Version 4 of the R `Hmisc`

package and version
5 of the R `rms`

package
interfaces with interactive `plotly`

graphics, which
is an interface to the `D3`

javascript graphics library. This allows
various results of statistical analyses to be viewed interactively, with
pre-programmed drill-down information. More examples will be added
here. We start with a video showing a new way to display survival
curves.

Note that plotly graphics are best used with RStudio Rmarkdown html notebooks, and are distributed to reviewers as self-contained (but somewhat large) html files. Printing is discouraged, but possible, using snapshots of the interactive graphics.

Concerning the second bullet point below, boxplots have a high ink:information ratio and hide bimodality and other data features. Many statisticians prefer to use dot plots and violin plots. I liked those methods for a while, then started to have trouble with the choice of a smoothing bandwidth in violin plots, and found that dot plots do not scale well to very large datasets, whereas spike histograms are useful for all sample sizes. Users of dot charts have to have a dot stand for more than one observation if N is large, and I found the process too arbitrary. For spike histograms I typically use 100 or 200 bins. When the number of distinct data values is below the specified number of bins, I just do a frequency tabulation for all distinct data values, rounding only when two of the values are very close to each other. A spike histogram approximately reduces to a rug plot when there are no ties in the data, and I very much like rug plots.

`rms survplotp`

video: plotting survival curves`Hmisc histboxp`

interactive html example: spike histograms plus selected quantiles, mean, and Gini’s mean difference - replacement for boxplots - show all the data! Note bimodal distributions and zero blood pressure values for patients having a cardiac arrest.