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
```

Scatterplot with smoothing line through points.

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

```
WVPlots::ScatterHist(frm, "x", "y", smoothmethod="lm",
title="Example Linear Fit", estimate_sig = TRUE)
```

Scatterplot compared to the line `x = y`

. Also report the coefficient of determination between `x`

and `y`

(where `y`

is “true outcome” and `x`

is “predicted outcome”).

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

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

```
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")
```

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

```
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")
```

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' and formula 'y ~ x'`

```
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.

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.

```
set.seed(34903490)
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")
```

Plots precision and recall as functions of different classifier thresholds.

`PRTPlot()`

can also plot sensitivity, specificity, and false positive rate as a function of threshold. One application for this is to “unroll” an ROC Plot to explicitly match thresholds to given achievable combinations of false positive rate (AKA (1 - specificity)) and sensitivity (AKA recall or true positive rate). Compare the below graph with the ROC plot for `frm`

, above.