Vignette3.R
In superb: Summary Plots with Adjusted Error Bars

## ----echo = FALSE, warning=FALSE, message = FALSE,  results = 'hide'----------
cat("this will be hidden; use for general initializations.")
library(superb)
library(ggplot2)

## -----------------------------------------------------------------------------
head(dataFigure2)

## -----------------------------------------------------------------------------
t.test(dataFigure2$pre, dataFigure2$post, var.equal=TRUE)

## -----------------------------------------------------------------------------
t.test(dataFigure2$pre, dataFigure2$post, var.equal=TRUE, paired = TRUE)

## ----message=FALSE, echo=TRUE, fig.width = 4, fig.cap="**Figure 1**. Representation of the individual participants"----
library(reshape2)

# first transform the data in long format; the pre-post scores will go into column "variable"
dl <- melt(dataFigure2, id="id")

# add transparency when pre is smaller or equal to post
dl$trans = ifelse(dataFigure2$pre <= dataFigure2$post,0.9,1.0)

# make a plot, with transparent lines when the score increased
ggplot(data=dl, aes(x=variable, y=value, group=id, alpha = trans)) + 
    geom_line( ) +
    coord_cartesian( ylim = c(70,150) ) +
    geom_abline(intercept = 102.5, slope = 0, colour = "red", linetype=2)

## ----message=FALSE, echo=TRUE, fig.width = 4, fig.cap="**Figure 2**. Representation of the *subject-centered* individual participants"----
# use subjectCenteringTransform function 
library(superb)
df2 <- subjectCenteringTransform(dataFigure2, c("pre","post"))

# tranform into long format
library(reshape2)
dl2 <- melt(df2, id="id")

# make the plot
ggplot(data=dl2, aes(x=variable, y=value, colour=id, group=id)) + geom_line()+
    coord_cartesian( ylim = c(70,150) ) +
    geom_abline(intercept = 102.5, slope = 0, colour = "red", size = 0.5, linetype=2)

## -----------------------------------------------------------------------------
t.test(dataFigure2$pre, dataFigure2$post, paired=TRUE)

## -----------------------------------------------------------------------------
cor(dataFigure2$pre, dataFigure2$post)

## ----message=FALSE, warning=FALSE, echo=TRUE, fig.height = 3, fig.width = 4, fig.cap="**Figure 3a**. Means and difference and correlation-adjusted 95% confidence intervals"----
superbPlot(dataFigure2, 
	WSFactors    = "Moment(2)", 
	adjustments = list(
					purpose = "difference", 
					decorrelation = "CA"    ## NEW! use a decorrelation technique
	), 
	variables   = c("pre","post"), 
	plotStyle   = "line" )

## ----message=FALSE, warning=FALSE, echo=TRUE, fig.width = 4, fig.cap="**Figure 3b**. Means and 95% confidence intervals on raw data (left) and on decorrelated data (right)"----
options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages
library(gridExtra)

## realize the plot with unadjusted (left) and ajusted (right) 95\% confidence intervals
plt2a <- superbPlot(dataFigure2, 
        WSFactors    = "Moment(2)", 
        adjustments = list(purpose = "difference"), 
        variables   = c("pre","post"), 
        plotStyle   = "line" ) + 
    xlab("Group") + ylab("Score") + 
    labs(title="Difference-adjusted\n95% confidence intervals") +
    coord_cartesian( ylim = c(85,115) ) +
    theme_gray(base_size=10) + 
    scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity"))
plt2b <- superbPlot(dataFigure2, 
        WSFactors    = "Moment(2)", 
        adjustments = list(purpose = "difference", decorrelation = "CA"),  #only difference
        variables   = c("pre","post"), 
        plotStyle   = "line" ) + 
    xlab("Group") + ylab("Score") + 
    labs(title="Correlation and difference-adjusted\n95% confidence intervals") +
    coord_cartesian( ylim = c(85,115) ) + 
    theme_gray(base_size=10) +
    scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity"))
plt2  <- grid.arrange(plt2a,plt2b,ncol=2)

## ----message=FALSE, warning=FALSE, echo=FALSE, fig.width = 4, fig.cap="**Figure 4**. All three decorelation techniques on the same plot along with un-decorrelated error bars"----
# using GRD to generate data with correlation of .8 and a moderate effect
options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages
test <- GRD(WSFactors = "Moment(5)", 
            Effects = list("Moment" = extent(10) ),
            Population = list(mean = 100, stddev = 25, rho = 0.8) ) 

# the common label to all 4 plots
tlbl <- paste( "(red) Difference-adjusted only\n",
            "(blue) Difference adjusted and decorrelated with CM\n",
            "(green) Difference-adjusted and decorrelated with LM\n",
            "(orange) Difference-adjusted and decorrelated with CA\n",
			"(bisque) Difference-adjusted and decorrelated with UA", sep="")

# to make the plots all identical except for the decorrelation method
makeplot <- function(dataset, decorrelationmethod, color, nudge, dir) {
    superbPlot(dataset, 
            WSFactors = "Moment(5)",  
            variables   = c("DV.1","DV.2","DV.3","DV.4","DV.5"), 
            adjustments=list(purpose = "difference", decorrelation = decorrelationmethod), 
            errorbarParams = list(color=color, width= 0.1, position = position_nudge(nudge), direction = dir ),
            plotStyle="line" ) + 
        xlab("Moment") + ylab("Score") + 
        labs(subtitle=tlbl) +
        coord_cartesian( ylim = c(85,115) ) +
        theme_gray(base_size=10) 
}

theme_transparent <- theme(
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent",colour = NA),
        plot.background = element_rect(fill = "transparent",colour = NA)
        )

# generate the plots, nudging the error bars and using distinct colors
pltrw <- makeplot(test, "none", "red",           0.0, "both")
pltCM <- makeplot(test, "CM",   "blue",         -0.2, "left")
pltLM <- makeplot(test, "LM",   "chartreuse3",  -0.1, "left")
pltCA <- makeplot(test, "CA",   "orange",       +0.1, "right")
pltUA <- makeplot(test, "UA",   "bisque4",      +0.2, "right")

# transform the ggplots into "grob" so that they can be manipulated
pltrwg <- ggplotGrob(pltrw)
pltCMg <- ggplotGrob(pltCM + theme_transparent)
pltLMg <- ggplotGrob(pltLM + theme_transparent)
pltCAg <- ggplotGrob(pltCA + theme_transparent)
pltUAg <- ggplotGrob(pltUA + theme_transparent)

# put the grobs onto an empty ggplot 
ggplot() + 
    annotation_custom(grob=pltrwg) + 
    annotation_custom(grob=pltCMg) + 
    annotation_custom(grob=pltLMg) + 
    annotation_custom(grob=pltCAg) +
    annotation_custom(grob=pltUAg)

## ----message=FALSE, warning=FALSE, echo=TRUE, fig.width = 4, fig.cap="**Figure 5**. Means and 95% confidence intervals along with individual scores depicted as lines"----
superbPlot(dataFigure2, 
    WSFactors    = "Moment(2)", 
    adjustments = list(purpose = "difference", decorrelation = "CM"), 
    variables   = c("pre","post"), 
    plotStyle   = "pointindividualline" ) + 
xlab("Group") + ylab("Score") + 
labs(subtitle="Correlation- and Difference-adjusted\n95% confidence intervals") +
coord_cartesian( ylim = c(70,150) ) +
theme_gray(base_size=10) + 
scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity"))