scales: Add a scale to a ggvis plot

Description

This creates a scale object for a given scale and variable type, and adds it to a ggvis plot. The scale object is populated with default settings, which depend on the scale (e.g. fill, x, opacity) and the type of variable (e.g. numeric, nominal, ordinal). Any settings that are passed in as arguments will override the defaults.

Arguments

vis

A ggvis object.

scale

The name of a scale, such as "x", "y", "fill", "stroke", etc.

type

A variable type. One of "numeric", "nominal", "ordinal", "logical", "datetime".

...

other arguments passed to the scale function. See the help for scale_numeric, scale_ordinal and scale_datetime for more details. For example, you might supply trans = "log" to create a log scale.

name

If NULL, the default, the scale name is the same as scale. Set this to a custom name to create multiple scales for stroke or fill, or (god forbid) a secondary y scale.

Scale selection

ggvis supports the following types of scales. Typical uses for each scale type are listed below:

numeric For continuous numeric values.
nominal For character vectors and factors.
ordinal For ordered factors (these presently behave the same as nominal).
logical For logical (TRUE/FALSE) values.
datetime For dates and date-times.

Each type has a corresponding function: scale_numeric, scale_nominal, and so on.

The scale types for ggvis are mapped to scale types for Vega, which include "ordinal", "quantitative", and "time". See ggvis_scale for more details.

Given a scale and type, the range is selected based on the combination of the scale and type. For example, you get a different range of colours depending on whether the data is numeric, ordinal, or nominal. Some scales also set other properties. For example, nominal/ordinal position scales also add some padding so that points are spaced away from plot edges.

Not all combinations have an existing default scale. If you use a combination that does not have an existing combination, it may suggest you're displaying the data in a suboptimal way. For example, there is no default for a numeric shape scale, because there's no obvious way to map continuous values to discrete shapes.

Examples

Run this code

# NOT RUN {
p <- mtcars %>%
  ggvis(x = ~wt, y = ~mpg, fill = ~factor(cyl), stroke = ~hp) %>%
  layer_points()

p %>% scale_numeric("x")
p %>% scale_numeric("stroke")
p %>% scale_nominal("fill")

# You can also supply additional arguments or override the defaults
p %>% scale_numeric("x", trans = "log")
p %>% scale_numeric("stroke", range = c("red", "blue"))
# }

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