#' @title Creates a CorrTask Objects
#'
#' @description
#' A Task encapsulates the Data with some additional information
#'
#' @param id [\code{character(1)}]\cr
#' ID of the Task Object
#' @param data [\code{data.frame}]\cr
#' A Dataframe with different variables
#' @param method [\code{character(1)}]\cr
#' Defines the correlation method
#' Possible choices are:
#' \dQuote{pearson}, \dQuote{spearman}, \dQuote{kendall} \cr
#' Default method is \code{method = "pearson"}
#' @param vars [\code{character(1)}]\cr
#' Column names to use for correlation
#' @param type [\code{character(1)}]\cr
#' The type of the Report to create. Example: "CorrPlot"
#' @param show.NA.msg [\code{logical(1)}]\cr
#' Logical whether to show missing values message\cr
#' Default is \code{FALSE}.
#' @param ...
#' For now has no use
#' @return CorrTask
#'
#' @examples
#' corr.task = makeCorrTask(id = "test", data = cars)
#' # Extract Data
#' corr.task$env$data
#' @import checkmate
#' @import BBmisc
#' @export
makeCorrTask = function(id, data, method = "pearson", vars = NULL,
type = "CorrPlot", show.NA.msg = FALSE, ...){
# Argument Checks
assertCharacter(id, min.chars = 1L)
assertDataFrame(data, col.names = "strict")
assertSubset(method, c("pearson", "spearman", "kendall"), empty.ok = FALSE)
assertSubset(type, choices = "CorrPlot")
#add warning for NAs:
if (any(is.na(data)) & show.NA.msg) {
message("The data set contains NAs.
These values might removed in the further calculations.
If so, another warning will be displayed.")
}
if (!is.null(vars)) {
assertCharacter(vars, min.chars = 1L, min.len = 2L)
data.type = getDataType(data[, vars], target = NULL)
} else{
data.type = getDataType(data, target = NULL)
}
# Encapsulate Data into new env
env = new.env(parent = emptyenv())
env$data = data
# For pearson no ordinal features
if (method == "pearson") {
data.types = data.type[c("num", "int")]
} else {
data.types = data.type[c("num", "int", "ord")]
}
makeS3Obj("CorrTask",
id = id,
env = env,
features = data.types,
size = nrow(data),
method = method,
data.name = deparse(substitute(data)),
needed.pkgs = NULL,
missing.values = sum(is.na(data)),
type = type)
}
#' @export
# Print fuction for CorrTask Object
print.CorrTask = function(x, ...) {
catf("Task: %s", x$id)
catf("Type: %2s", x$type)
catf("Selected Features: %s", collapse(unlist(x$features), sep = ", "))
catf("Observations: %i", x$size)
catf("Method: %s", x$method)
catf("Missing Values: %s", x$missing.values)
catf("Name of the Data: %s", x$data.name)
catf("Seleted type : %s", x$type)
catf("Needed packages: %s", if (is.null(x$needed.pkgs)) {"None"} else{x$needed.pkgs})
}
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