Some example data science plots in R using ggplot2. See https://github.com/WinVector/WVPlots for code/details.

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

Scatterplots

WVPlots::ScatterHist(frm, "x", "y", title="Example Fit")
## `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).

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).

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

set.seed(34903490)
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")

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

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

Gain Curves

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)))

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", 
                          gainx=gainx,labelfun=labelfun) 

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", title="Example ROC 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

set.seed(34903490)

# discrete variable: letters of the alphabet
# frequencies of letters in English
# source: http://en.algoritmy.net/article/40379/Letter-frequency-English
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()