Add Contributing.md

NAMESPACE

@@ -1 +1,2 @@
 exportPattern("^[[:alpha:]]+")
+importFrom("stats", "quantile")

R/meanimpute.R

@@ -1,4 +1,6 @@
 #' Meanimputation
+#' @param x A vector.
+#' 
 #' @export
 meanimpute <- function(x) {
   x[is.na(x)] <- mean(x, na.rm = TRUE)

R/transform_log.R

@@ -0,0 +1,16 @@
+#' Transform_log
+#' 
+#' Log transformation.
+#' 
+#' @param x A vector.
+#' 
+#' @examples
+#' transform_log(exp(rnorm(2)))
+#' 
+#' @export
+
+transform_log <- function(x) {
+  if (any(x<0)) {stop("log values must be positive")}
+  x <- log(x)
+  x
+}

R/windsorize.R

@@ -1,10 +1,25 @@
 #' Windsorize
 #'
-#' Do some windsorization.
+#' Winsorizing or winsorization is the transformation of statistics by limiting 
+#' extreme values to reduce the effect of 
+#' spurious outliers in statistical data.
+#' 
+#' @param x A vector.
+#' @param p A quantile.
+#' 
+#' @examples 
+#' windsorize(rnorm(5))
+#' 
 #' @export
 windsorize <- function(x, p = .90) {
-  q <- quantile(x, p)
-  x[x >= q] <- q
+  if (is.null(x)) {
+    stop("nul vector")}
+    if(is.na(x)){
+      stop("NA vector")}
+  q_low <- quantile(x, 0.5-p/2)
+  q_high <- quantile(x, 0.5+p/2)
+  x[x >= q_high] <- q_high
+  x[x <= q_low] <- q_low
   x
 }
 

tests/testthat.R

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+library(testthat)
+library(datacleaner)
+
+test_check("datacleaner")
+
+

tests/testthat/test_transform_log.R

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+context("No negative values")
+library(datacleaner)
+test_that("There are no negative values in input", {
+  expect_error(transform_log(c(1,2,-1)), "input can't be negative")
+})

tests/testthat/test_windsorize.R

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+context("NA and null values")
+library(datacleaner)
+test_that("NA and null values produce error message", {
+  expect_error(windsorize(NA), "argument should not be a vector containing only NA-s or NULL-s")
+  expect_error(windsorize(NULL), "argument should not be a vector containing only NA-s or NULL-s")
+})