translate_qplot_lattice {ggplot2} | R Documentation

## Translating between qplot and lattice

### Description

The major difference between lattice and ggplot2 is that lattice uses a formula based interface. ggplot2 does not because the formula does not generalise well to more complicated situations.

### Examples

    library(lattice)

    if (require("ggplot2movies")) {
    xyplot(rating ~ year, data=movies)
    qplot(year, rating, data=movies)

    xyplot(rating ~ year | Comedy + Action, data = movies)
    qplot(year, rating, data = movies, facets = ~ Comedy + Action)
    # Or maybe
    qplot(year, rating, data = movies, facets = Comedy ~ Action)

    # While lattice has many different functions to produce different types of
    # graphics (which are all basically equivalent to setting the panel argument),
    # ggplot2 has qplot().

    stripplot(~ rating, data = movies, jitter.data = TRUE)
    qplot(rating, 1, data = movies, geom = "jitter")

    histogram(~ rating, data = movies)
    qplot(rating, data = movies, geom = "histogram")

    bwplot(Comedy ~ rating ,data = movies)
    qplot(factor(Comedy), rating, data = movies, geom = "boxplot")

    xyplot(wt ~ mpg, mtcars, type = c("p","smooth"))
    qplot(mpg, wt, data = mtcars, geom = c("point","smooth"))
    }

    # The capabilities for scale manipulations are similar in both ggplot2 and
    # lattice, although the syntax is a little different.

    xyplot(wt ~ mpg | cyl, mtcars, scales = list(y = list(relation = "free")))
    qplot(mpg, wt, data = mtcars) + facet_wrap(~ cyl, scales = "free")

    xyplot(wt ~ mpg | cyl, mtcars, scales = list(log = 10))
    qplot(mpg, wt, data = mtcars, log = "xy")

    xyplot(wt ~ mpg | cyl, mtcars, scales = list(log = 2))
    qplot(mpg, wt, data = mtcars) +
      scale_x_continuous(trans = scales::log2_trans()) +
      scale_y_continuous(trans = scales::log2_trans())

    xyplot(wt ~ mpg, mtcars, group = cyl, auto.key = TRUE)
    # Map directly to an aesthetic like colour, size, or shape.
    qplot(mpg, wt, data = mtcars, colour = cyl)

    xyplot(wt ~ mpg, mtcars, xlim = c(20,30))
    # Works like lattice, except you can't specify a different limit
    # for each panel/facet
    qplot(mpg, wt, data = mtcars, xlim = c(20,30))

    # Both lattice and ggplot2 have similar options for controlling labels on the plot.

    xyplot(wt ~ mpg, mtcars, xlab = "Miles per gallon", ylab = "Weight",
      main = "Weight-efficiency tradeoff")
    qplot(mpg, wt, data = mtcars, xlab = "Miles per gallon", ylab = "Weight",
      main = "Weight-efficiency tradeoff")

    xyplot(wt ~ mpg, mtcars, aspect = 1)
    qplot(mpg, wt, data = mtcars, asp = 1)

    # par.settings() is equivalent to + theme() and trellis.options.set()
    # and trellis.par.get() to theme_set() and theme_get().
    # More complicated lattice formulas are equivalent to rearranging the data
    # before using ggplot2.

[Package ggplot2 version 2.1.0 Index]