You learned from the Plot chapter that the plot function is used to plot numbers against each other. A 'scatter plot' is a type of plot used to display the relationship between two numerical variables, and plots one dot for each observation. Ggplot2 is now over 10 years old and is used by hundreds of thousands of people to make millions of plots. That means, by-and-large, ggplot2 itself changes relatively little. When we do make changes, they will be generally to add new functions or arguments rather than changing the behaviour of existing functions, and if we do make changes to.
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.
the coordinates of points in the plot. Alternatively, a single plotting structure, function or any R object with a plot
method can be provided.
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 points,
'l'
for lines,
'b'
for both,
'c'
for the lines part alone of 'b'
,
'o'
for both ‘overplotted’,
'h'
for ‘histogram’ like (or ‘high-density’) vertical lines,
's'
for stair steps,
'S'
for other steps, 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()
.
For X-Y-Z plotting see contour
, persp
and image
.
library(graphics)
# NOT RUN {require(stats) # for lowess, rpois, rnormplot(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')# }
```r # Plot with multiple lines in different color: plot(sin,-pi, 4*pi, col = 'red') plot(cos,-pi, 4*pi, col = 'blue', add = TRUE) ```
```r ## Plot with multiple lines in different color: plot(sin,-pi, 4*pi, col = 'red') plot(cos,-pi, 4*pi, col = 'blue', add = TRUE) ```
plot(basedata1$iq, basedata$read_ab, main='Diagrama de Dispersión', xlab = 'read_ab', ylab = 'iq')
## Linear Regression ExamplePlot points and add linear regression model line:```rlinreg <- lm(dist ~ speed, cars)linreg_coeffs <- coef(linreg)lineq <- paste('distance = ', linreg_coeffs[2], ' * speed + ', linreg_coeffs[1])plot(cars, main = 'Car distance by speed', sub = lineq, xlab = 'speed', ylab = 'distance', pch = 19)abline(linreg, col = 'blue')```
Pass a numeric vector to the `x` and `y` arguments, and you get a scatter plot. The `main` argument provides a [`title()`](https://www.rdocumentation.org/packages/graphics/topics/title). ```{r} plot(1:100, (1:100) ^ 2, main = 'plot(1:100, (1:100) ^ 2)') ``` If you only pass a single argument, it is interpreted as the `y` argument, and the `x` argument is the sequence from 1 to the length of `y`. ```{r} plot((1:100) ^ 2, main = 'plot((1:100) ^ 2)') ``` `cex` ('character expansion') controls the size of points. `lwd` controls the line width. `pch` controls the shape of points - you get 25 symbols to choose from, as well as alphabetic characters. `col` controls the color of the points. When `pch` is `21:25`, the points also get a background color which is set using `bg`. [`points()`](https://www.rdocumentation.org/packages/graphics/topics/points) for more on how to change the appearance of points in a scatter plot. ```{r} plot( 1:25, cex = 3, lwd = 3, pch = 1:25, col = rainbow(25), bg = c(rep(NA, 20), terrain.colors(5)), main = 'plot(1:25, pch = 1:25, ...)' ) ``` If you specify `type = 'l'`, you get a line plot instead. See [`plot.default()`](https://www.rdocumentation.org/packages/graphics/topics/plot.default) for a demonstration of all the possible values for type. ```{r} plot( (1:100) ^ 2, type = 'l', main = 'plot((1:100) ^ 2, type = 'l')' ) ``` `lty` controls the line type. `col` and `lwd` work in the same way as with points. [`lines()`](https://www.rdocumentation.org/packages/graphics/topics/lines) for more on how to change the appearance of lines in a line plot. ```{r} plot( (1:100) ^ 2, type = 'l', lty = 'dashed', lwd = 3, col = 'chocolate', main = 'plot((1:100) ^ 2, type = 'l', lty = 'dashed', ...)' ) ``` It is best practise to keep your `x` and `y` variables together, rather than as