Some example data science plots in R using ggplot2. See for code/details.

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


Scatterplot with smoothing line through points. Reports the square of the correlation between x and y (R-squared) and its significance.

WVPlots::ScatterHist(frm, "x", "y", title="Example Fit")
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'

Scatterplot with best linear fit through points. Reports the R-squared and significance of the linear fit.

WVPlots::ScatterHist(frm, "x", "y", smoothmethod="lm", 
                     title="Example Linear Fit", annot_size=2)

Scatterplot compared to the line x = y. Reports the square of the correlation between x and y (R-squared) and its significance.

WVPlots::ScatterHist(frm, "x", "y", smoothmethod="identity", 
                     title="Example Relation Plot", annot_size=2)

Scatterplot of (x, y) color-coded by category/group, with marginal distributions of x and y conditioned on group.

fmScatterHistC = data.frame(x=rnorm(50),y=rnorm(50))
fmScatterHistC$cat <- fmScatterHistC$x+fmScatterHistC$y>0
WVPlots::ScatterHistC(fmScatterHistC, "x", "y", "cat", title="Example Conditional Distribution")

Scatterplot of (x, y) color-coded by discretized z. The continuous variable z is binned into three groups, and then plotted as by ScatterHistC

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

Plot the relationship y as a function of x with a smoothing curve that estimates \(E[y | x]\). If y is a 0/1 variable as below (binary classification, where 1 is the target class), then the smoothing curve estimates \(P(y | x)\). Since \(y \in \{0,1\}\) with \(y\) intended to be monotone in \(x\) is the most common use of this graph, BinaryYScatterPlot uses a glm smoother by default (use_glm=TRUE, this is essentially Platt scaling), as the best estimate of \(P(y | x)\).

WVPlots::BinaryYScatterPlot(frm, "x", "posY", use_glm=FALSE,
                            title="Example 'Probability of Y' Plot (ggplot2 smoothing)")
## `geom_smooth()` using method = 'loess'

WVPlots::BinaryYScatterPlot(frm, "x", "posY", use_glm=TRUE, 
                            title="Example 'Probability of Y' Plot (GLM smoothing)")

Gain Curves

y = abs(rnorm(20)) + 0.1
x = abs(y + 0.5*rnorm(20))

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

frm$rate = with(frm, value/costs)

frm$isValuable = (frm$value >= as.numeric(quantile(frm$value, probs=0.8)))

Basic curve: each item “costs” the same. The wizard sorts by true value, the x axis sorts by the model, and plots the fraction of the total population.

WVPlots::GainCurvePlot(frm, "model", "value", title="Example Continuous Gain Curve")

We can annotate a point of the model at a specific x value

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", 

When the x values have different costs, take that into account in the gain curve. The wizard now sorts by value/cost, and the x axis is sorted by the model, but plots the fraction of total cost, rather than total count.

WVPlots::GainCurvePlotC(frm, "model", "costs", "value", title="Example Continuous Gain CurveC")

ROC Plots

WVPlots::ROCPlot(frm, "model", "isValuable", TRUE, title="Example ROC plot")

x1 = rnorm(50)
x2 = rnorm(length(x1))
y = 0.2*x2^2 + 0.5*x2 + x1 + rnorm(length(x1))
frmP = data.frame(x1=x1,x2=x2,yC=y>=as.numeric(quantile(y,probs=0.8)))
# WVPlots::ROCPlot(frmP, "x1", "yC", TRUE, title="Example ROC plot")
# WVPlots::ROCPlot(frmP, "x2", "yC", TRUE, title="Example ROC plot")
WVPlots::ROCPlotPair(frmP, "x1", "x2", "yC", TRUE, title="Example ROC pair plot")

Precision-Recall Plot

WVPlots::PRPlot(frm, "model", "isValuable", TRUE, title="Example Precision-Recall plot")

Double Density Plot

WVPlots::DoubleDensityPlot(frm, "model", "isValuable", title="Example double density plot")

Double Histogram Plot

WVPlots::DoubleHistogramPlot(frm, "model", "isValuable", title="Example double histogram plot")

Cleveland Style Dotplots


# discrete variable: letters of the alphabet
# frequencies of letters in English
# source:
letterFreqs = c(8.167, 1.492, 2.782, 4.253, 12.702, 2.228,
                2.015, 6.094, 6.966, 0.153, 0.772, 4.025, 2.406, 6.749, 7.507, 1.929,
                0.095, 5.987, 6.327, 9.056, 2.758, 0.978, 2.360, 0.150, 1.974, 0.074)
letterFreqs = letterFreqs/100
letterFrame = data.frame(letter = letters, freq=letterFreqs)

# now let's generate letters according to their letter frequencies
N = 1000
randomDraws = data.frame(draw=1:N, letter=sample(letterFrame$letter, size=N, replace=TRUE, prob=letterFrame$freq))

WVPlots::ClevelandDotPlot(randomDraws, "letter", title = "Example Cleveland-style dot plot")

WVPlots::ClevelandDotPlot(randomDraws, "letter", limit_n = 10,  title = "Top 10 most frequent letters")

WVPlots::ClevelandDotPlot(randomDraws, "letter", sort=0, title="Example Cleveland-style dot plot, unsorted")

WVPlots::ClevelandDotPlot(randomDraws, "letter", sort=1, stem=FALSE, title="Example with increasing sort order + coord_flip, no stem") + ggplot2::coord_flip()

ScatterBox Plots

classes = c("a", "b", "c")
means = c(2, 4, 3)
names(means) = classes
label = sample(classes, size=1000, replace=TRUE)
meas = means[label] + rnorm(1000)
frm2 = data.frame(label=label,
                  meas = meas)

WVPlots::ScatterBoxPlot(frm2, "label", "meas", pt_alpha=0.2, title="Example Scatter/Box plot")

WVPlots::ScatterBoxPlotH(frm2, "meas", "label",  pt_alpha=0.2, title="Example Scatter/Box plot")

Discrete Distribution Plot

frmx = data.frame(x = rbinom(1000, 20, 0.5))
WVPlots::DiscreteDistribution(frmx, "x","Discrete example")

Distribution and Count Plot

d <- data.frame(wt=100*rnorm(100))


Smoothed Scatterplots

y = c(1,2,3,4,5,10,15,18,20,25)
x = seq_len(length(y))
df = data.frame(x=x,y=y)

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", NULL, title="centered smooth, one group")

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", NULL, title="left smooth, one group", align="left")

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", NULL, title="right smooth, one group", align="right")

n = length(x)
df = rbind(data.frame(x=x, y=y+rnorm(n), gp="times 1"),
           data.frame(x=x, y=0.5*y + rnorm(n), gp="times 1/2"),
           data.frame(x=x, y=2*y + rnorm(n), gp="times 2"))

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", "gp", title="centered smooth, multigroup")

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", "gp", title="left smooth, multigroup", align="left")

WVPlots::ConditionalSmoothedScatterPlot(df, "x", "y", "gp", title="right smooth, multigroup", align="right")

Density Plot with Shaded Tail

d = data.frame(meas=rnorm(100))
threshold = -1.5
WVPlots::ShadedDensity(d, "meas", threshold, 
                       title="Example shaded density plot, left tail")

WVPlots::ShadedDensity(d, "meas", -threshold, tail="right", 
                       title="Example shaded density plot, right tail")