Scatterplot

Create a scatter plot between two quantitative variables.

scatter(
  data,
  x,
  y,
  outlier = 3,
  alpha = 1,
  digits = 3,
  title,
  margin = "none",
  stats = TRUE,
  point_color = "deepskyblue2",
  outlier_color = "violetred1",
  line_color = "grey30",
  margin_color = "deepskyblue2"
)

Arguments

data	data frame
x	quantitative predictor variable
y	quantitative response variable
outlier	number. Observations with studentized residuals larger than this value are flagged. If set to 0, observations are not flagged.
alpha	Transparency of data points. A numeric value between 0 (completely transparent) and 1 (completely opaque).
digits	Number of significant digits in displayed statistics.
title	Optional title.
margin	Marginal plots. If specified, parameter can be histogram, boxplot, violin, or density. Will add these features to the top and right margin of the graph.
stats	logical. If TRUE, the slope, correlation, and correlation squared (expressed as a percentage) for the regression line are printed on the subtitle line.
point_color	Color used for points.
outlier_color	Color used to identify outliers (see the outlier parameter.
line_color	Color for regression line.
margin_color	Fill color for margin boxplots, density plots, or histograms.

Value

a ggplot2 graph

Details

The scatter function generates a scatterplot between two quantitative variables, along with a line of best fit and a 95% confidence interval. By default, regression statistics (b, r, r2, p) are printed and outliers (observations with studentized residuals > 3) are flagged. Optionally, variable distributions (histograms, boxplots, violin plots, density plots) can be added to the plot margins.

Note

Variable names do not have to be quoted.

Examples

scatter(cars74, hp, mpg)
scatter(cars74, wt, hp)
p <- scatter(ggplot2::mpg, displ, hwy,
        margin="histogram",
        title="Engine Displacement vs. Highway Mileage")
plot(p)