dataMaid (version 1.0.0)

visualFunction: Create an object of class visualFunction

Description

Convert a function, f, into an S3 visualFunction object. This adds f to the overview list returned by an allVisualFunctions() call.

Usage

visualFunction(f, description, classes = NULL)

Arguments

f

A function. See details and examples below for the exact requirements of this function.

description

A character string describing the visualization returned by f. If NULL (the default), the name of f will be used instead.

classes

The classes for which f is intended to be called. If NULL (the default), one of two things happens. If f is not a S3 generic function, the classes attribute of f will be an empty character string. If f is a S3 generic function, an automatic look-up for methods will be conducted, and the classes attribute will then be filled out automatically. Note that the function allClasses (listing all classes used in dataMaid) might be useful.

Value

A function of class visualFunction which has to attributes, namely classes and description.

Details

visualFunction represents the functions used in visualize and makeDataReport for plotting the distributions of the variables in a dataset.

An example of defining a new visualFunction is given below. Note that the minimal requirements for such a function (in order for it to be compatible with visualize() and makeDataReport()) is the following input/output-structure: It must input exactly the following three arguments, namely v (a vector variable), vnam (a character string with the name of the variable) and doEval (a logical). The last argument is supposed to control whether the function produces a plot in the graphic device (if doEval = TRUE) or instead returns a character string including R code for generating such a plot. In the latter setting, the code must be stand-alone, that is, it cannot depend on object available in an environment. In practice, this will typically imply that the data variable is included in the code snip. It is not strictly necessary to implement the doEval = TRUE setting for the visualFunction to be compatible with makeDataReport, but we recommend doing it anyway such that the function can also be used interactively.

Note that all available visualFunctions are listed by the call allVisualFunctions() and we recommed looking into these function, if more knowledge about visualFunctions is required.

See Also

allVisualFunctions, visualize, makeDataReport

Examples

# NOT RUN {
#Defining a new visualFunction:
 mosaicVisual <- function(v, vnam, doEval) {
   thisCall <- call("mosaicplot", table(v), main = vnam, xlab = "")
   if (doEval) {
    return(eval(thisCall))
   } else return(deparse(thisCall))
 }
 mosaicVisual <- visualFunction(mosaicVisual, description = "Mosaicplots from graphics",
                                classes = allClasses())

#mosaicVisual is now included in a allVisualFunctions() call:
 allVisualFunctions()
 
#Create a mosaic plot:
 ABCvar <- c(rep("a", 10), rep("b", 20), rep("c", 5))
 mosaicVisual(ABCvar, "ABCvar", TRUE)
 
#Create a character string with the code for a mosaic plot:
 mosaicVisual(ABCvar, "ABCVar", FALSE)

#Extract or set description of a visualFunction:
 description(mosaicVisual)
 description(mosaicVisual) <- "A cubist version of a pie chart"
 description(mosaicVisual)


# }