Generic function for plotting of R objects. For more details about
the graphical parameter arguments, see `par`

.

For simple scatter plots, `plot.default`

will be used.
However, there are `plot`

methods for many R objects,
including `function`

s, `data.frame`

s,
`density`

objects, etc. Use `methods(plot)`

and
the documentation for these.

`plot(x, y, …)`

x

the coordinates of points in the plot. Alternatively, a
single plotting structure, function or *any R object with a
plot method* can be provided.

y

the y coordinates of points in the plot, *optional*
if `x`

is an appropriate structure.

…

Arguments to be passed to methods, such as
graphical parameters (see `par`

).
Many methods will accept the following arguments:

`type`

what type of plot should be drawn. Possible types are

`"p"`

for**p**oints,`"l"`

for**l**ines,`"b"`

for**b**oth,`"c"`

for the lines part alone of`"b"`

,`"o"`

for both ‘**o**verplotted’,`"h"`

for ‘**h**istogram’ like (or ‘high-density’) vertical lines,`"s"`

for stair**s**teps,`"S"`

for other**s**teps, see ‘Details’ below,`"n"`

for no plotting.

`type`

s give a warning or an error; using, e.g.,`type = "punkte"`

being equivalent to`type = "p"`

for S compatibility. Note that some methods, e.g.`plot.factor`

, do not accept this.`main`

an overall title for the plot: see

`title`

.`sub`

a sub title for the plot: see

`title`

.`xlab`

a title for the x axis: see

`title`

.`ylab`

a title for the y axis: see

`title`

.`asp`

the \(y/x\) aspect ratio, see

`plot.window`

.

The two step types differ in their x-y preference: Going from
\((x1,y1)\) to \((x2,y2)\) with \(x1 < x2\), `type = "s"`

moves first horizontal, then vertical, whereas `type = "S"`

moves
the other way around.

`plot.default`

, `plot.formula`

and other
methods; `points`

, `lines`

, `par`

.
For thousands of points, consider using `smoothScatter()`

instead of `plot()`

.

# NOT RUN { require(stats) # for lowess, rpois, rnorm plot(cars) lines(lowess(cars)) plot(sin, -pi, 2*pi) # see ?plot.function ## Discrete Distribution Plot: plot(table(rpois(100, 5)), type = "h", col = "red", lwd = 10, main = "rpois(100, lambda = 5)") ## Simple quantiles/ECDF, see ecdf() {library(stats)} for a better one: plot(x <- sort(rnorm(47)), type = "s", main = "plot(x, type = \"s\")") points(x, cex = .5, col = "dark red") # }