point_plot: One-step data graphics

Description

point_plot() makes it easy to construct an informative basic graph of a data frame. "Making it easy" means that the user only needs to specify two things: 1) the data frame to be used and 2) a tilde expression with the response variable on the left and up to three explanatory variables on the right. The response variable is mapped to the vertical axis while the first explanatory variable defines the horizontal axis. The second explanatory variable (if any) maps to color, the third (if any) defines facets. Quantitative variables used for color or faceting are cut into categorical variables, so color and facets will always be discrete.

Usage

point_plot(
  D,
  tilde,
  ...,
  seed = 101,
  annot = c("none", "violin", "model", "bw"),
  jitter = c("default", "none", "all", "x", "y"),
  interval = c("confidence", "none", "prediction"),
  point_ink = 0.5,
  model_ink = 0.4,
  palette = LETTERS[1:8],
  bw = NULL,
  level = 0.95,
  nx = 50,
  model_family = NULL
)

Value

A ggplot graphics object

Arguments

D: a data frame
tilde: tilde expression specifying y ~ x or y ~ x + color
seed: (optional) random seed for jittering
annot: Statistical annotation (one of "none", "violin", "model", "bw")
jitter: Options for turning on jitter: one of "default", "both", "none", "x", "y". By default, By default, categorical variables are jittered.
interval: the type of interval: default "confidence". Others: "none" or "prediction"
point_ink: Opacity of ink for the data points
model_ink: Opacity of ink for the model annotation
palette: Depending on taste and visual capabilities, some people might prefer to alter the color scheme. There are 8 palettes available: "A" through "H".
bw: bandwidth for violin plot
level: confidence level to use (0.95)
nx: Number of places to evaluate any x-axis quantitative vars. Default 50. Use higher if graph isn't smooth enough.
model_family: Override the default model type. See model_train()
...: Graphical options for the data points, labels, e.g. size

Details

When an x- or y- variables is categorical, jittering is automatically applied.

Using annot = "model" will annotate the data with the graph of a model --- shown as confidence intervals/bands --- corresponding to the tilde expression. annot = "violin" will annotate with a violin plot.

If you want to use the same explanatory variable for color and faceting (this might have pedagogical purposes) merely repeat the name of the color variable in the faceting position, e.g. mpg ~ hp + cyl + cyl.

Examples

Run this code

mosaicData::Galton |> point_plot(height ~ mother + sex + father, annot="model", model_ink=1)
mtcars |> point_plot(mpg ~ wt + cyl)
mtcars |> point_plot(mpg ~ wt + cyl + hp, annot="model")