One-step data graphics

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
)

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.

See also

add_plot_labels to add labels to the plot (without needing the ggplot2 + pipe)

Examples

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")