View source: R/functionsPlotting_corset.R
superbPlot.corset | R Documentation |
superbPlot comes with a few built-in templates for making the final plots. The corset plot is specifically devised for 2-repeated-measure design: it merges the "pointindividualline" layout with a raincloud layout \insertCitekb21superb. All layout produces ggplot objects that can be further customized. Additionally, it is possible to create custom-make templates (see vignette 5). The functions, to be "superbPlot-compatible", must have these parameters:
superbPlot.corset(
summarydata,
xfactor,
groupingfactor,
addfactors,
rawdata = NULL,
lineParams = list(),
pointParams = list(),
errorbarParams = list(),
jitterParams = list(),
violinParams = list(),
facetParams = list(),
xAsFactor = TRUE
)
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; |
lineParams |
(optional) list of graphic directives that are sent to the geom_line layer; |
pointParams |
(optional) list of graphic directives that are sent to the geom_bar layer; |
errorbarParams |
(optional) list of graphic directives that are sent to the geom_superberrorbar layer; |
jitterParams |
(optional) list of graphic directives that are sent to the geom_jitter layer; |
violinParams |
(optional) list of graphic directives that are sent to the geom_boxplot layer; |
facetParams |
(optional) list of graphic directives that are sent to the facet_grid layer; |
xAsFactor |
(optional) Boolean to indicate if the factor on the horizontal should continuous or discrete (default is discrete). |
a ggplot object
# We first generate randomly a 2-measurement dataset with 50 participants and a large effect
dta <- GRD(SubjectsPerGroup = 50, WSFactors = "moment(2)", Effects = list("moment"=slope(3)))
# This will make a basic corset plot
superb(
cbind(DV.1, DV.2) ~ .,
dta,
WSFactors = "moment(2)",
plotStyle = "corset"
)
# This will color the increasing and decreasing individuals
superb(
cbind(DV.1, DV.2) ~ .,
dta,
WSFactors = "moment(2)",
plotStyle = "corset",
lineParams = list(colorize=TRUE)
)
# This layout has similarities with the "pointindividualline" layout
superb(
cbind(DV.1, DV.2) ~ .,
dta,
WSFactors = "moment(2)",
plotStyle = "pointindividualline"
)
# if you extract the data with superbData, you can
# run this layout directly
processedData <- superb(
cbind(DV.1, DV.2) ~ .,
dta,
WSFactors = "moment(2)",
showPlot = FALSE
)
superbPlot.corset(processedData$summaryStatistic,
"moment", NULL, ".~.",
processedData$rawData,
lineParams = list(colorize=TRUE) )
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