ggplot: Create a new ggplot

In hadley/ggplot1: Before there was ggplot2

Create a new ggplot

Description

This function creates the basic ggplot object which you can then furnish with graphical objects. Here you will set up the default data frame, default aesthetics and the formula that will determine how the panels are broken apart. See cast for more details on specifying the facetting formula and margin arguments. Note that ggplot creates a plot object without a "plot": you need to grobs (points, lines, bars, etc.) to create something that you can see.

Usage

ggplot(data, ...)

## Default S3 method:
ggplot(data = NULL, aesthetics = list(), formula = . ~ ., margins = FALSE, ...)

Arguments

data	default data frame
...	Not used
aesthetics	default list of aesthetic mappings (these can be colour, size, shape, line type – see individual grob functions for more details)
formula	formula describing row and column layout, see cast for more details
margins	a vector of names giving which margins to display, can include grand_row and grand_col or us TRUE to display all margins

Details

Steps to create a plot:

1. Create a new plot. (p <- ggplot(mtcars, aesthetics=list(y=hp, x=mpg)))

2. Set scales (if necessary)

3. Add grobs to the plot (ggpoint(p))

Simple grobs:

• ggabline: line with given slope and intercept

• ggarea: area (polygons with base on y=0)

• ggbar: bars (stocked and dodgted)

• ggjitter: jittered points (useful for discrete data)

• ggline: lines (paths sorted by x-axis values)

• ggpath: paths

• ggpoint: points

• ggribbon: ribbon

• ggtext: text

• ggtile: tiles, like a levelplot

Complex grobs:

• ggboxplot: box plot

• ggcontour: contour lines

• ggdensity: 1d density plot (continuous analogue of histogram)

• gg2density: 2d density countours

• gghistogram: histogram

• ggquantile: quantile lines from a quantile regression

• ggsmooth: smooths from any model family

Look at the documentation of these objects to see many examples of ggplot in action.

You will also want to add scales to the basic plot to give finer control over how the data values are mapped to aethetics attributes of the grobs. For scales that control position of the points see:

• pscontinuous: continuous scales (with optional transformation)

• pscategorical: categorical scales

For other scales, see:

• sccolour: colour categorical variables using Brewer colour scales (see also scfill)

• scgradient: colour continuous scales with a gradient (see also scfillgradient)

• schcl: map continuous variable to hue, chroma or luminance components (see also scfillhcl)

• scmanual: no automatic conversion, uses raw values directly

• sclinetype: line type (solid, dashed, dotted, etc.)

• scrgb: map continuous variable to red, green or blue components (see also scfillrgb)

• scshape: point shape (glyph)

• scsize: point or line size

ggplot is different from base and lattice graphics in how you build up the plot. With ggplot you build up the plot object (rather than the plot on the screen as in base graphics, or all at once as in lattice graphics.)

Each of the grob and scale functions adds the grob to the plot and returns the modified plot object. This lets you quickly experiment with different versions of the plot, using different grobs or scales. You can see how this works in the examples

You can also use summary to give a quick description of a plot.

If you want to change the background colour, how the panel strips are displayed, or any other default graphical option, see ggopt.

See Also

http://had.co.nz/ggplot, stamp, cast, ggopt

Examples

data("tips", package = "reshape")

p <- ggplot(tips)
summary(p)
ggpoint(p, aesthetic = list(y = tip, x = total_bill))
p <- ggplot(tips, aesthetic = list(y = tip, x = total_bill))
p$title <- "Tips"
summary(p)
ggpoint(p)
ggpoint(p, colour = "darkgreen", size = 3)
ggpoint(p, list(colour = sex))
ggpoint(ggplot(tips, formula = . ~ sex, aesthetics = list(y = tip, x = total_bill)))

# Create complex plots from simple components
p <- ggplot(tips, formula = smoker ~ sex, aesthetics = list(y = tip, x = total_bill))
p |> ggpoint()

p |>
  ggpoint() |>
  ggsmooth()

p |>
  ggpoint() |>
  ggsmooth(method = lm, formula = y~x)

p |>
  ggpoint() |>
 ggabline(slope = c(0.1,0.15,0.2))

(p2 <- ggabline(ggpoint(p, aes = list(colour = tip/total_bill)), slope = c(0.1, 0.15, 0.2)))
summary(p2)
scgradient(p2)
scgradient(p2, midpoint = 0.15, high = "green", mid = "yellow")

p <- ggplot(tips, formula = sex ~ smoker, aesthetics = list(x = tip / total_bill), margins = TRUE)
gghistogram(p)
gghistogram(p, scale = "density", breaks = seq(0, 1, length = 20))
ggdensity(gghistogram(p))

p <- ggplot(tips, formula = . ~ smoker, aesthetics = list(x = sex, y = tip))
ggboxplot(p)
ggjitter(ggboxplot(p))

hadley/ggplot1 documentation built on Dec. 1, 2024, 11:23 a.m.