Both box-and-whisker plot and bar chart are appropriate graphics
for ANOVA according to The R Book (Crawley, 2013),
but which is more appropriate? I suppose it depends on situation… can anybody help me?
Boxplot – When to Use Boxplot and When to Use Barplot: Rules of Thumb
barplotboxplotrule-of-thumb
Related Solutions
barplot() is just a wrapper for rect(), so you could add the bars yourself. This could be a start:
x <- sort(sample(1:100, 10, replace=FALSE)) # x-coordinates
y <- log(x) # y-coordinates
yD <- c(0, 2*diff(y)) # twice the change between steps
barW <- 1 # width of bars
plot(x, y, ylim=c(0, log(100)), pch=16)
rect(xleft=x-barW, ybottom=0, xright=x+barW, ytop=yD, col=gray(0.5))
Your second idea could be realized by splitting the device region with par(fig).
par(fig=c(0, 1, 0.30, 1)) # upper device region
plot(x, y, ylim=c(0, log(100)), pch=16)
par(fig=c(0, 1, 0, 0.45), bty="n", new=TRUE) # lower device region
plot(x, y, type="n", ylim=c(0, max(yD))) # empty plot to get correct axes
rect(xleft=x-barW, ybottom=0, xright=x+barW, ytop=yD, col=gray(0.5))
Some people call this a cascade chart, but cascade chart means waterfall chart to other people. Check out this tutorial on my blog: Variable Width Column Charts (Cascade Charts).
For a regular Marimekko chart, check this one: Marimekko Charts.
Best Answer
Specifically for graphical illustration of ANOVA:
A box plot or bar chart is much better than nothing graphically for ANOVA, but as commonly plotted, both are indirect or incomplete as a graphical summary.
ANOVA is about comparisons of means in a context of variations of one or more kinds, so the most appropriate graphic would show, minimally, means as well as the raw data. Group standard deviations (SDs) or related quantities would do no harm.
Although some varieties of box plots show means as well as medians, the standard kind shows medians, quartiles and some information in the tails of the distribution. The most common variant seems to be that in which individual data points are shown if and only if they lie more than 1.5 IQR away from the nearer quartile. That is: interquartile range IQR $=$ upper quartile $-$ lower quartile, so plot as points values greater than upper quartile $+$ 1.5 IQR or less than lower quartile $-$ 1.5 IQR. Such a convention can be helpful at showing gross outliers which may be problematic for ANOVA, but neither medians nor quartiles play any part in ANOVA and whether medians approximate means is a point to be checked, not assumed. Commonly, experienced data analysts take e.g. pronounced marked outliers and/or asymmetry of distribution as a sign of a problem that needs action, such as transformation of the data or need for a generalized linear model with a non-identity link function. Nevertheless it is surprising how many textbook and other accounts show box plots when an ANOVA is being presented but don't mention the elephants not in the room, the means that are not plotted.
Conversely, the most common kind of bar chart in this context summarizes data by means and SDs or standard errors, but omits any display of individual data points otherwise. So, for example, outliers or marked asymmetry can only be inferred from out-of-line means or inflated variability within individual groups.
Generally, there are many suggestions of which kinds of graphs are useful but little consensus about which are best. I'd suggest as criteria that a good graph shows
The complete pattern of variation in the data, at least as backdrop or context
Relevant summaries of the data, specifically those relevant to the model being entertained or the descriptors being considered
Indications of possible problems with the data that cast doubt on assumptions being made.
There are several designs that help with ANOVA, such as dot or strip plots with added means and SEs.
This paper by John Tukey explains the difference between propaganda graphs and analytical graphs that is pertinent here. Too many graphical illustrations of ANOVA are propaganda graphs (look! the groups are very different) without much analysis (and what else can we learn about the data or the limitations of the technique in this application?).