View source: R/functionsPlotting_base.R
superbPlot.pointindividualline | R Documentation |
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, pointParams = list(), lineParams = list(), errorbarParams = list(), facetParams = list() )
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 |
pointParams |
(optional) list of graphic directives that are sent to the geom_bar layer |
lineParams |
(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 |
facetParams |
(optional) list of graphic directives that are sent to the facet_grid layer |
a ggplot object
# This will make a plot with points and individual lines for each subject's scores library(lsr) # we take the Orange built-in data.frame which has a within-subject design names(Orange) <- c("Tree","age","circ") # turn the data into a wide format Orange.wide <- longToWide(Orange, circ ~ age) # the identifier to each tree must be in a column called id Orange.wide$id = Orange.wide$Tree # Makes the plots two different way: superbPlot( Orange.wide, WSFactors = "age(7)", variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"), adjustments = list(purpose = "difference", decorrelation = "none"), plotStyle= "pointindividualline" ) # if you extract the data with superbData, you can # run this layout directly #processedData <- superbData(Orange.wide, WSFactors = "age(7)", # variables = c("circ_118","circ_484","circ_664","circ_1004","circ_1231","circ_1372","circ_1582"), # adjustments = list(purpose = "difference", decorrelation = "none"), #) # #superbPlot.pointindividualline(processedData$summaryStatistic, # "age", # NULL, # ".~.", # processedData$rawData)
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