ggformula (version 0.9.0)

gf_step: Formula interface to geom_step()

Description

geom_path() connects the observations in the order in which they appear in the data. geom_line() connects them in order of the variable on the x axis. geom_step() creates a stairstep plot, highlighting exactly when changes occur. The group aesthetic determines which cases are connected together.

Usage

gf_step(object = NULL, gformula = NULL, data = NULL, alpha, color,
  group, linetype, size, direction = "hv", xlab, ylab, title, subtitle,
  caption, geom = "step", stat = "identity", position = "identity",
  show.legend = NA, show.help = NULL, inherit = TRUE,
  environment = parent.frame(), ...)

Arguments

object

When chaining, this holds an object produced in the earlier portions of the chain. Most users can safely ignore this argument. See details and examples.

gformula

A formula with shape y ~ x. Faceting can be achieved by including | in the formula.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

alpha

Opacity (0 = invisible, 1 = opaque).

color

A color or a formula used for mapping color.

group

Used for grouping.

linetype

A linetype (numeric or "dashed", "dotted", etc.) or a formula used for mapping linetype.

size

A numeric size or a formula used for mapping size.

direction

direction of stairs: 'vh' for vertical then horizontal, or 'hv' for horizontal then vertical.

xlab

Label for x-axis. See also gf_labs().

ylab

Label for y-axis. See also gf_labs().

title

Title, sub-title, and caption for the plot. See also gf_labs().

subtitle

Title, sub-title, and caption for the plot. See also gf_labs().

caption

Title, sub-title, and caption for the plot. See also gf_labs().

geom

A character string naming the geom used to make the layer.

stat

The statistical transformation to use on the data for this layer, as a string.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

show.help

If TRUE, display some minimal help.

inherit

A logical indicating whether default attributes are inherited.

environment

An environment in which to look for variables not found in data.

...

Additional arguments. Typically these are (a) ggplot2 aesthetics to be set with attribute = value, (b) ggplot2 aesthetics to be mapped with attribute = ~ expression, or (c) attributes of the layer as a whole, which are set with attribute = value.

Value

a gg object

Specifying plot attributes

Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula. Setting and mapping of additional attributes can be done through the use of additional arguments. Attributes can be set can be set using arguments of the form attribute = value or mapped using arguments of the form attribute = ~ expression.

In formulas of the form A | B, B will be used to form facets using facet_wrap() or facet_grid(). This provides an alternative to gf_facet_wrap() and gf_facet_grid() that is terser and may feel more familiar to users of lattice.

Evaluation

Evaluation of the ggplot2 code occurs in the environment of gformula. This will typically do the right thing when formulas are created on the fly, but might not be the right thing if formulas created in one environment are used to create plots in another.

See Also

ggplot2::geom_step()

Examples

# NOT RUN {
if (require(mosaicData)) {
  gf_step( births ~ date, data = Births78, color = ~ wday)
}

# Roll your own Kaplan-Meier plot

if (require(survival) && require(broom)) {
  # fit a survival model
  surv_fit <- survfit(coxph(Surv(time, status) ~ age + sex, lung))
  surv_fit
  # use broom::tidy() to create a tidy data frame for plotting
  surv_df <- tidy(surv_fit)
  head(surv_df)
  # now create a plot
  surv_df %>%
    gf_step(estimate ~ time) %>%
    gf_ribbon(conf.low + conf.high ~ time, alpha = 0.2)
}
# }