superbPlot() comes with seven built-in layouts for plotting your data. However, it is possible to add additional, custom-made layouts. In this vignette, we present rapidly the existing layouts, then show how to supplement superb with your own layouts.

The built-in plot layouts

When calling superbPlot(), you use the plotStyle = "layout" option to indicate which layout you wish to use. Internally, superbPlot() is calling a function whose name is superbPlot."layout"(). For example, with plotStyle = "line", the plot is actually performed by the function superbPlot.line().

The seven layout available in superbPlot package are :

To determine if a certain function is superbPlot-compatible, use the following function:

## [1] TRUE

where you put between quote the name of a function. When devising your own, custom-made function, it is a good thing to check that it is superbPlot-compatible.

Illustrating the built-in layouts

To get a sense of the currently available layouts, we first generate a dataset composed of randomly generated scores mimicking a 3 \(\times\) 2 design with three degrees of Difficulties (as a between-group factor) and two days of testing (as a within-subject factor). It is believed (and simulated) that all two factors have main effets on the scores.

testdata <- GRD(
    RenameDV   = "score", 
    SubjectsPerGroup = 25, 
    BSFactors  = "Difficulty(3)", 
    WSFactors  = "Day(day1, day2)",
    Population = list(mean = 65,stddev = 12,rho = 0.5),
    Effects    = list("Day" = slope(-5), "Difficulty" = slope(3) )
##   id Difficulty score.day1 score.day2
## 1  1          1   44.95373   61.84914
## 2  2          1   81.13753   65.48081
## 3  3          1   72.65736   69.71055
## 4  4          1   56.28040   64.65367
## 5  5          1   75.67805   50.66005
## 6  6          1   53.38825   74.31316

For simplicity, we define a function whose arguments are the dataset and the layout:

mp <- function(data, style, ...) {
        WSFactors = "Day(2)",
        BSFactors = "Difficulty",
        variables = c("score.day1", "score.day2"),
        adjustments = list(purpose="difference", decorrelation="CM"),
        plotStyle = style,
    )+labs(title = paste("Layout is ''",style,"''",sep=""))

Lets compute the plots will the first six built-in layouts and show them

p1 <- mp(testdata, "bar")
p2 <- mp(testdata, "point")
p3 <- mp(testdata, "line")
p4 <- mp(testdata, "pointjitter" )
p5 <- mp(testdata, "pointjitterviolin")
p6 <- mp(testdata, "pointindividualline")

**Figure 1a**. Look of the six built-in layouts on the same random dataset

Figure 1a. Look of the six built-in layouts on the same random dataset

The last format, a raincloud plot (Allen et al., 2021), is better seen with coordinates flipped over:

mp(testdata, "raincloud") + coord_flip()
**Figure 1b**. The seventh layout, the raincloud

Figure 1b. The seventh layout, the raincloud

For more controls, you can manually set the colors, the fills and/or the shapes, as done here in a list:

ornate = list( 
    scale_colour_manual( name = "Difference", 
        labels = c("Easy", "Hard", "Unthinkable"), 
        values = c("blue", "black", "purple")) ,
    scale_fill_manual( name = "Difference", 
        labels = c("Easy", "Hard", "Unthinkable"), 
        values = c("blue", "black", "purple")) ,
    scale_shape_manual( name = "Difference", 
        labels = c("Easy", "Hard", "Unthinkable") ,
        values = c(0, 10, 13)) ,
    theme_bw(base_size = 9) ,
    labs(x = "Days of test", y = "Score in points" ),
    scale_x_discrete(labels=c("1" = "Former day", "2" = "Latter day"))
    p1+ornate, p2+ornate, p3+ornate,
    p4+ornate, p5+ornate, p6+ornate,
**Figure 2a**. The six built-in template with ornamental styling added.

Figure 2a. The six built-in template with ornamental styling added.

As of the raincloud with the same ornaments, we get

mp(testdata, "raincloud") + coord_flip() + ornate