize
+#'
+#' Its purposes is to eliminate outliers in a following way. Values of (0.5 +- p/2)th quantiles are calculated and all
+#' values above(below) those quantiles are replaced by the quantiles.
+#' @param x a numeric vector
+#' @param p quantile
+#' @return Windsorized  vector \code{x}
+#' @examples
+#' example_vector=c(-1000,1,2,3,4,5,6,7,8,9,1000)
+#' windsorize(example_vector, 0.9)
+#' 
+#' example_vector=rnorm(100)
+#' windsorize(example_vector, 0.9)
+#' @export
+#' @import stats
+
+windsorize <- function(x, p = .90) {
+
+  if(is.null(x)) {stop("Input vector cannot be NULL.")}
+  if(any(is.na(x))) {stop("There should be no NA's in input vector.")}
+  if(all(is.numeric(x)==FALSE)) {stop("There should only numeric values in the input vector.")}
+
+  if(is.na(p)==TRUE) {stop("Input quantile should be a number between 0 and 1 ")}
+  if(is.numeric(p)==FALSE) {stop("Input quantile should be a number between 0 and 1 ")}
+  if(p > 1) {stop("Input quantile should be a number between 0 and 1 ")}
+  if(p < 0) {stop("Input quantile should be a number between 0 and 1 ")}
+
+
+  q_u <- quantile(x, 0.5 + p/2)
+  x[x >= q_u] <- q_u
+
+  q_l <- quantile(x, 0.5 - p/2)
+  x[x <= q_l] <- q_l
+
+  return(x)
+}