superbPlot.boxplot {superb} | R Documentation |
superbPlot 'boxplot' layout
Description
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 create custom-make templates (see vignette 5). The functions, to be "superbPlot-compatible", must have these parameters:
Usage
superbPlot.boxplot(
summarydata,
xfactor,
groupingfactor,
addfactors,
rawdata = NULL,
pointParams = list(),
errorbarParams = list(),
facetParams = list(),
boxplotParams = list(),
xAsFactor = TRUE
)
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; |
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; |
facetParams |
(optional) list of graphic directives that are sent to the facet_grid layer; |
boxplotParams |
(optional) list f graphic directives that are sent to the geo_boxplot layer; |
xAsFactor |
(optional) Boolean to indicate if the factor on the horizontal should continuous or discrete (default is discrete). |
Value
a ggplot object
Examples
# This will make a plot with boxes for interquartile (box), median (line) and outliers (whiskers)
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"), variables = "len",
plotStyle = "boxplot"
)
# This layout of course is more meaningful if the statistic displayed is the median
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"),
variables = "len",
statistic = "median",
plotStyle = "boxplot"
)
# if you extracted the data with superbData, you can
# run this layout directly
processedData <- superbData(ToothGrowth,
BSFactors = c("dose","supp"), variables = "len", statistic = "median"
)
superbPlot.boxplot(processedData$summaryStatistic,
"dose", "supp", ".~.",
processedData$rawData)
# This will make a plot with customized boxplot parameters and black dots
superbPlot(ToothGrowth,
BSFactors = c("dose","supp"), variables = "len",
statistic = "median",
plotStyle = "boxplot",
boxplotParams = list( outlier.shape=8, outlier.size=4 ),
pointParams = list(color="black")
)
# You can customize the plot in various ways, e.g.
plt3 <- superbPlot(ToothGrowth,
BSFactors = c("dose","supp"), variables = "len",
statistic = "median",
plotStyle = "boxplot",
pointParams = list(color="black")
)
# ... by changing the colors of the fillings
library(ggplot2) # for scale_fill_manual, geom_jitter and geom_dotplot
plt3 + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# ... by overlaying jittered dots of the raw data
plt3 + geom_jitter(data = processedData$rawData, mapping=aes(x=dose, y=DV),
position= position_jitterdodge(jitter.width=0.5 , dodge.width=0.8 ) )
# ... by overlaying dots of the raw data, aligned along the center of the box
plt3 + geom_dotplot(data = processedData$rawData, mapping=aes(x=dose, y=DV), dotsize=0.5,
binaxis='y', stackdir='center', position=position_dodge(0.8))