superbPlot.boxplot: superbPlot 'boxplot' layout

View source: R/functionsPlotting_boxplot.R

superbPlot.boxplotR 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))  
 


superb documentation built on May 29, 2024, 8:51 a.m.