The textbook I use in my intro stats course makes extensive use of dotplots as an intuitive alternative to histograms when the number of data points is small enough to visualize each case as a single dot. Here are two base graphics and one ggplot solution. I especially like the last option below.

Here base graphics function **stripchart()** takes some experimentation to get workable values for the parameters offset and at. By contrast, qplot and ggplot handle this as easily as a histogram. Note however the vertical axis label “counts” in the qplot/ggplot dotplot clearly doesn’t match the scale of the axis, which looks more like a distribution, but probably not because I think the sum over that axis would be greater than 1. In fact the documentation of **geom_dotplot** admits to this error: *“When binning along the x axis and stacking along the y axis, the numbers on y axis are not meaningful, due to technical limitations of ggplot2.” *Argh…..

We should probably hide the y axis with **+scale_y_continuous(NULL, breaks = NULL)** I learned from stackoverflow of another method using base graphics **plot()** together with **sort()**, **sequence()**, and** table()**. After some needed attention to the axis labels, this option looks to me like the winner of the bunch..

stripchart(StudentSurvey$Height, method = "stack", offset = .5,
at = .1, pch = 19)
qplot(Height,data=StudentSurvey,geom="dotplot")
G=ggplot(data=StudentSurvey)
G+geom_dotplot(aes(x=Height))
#Another way to get the job done using base graphics
x=StudentSurvey$Height
plot(sort(x), sequence(table(x)))
#Here's a custom function to make this last thing happen with appropriate labels
dotty=function(x){plot(sort(x), sequence(table(x)),ylab="count",xlab=deparse(substitute(x)))}

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