1% Generated by roxygen2: do not edit by hand 2% Please edit documentation in R/unorder.R 3\name{step_unorder} 4\alias{step_unorder} 5\title{Convert Ordered Factors to Unordered Factors} 6\usage{ 7step_unorder( 8 recipe, 9 ..., 10 role = NA, 11 trained = FALSE, 12 columns = NULL, 13 skip = FALSE, 14 id = rand_id("unorder") 15) 16} 17\arguments{ 18\item{recipe}{A recipe object. The step will be added to the 19sequence of operations for this recipe.} 20 21\item{...}{One or more selector functions to choose variables 22for this step. See \code{\link[=selections]{selections()}} for more details.} 23 24\item{role}{Not used by this step since no new variables are 25created.} 26 27\item{trained}{A logical to indicate if the quantities for 28preprocessing have been estimated.} 29 30\item{columns}{A character string of variable names that will 31be populated (eventually) by the \code{terms} argument.} 32 33\item{skip}{A logical. Should the step be skipped when the 34recipe is baked by \code{\link[=bake.recipe]{bake.recipe()}}? While all operations are baked 35when \code{\link[=prep.recipe]{prep.recipe()}} is run, some operations may not be able to be 36conducted on new data (e.g. processing the outcome variable(s)). 37Care should be taken when using \code{skip = TRUE} as it may affect 38the computations for subsequent operations.} 39 40\item{id}{A character string that is unique to this step to identify it.} 41} 42\value{ 43An updated version of \code{recipe} with the new step added to the 44sequence of any existing operations. 45} 46\description{ 47\code{step_unorder} creates a \emph{specification} of a recipe 48step that will transform the data. 49} 50\details{ 51The factors level order is preserved during the transformation. 52 53When you \code{\link[=tidy]{tidy()}} this step, a tibble with column \code{terms} (the 54columns that will be affected) is returned. 55} 56\examples{ 57lmh <- c("Low", "Med", "High") 58 59examples <- data.frame(X1 = factor(rep(letters[1:4], each = 3)), 60 X2 = ordered(rep(lmh, each = 4), 61 levels = lmh)) 62 63rec <- recipe(~ X1 + X2, data = examples) 64 65factor_trans <- rec \%>\% 66 step_unorder(all_nominal_predictors()) 67 68factor_obj <- prep(factor_trans, training = examples) 69 70transformed_te <- bake(factor_obj, examples) 71table(transformed_te$X2, examples$X2) 72 73tidy(factor_trans, number = 1) 74tidy(factor_obj, number = 1) 75} 76\seealso{ 77Other dummy variable and encoding steps: 78\code{\link{step_bin2factor}()}, 79\code{\link{step_count}()}, 80\code{\link{step_date}()}, 81\code{\link{step_dummy_multi_choice}()}, 82\code{\link{step_dummy}()}, 83\code{\link{step_factor2string}()}, 84\code{\link{step_holiday}()}, 85\code{\link{step_indicate_na}()}, 86\code{\link{step_integer}()}, 87\code{\link{step_novel}()}, 88\code{\link{step_num2factor}()}, 89\code{\link{step_ordinalscore}()}, 90\code{\link{step_other}()}, 91\code{\link{step_regex}()}, 92\code{\link{step_relevel}()}, 93\code{\link{step_string2factor}()}, 94\code{\link{step_unknown}()} 95} 96\concept{dummy variable and encoding steps} 97