bin_x: Group continuous data values (x-axis)

In vegalite: Tools to Encode Visualizations with the 'Grammar of Graphics'-Like 'Vega-Lite' 'Spec'

Description

The "bin" property is for grouping quantitative, continuous data values of a particular field into smaller number of “bins” (e.g., for a histogram).

Usage

bin_x(vl, min = NULL, max = NULL, base = NULL, step = NULL,
  steps = NULL, minstep = NULL, div = NULL, maxbins = NULL)

Arguments

vl	Vega-Lite object
min	the minimum bin value to consider.
max	the maximum bin value to consider.
base	the number base to use for automatic bin determination.
step	an exact step size to use between bins.
steps	an array of allowable step sizes to choose from.
minstep	minimum allowable step size (particularly useful for integer values).
div	Scale factors indicating allowable subdivisions. The default value is [5, 2], which indicates that for base 10 numbers (the default base), the method may consider dividing bin sizes by 5 and/or 2. For example, for an initial step size of 10, the method can check if bin sizes of 2 (= 10/5), 5 (= 10/2), or 1 (= 10/(5*2)) might also satisfy the given constraints.
maxbins	the maximum number of allowable bins.

References

Vega-Lite Binning

Examples

vegalite() %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("IMDB_Rating", "quantitative") %>%
  encode_y("Rotten_Tomatoes_Rating", "quantitative") %>%
  encode_size("*", "quantitative", aggregate="count") %>%
  bin_x(maxbins=10) %>%
  bin_y(maxbins=10) %>%
  mark_point()

Example output

vegalite documentation built on May 2, 2019, 10:46 a.m.