visualize: Produce distribution plots

In ekstroem/cleanR: A Suite of Checks for Identification of Potential Errors in a Data Frame as Part of the Data Screening Process

Description

Generic shell function that calls a plotting function in order to produce a marginal distribution plot for a variable (or for each variable in a dataset). What type of plot is made might depend on the data class of the variable.

Usage

visualize(v, vnam = NULL, visuals = setVisuals(), doEval = TRUE, ...)

Arguments

v	The variable (vector) or dataset (data.frame) which is to be plotted.
vnam	The name of the variable. This name might be printed on the plots, depending on the choice of plotting function. If not supplied, it will default to the name of v.
visuals	A list of visual functions to use on each supported variable type. We recommend using setVisuals for creating this list and refer to the documentation of this function for more details. This function allows for choosing variable-type dependent visuals. However, if visualize() is called on a full dataset, all visualizations must be of the same type and therefore, the all argument of setVisuals is used.
doEval	A logical. If TRUE (the default), visualize has the side effect of producing a plot (or multiple plots, if v is a data.frame). Otherwise, visualize returns a character string containing R-code for producing the plot (or, when v is a data.frame, a list of such character strings).
...	Additional arguments used for class-specific choices of visual functions (see details).

Details

Visual functions can be supplied using their names (in character strings) using setVisuals. Note that only a single visual function is allowed for each variable class. The default visual settings can be inspected by calling setVisuals(). An overview of all available visualFunctions can be obtained by calling allVisualFunctions.

A user defined visual function can be supplied using its function name. Details on how to construct valid visual functions are found in visualFunction.

References

Petersen AH, Ekstrøm CT (2019). “dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R.” _Journal of Statistical Software_, *90*(6), 1-38. doi: 10.18637/jss.v090.i06 ( https://doi.org/10.18637/jss.v090.i06).

See Also

setVisuals, allVisualFunctions, standardVisual, basicVisual

Examples

 #Standard use: Return standalone code for plotting a function:
   visualize(c(1:10), "Variable 1", doEval = FALSE)

 #Define a new visualization function and call it using visualize either
 #using allVisual or a class specific argument:
   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())
  
  #Inspect all options for visualFunctions:
  allVisualFunctions()


 ## Not run: 
   #set mosaicVisual for all variable types:
   visualize(c("1", "1", "1", "2", "2", "a"), "My variable", 
       visuals = setVisuals(all = "mosaicVisual"))

   #set mosaicVisual only for character variables:
   visualize(c("1", "1", "1", "2", "2", "a"), "My variable", 
      visuals = setVisuals(character = "mosaicVisual"))

   #this will use standardVisual, as our variable is not numeric:
   visualize(c("1", "1", "1", "2", "2", "a"), "My variable", 
       visuals = setVisuals(numeric = "mosaicVisual"))
 
## End(Not run)

   #return code for a mosaic plot
   visualize(c("1", "1", "1", "2", "2", "a"), "My variable", 
       allVisuals = "mosaicVisual", doEval=FALSE)

 ## Not run: 
 #Produce multiple plots easily by calling visualize on a full dataset:
   data(testData)
   testData2 <- testData[, c("charVar", "factorVar", "numVar", "intVar")]
   visualize(testData2)
   
 #When using visualize on a dataset, datatype specific arguments have no
 #influence:
   visualize(testData2, setVisuals(character = "basicVisual", 
       factor = "basicVisual"))
   
 #But we can still use the "all" argument in setVisuals:
   visualize(testData2, visuals =  setVisuals(all = "basicVisual"))
 
## End(Not run)

ekstroem/cleanR documentation built on Jan. 31, 2022, 8:58 a.m.