Nina Zumel and I have been working on packaging our favorite graphing techniques in a more reusable way that emphasizes the analysis task at hand over the steps needed to produce a good visualization. The idea is: we sacrifice some of the flexibility and composability inherent to ggplot2 in R for a menu of prescribed presentation solutions (which we are sharing on Github).

For example the plot below showing both an observed discrete empirical distribution (as stems) and a matching theoretical distribution (as bars) is a built in “one liner.”

set.seed(52523)
d <- data.frame(wt=100*rnorm(100))
WVPlots::PlotDistCountNormal(d,'wt','example')

The graph above is actually the product of a number of presentation decisions:

All of these decisions are triggered by choosing which plot to use from the WVPlots library. In this case we chose WVPlots::PlotDistCountNormal. For an audience of analysts we might choose an area/density based representation (by instead specifying WVPlots::PlotDistDensityNormal) which is shown below:

WVPlots::PlotDistDensityNormal(d,'wt','example')

Switching the chosen plot simultaneously changes many of the details of the presentation. WVPlots is designed to make this change simple by insisting an a very simple unified calling convention. The plot calls all insist on roughly the following arguments:

This rigid calling interface is easy to remember and makes switching between plot types very easy. We have also make title a required argument, as we feel all plots should be labeled.

What we are trying to do is separate the specification of exactly what plot we want from the details of how to produce it. We find this separation of concerns and encapsulation of implementation allows us to routinely use rich annotated graphics. Below are a few more examples:

set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(x=x,y=y,yC=y>=as.numeric(quantile(y,probs=0.8)))
frm$absY <- abs(frm$y)
frm$posY = frm$y > 0
WVPlots::ScatterHist(frm, "x", "y", smoothmethod="lm", 
                     title="Example Linear Fit", annot_size=2)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing missing values
## (geom_bar).
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 1 rows containing missing values
## (geom_bar).

set.seed(34903490)
y = abs(rnorm(20)) + 0.1
x = abs(y + 0.5*rnorm(20))

frm = data.frame(model=x, value=y)

frm$costs=1
frm$costs[1]=5
frm$rate = with(frm, value/costs)

frm$isValuable = (frm$value >= as.numeric(quantile(frm$value, probs=0.8)))
gainx = 0.10  # get the top 10% most valuable points as sorted by the model

# make a function to calculate the label for the annotated point
labelfun = function(gx, gy) {
  pctx = gx*100
  pcty = gy*100
  
  paste("The top ", pctx, "% most valuable points by the model\n",
        "are ", pcty, "% of total actual value", sep='')
}

WVPlots::GainCurvePlotWithNotation(frm, "model", "value", 
                                   title="Example Gain Curve with annotation", 
                          gainx=gainx,labelfun=labelfun) 

set.seed(52523)
d = data.frame(meas=rnorm(100))
threshold = 1.5
WVPlots::ShadedDensity(d, "meas", threshold, tail="right", 
                       title="Example shaded density plot, right tail")

set.seed(34903490)
frm = data.frame(x=rnorm(50),y=rnorm(50))
frm$z <- frm$x+frm$y
WVPlots::ScatterHistN(frm, "x", "y", "z", title="Example Joint Distribution")

We know this collection doesn’t rise to the standard of a complete “grammar of graphics” or even a substantial library (which is why we are not submitting it to CRAN). But it can become (through accumulation) a re-usable repository of a number of specific graphing tasks done well. It is also a chance to eventually document presentation design decisions (though we haven’t gotten far on that yet). The complete current set of graphs is viewable as WVPlots examples at RStudio’s rpubs site.