v1.2.0

DESCRIPTION

@@ -1,16 +1,16 @@
 Package: cleaner
 Title: Fast and Easy Data Cleaning
 Version: 1.2.0
-Date: 2019-10-31
+Date: 2019-11-04
 Authors@R: 
   person(
     given = c("Matthijs", "S."),
     family = "Berends",
     email = "[email protected]",
     role = c("aut", "cre"),
     comment = c(ORCID = "0000-0001-7620-1800"))
-Description: Data cleaning functions for classes 'logical',
-  'factor', 'numeric', 'character', 'currency' and 'Date' to make
+Description: Data cleaning functions for classes logical,
+  factor, numeric, character, currency and Date to make
   data cleaning fast and easy. Relying on very few dependencies, it 
   provides smart guessing, but with user options to override 
   anything if needed.

R/freq.R

@@ -41,7 +41,21 @@
 #' @param f a frequency table
 #' @param n number of top \emph{n} items to return, use -n for the bottom \emph{n} items. It will include more than \code{n} rows if there are ties.
 #' @param property property in header to return this value directly
-#' @details Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. This package also has a vignette available to explain the use of this function further, run \code{browseVignettes("clean")} to read it.
+#' @details Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. 
+#' 
+#' Input can be done in many different ways. Base R methods are:
+#' \preformatted{
+#' freq(df$variable)
+#' freq(df[, "variable"])
+#' }
+#' 
+#' Tidyverse methods are:
+#' \preformatted{
+#' df$variable \%>\% freq()
+#' df[, "variable"] \%>\% freq()
+#' df \%>\% freq("variable")
+#' df \%>\% freq(variable)
+#' }
 #'
 #' For numeric values of any class, these additional values will all be calculated with \code{na.rm = TRUE} and shown into the header:
 #' \itemize{
@@ -69,17 +83,17 @@
 #' Interested in extending the \code{freq()} function with your own class? Add a method like below to your package, and optionally define some header info by passing a \code{\link{list}} to the \code{.add_header} parameter, like below example for class \code{difftime}. This example assumes that you use the \code{roxygen2} package for package development.
 #' \preformatted{
 #' #' @exportMethod freq.difftime
-#' #' @importFrom clean freq.default
+#' #' @importFrom cleaner freq.default
 #' #' @export
 #' #' @noRd
 #' freq.difftime <- function(x, ...) {
 #'   freq.default(x = x, ...,
 #'                .add_header = list(units = attributes(x)$units))
 #' }
 #' }
-#' Be sure to call \code{freq.default} in your function and not just \code{freq}. Also, add \code{clean} to the \code{Imports:} field of your \code{DESCRIPTION} file, to make sure that it will be installed with your package, e.g.:
+#' Be sure to call \code{freq.default} in your function and not just \code{freq}. Also, add \code{cleaner} to the \code{Imports:} field of your \code{DESCRIPTION} file, to make sure that it will be installed with your package, e.g.:
 #' \preformatted{
-#' Imports: clean
+#' Imports: cleaner
 #' }
 #' @keywords summary summarise frequency freq
 #' @rdname freq
@@ -88,22 +102,15 @@
 #' @export
 #' @exportMethod freq
 #' @examples
-#' \dontrun{
-#' 
-#' # this all gives the same results:
-#' freq(df$variable)
-#' freq(df[, "variable"])
-#' df$variable %>% freq()
-#' df[, "variable"] %>% freq()
-#' df %>% freq("variable")
-#' df %>% freq(variable) # <- tidyverse way
-#' }
+#' freq(unclean$gender, markdown = FALSE)
 #' 
-#' clean_gender <- clean_factor(unclean$gender, 
-#'                              levels = c("^m" = "Male", 
-#'                                         "^f" = "Female"))
-#' freq(unclean$gender)
-#' freq(clean_gender)
+#' freq(x = clean_factor(unclean$gender, 
+#'                       levels = c("^m" = "Male", 
+#'                                  "^f" = "Female")),
+#'      markdown = TRUE,
+#'      title = "Frequencies of a cleaned version for a markdown report!",
+#'      header = FALSE,
+#'      quote = TRUE)
 freq <- function(x, ...) {
   UseMethod("freq")
 }

R/helpers.R

@@ -56,8 +56,8 @@ round2 <- function(x, digits = 0, force_zero = TRUE) {
   # https://stackoverflow.com/a/12688836/4575331
   val <- (trunc((abs(x) * 10 ^ digits) + 0.5) / 10 ^ digits) * sign(x)
   if (digits > 0 & force_zero == TRUE) {
-    val[val != as.integer(val)] <- paste0(val[val != as.integer(val)],
-                                          strrep("0", max(0, digits - nchar(gsub(".*[.](.*)$", "\\1", val[val != as.integer(val)])))))
+    val[val != as.integer(val) & !is.na(val)] <- paste0(val[val != as.integer(val) & !is.na(val)],
+                                                        strrep("0", max(0, digits - nchar(gsub(".*[.](.*)$", "\\1", val[val != as.integer(val) & !is.na(val)])))))
   }
   val
 }

R/percentage.R

@@ -44,16 +44,6 @@
 #' 
 #' round(0.4455 * 100, 1) # mind the rounding
 #' percentage(0.4455) # does not round to 44.5%
-#' 
-#' \dontrun{
-#' library(ggplot2)
-#' ggplot(data.frame(a = LETTERS[1:6],
-#'                   b = runif(6)),
-#'        aes(a, b)) +
-#'   geom_col() + 
-#'   geom_label(aes(label = percentage(b))) +
-#'   scale_y_continuous(labels = percentage) 
-#' }
 as.percentage <- function(x, ...) {
   if (is.percentage(x)) {
     return(x)