superbPlot point and individual-line layout for within-subject design

Source: R/functionsPlotting_moderate.R

superbPlot comes with a few built-in templates for making the final plots. All produces ggplot objects that can be further customized. Additionally, it is possible to add custom-make templates (see vignette 6). The functions, to be "superbPlot-compatible", must have these parameters:

superbPlot.pointindividualline(
  summarydata,
  xfactor,
  groupingfactor,
  addfactors,
  rawdata,
  datapointParams = list(),
  pointParams = list(),
  lineParams = list(),
  errorbarParams = list(),
  facetParams = list()
)

Arguments

summarydata

a data.frame with columns "center", "lowerwidth" and "upperwidth" for each level of the factors;

xfactor

a string with the name of the column where the factor going on the horizontal axis is given;

groupingfactor

a string with the name of the column for which the data will be grouped on the plot;

addfactors

a string with up to two additional factors to make the rows and columns panels, in the form "fact1 ~ fact2";

rawdata

always contains "DV" for each participants and each level of the factors

datapointParams

(optional) list of graphic directives that are sent to the geom_point layer of the individual lines

pointParams

(optional) list of graphic directives that are sent to the geom_point layer

lineParams

(optional) list of graphic directives that are sent to the geom_line layer; the parameter colorize can be used to obtain distinct colors for decreasing segments of line (colorize = "bySlope"), to obtain distinct colors for each participants (colorize = "byId"), or to have them all gray (default with colorize = "none").

errorbarParams

(optional) list of graphic directives that are sent to the geom_superberrorbar layer

facetParams

(optional) list of graphic directives that are sent to the facet_grid layer

Value

a ggplot object

Examples

# This will make a plot with points and individual lines for each subject's scores

# we take the Orange built-in data.frame but shorten the names...
names(Orange) <- c("Tree","age","circ")
# Makes the plot:
 superb( circ ~ age | Tree, 
   Orange, 
   adjustments = list(purpose = "difference", decorrelation = "none"),
   plotLayout= "pointindividualline"
 )


# if you extract the data, you can 
# run this layout directly
#processedData <- superb( circ ~ age | Tree, 
#  Orange,
#  adjustments = list(purpose = "difference", decorrelation = "none"),
#)
#
#superbPlot.pointindividualline(processedData$summaryStatistic,
#   "age",
#   NULL,
#   ".~.",
#   processedData$rawData)