separate variables. ```{r} with( cars, plot(speed, dist, main = 'with(cars, plot(speed, dist))') ) ``` The formula interface, similar to modeling functions like [`lm()`](https://www.rdocumentation.org/packages/stats/topics/lm), makes this convenient. See [`plot.formula()`](https://www.rdocumentation.org/packages/graphics/topics/plot.formula) for more information. ```{r} plot( dist ~ speed, data = cars, main = 'plot(dist ~ speed, data = cars)' ) ``` If you pass a two column data frame or matrix then the columns are treated as the x and y values. So in this case, you can simply do: ```{r} plot(cars, main = 'plot(cars)') ``` The [`lines()`](https://www.rdocumentation.org/packages/graphics/topics/lines), [`points()`](https://www.rdocumentation.org/packages/graphics/topics/points) and [`title()`](https://www.rdocumentation.org/packages/graphics/topics/title) functions add lines, points and titles respectively to an existing plot. ```{r} plot(cars) lines(lowess(cars)) title('plot(cars); lines(lowess(cars))') ``` If the `x` variable is categorical, `plot()` knows to draw a box plot instead of a scatter plot. See [`boxplot()`](https://www.rdocumentation.org/packages/graphics/topics/boxplot) for more information on drawing those. ```{r} with( sleep, plot(group, extra, main = 'with(sleep, plot(group, extra))') ) ``` Again, the formula interface can be useful here. ```{r} plot(extra ~ group, sleep, main = 'plot(extra ~ group, sleep)') ``` Axis limits can be set using `xlim` and `ylim`. ```{r} plot( (1:100) ^ 2, xlim = c(-100, 200), ylim = c(2500, 7500), main = 'plot((1:100) ^ 2, xlim = c(-100, 200), ylim = c(2500, 7500))' ) ``` You can set log-scale axes using the `log` argument. ```{r} plot( exp(1:10), 2 ^ (1:10), main = 'plot(exp(1:10), 2 ^ (1:10))' ) plot( exp(1:10), 2 ^ (1:10), log = 'x', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'x')' ) plot( exp(1:10), 2 ^ (1:10), log = 'y', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'y')' ) plot( exp(1:10), 2 ^ (1:10), log = 'xy', main = 'plot(exp(1:10), 2 ^ (1:10), log = 'xy')' ) ``` If you pass a table of counts for a vector, `plot()` draws a simple histogram-like plot. See [`hist()`](https://www.rdocumentation.org/packages/graphics/topics/hist) for a more comprehensive histogram function. ```{r} plot( table(rpois(100, 5)), main = 'plot(table(rpois(100, 5)))' ) ``` For multi-dimensional tables, you get a mosaic plot. See [`mosaicplot()`](https://www.rdocumentation.org/packages/graphics/topics/mosaicplot) for more information. ```{r} plot( table(X = rpois(100, 5), Y = rbinom(100, 10, 0.75)), main = 'plot(table(X = rpois(100, 5), Y = rbinom(100, 10, 0.75)))' ) ``` You can also pass functions to plot. See [`curve()`](https://www.rdocumentation.org/packages/graphics/topics/curve) for more examples. ```{r} plot( sin, from = -pi, to = 2 * pi, main = 'plot(sin, from = -pi, to = 2 * pi)' ) ``` Use the axis function to give fine control over how the axes are created. See [`axis()`](https://www.rdocumentation.org/packages/graphics/topics/axis) and [`Axis()`](https://www.rdocumentation.org/packages/graphics/topics/Axis) for more info. ```{r} plot( sin, from = -pi, to = 2 * pi, axes = FALSE, main = 'plot(sin, axes = FALSE, ...); axis(1, ...); axis(2)' ) axis( 1, # bottom axis pi * (-1:2), c(expression(-pi), 0, expression(pi), expression(2 * pi)) ) axis(2) # left axis ``` Further graphical parameters can be set using [`par()`](https://www.rdocumentation.org/packages/graphics/topics/par). See [`with_par()`](https://www.rdocumentation.org/packages/withr/topics/with_par) for the best way to use `par()`. ```{r} old_pars <- par(las = 1) # horizontal axis labels plot((1:100) ^ 2, main = 'par(las = 1); plot((1:100) ^ 2)') par(old_pars) # reset parameters ```
In this article, you will learn to use par() function to put multiple graphs in a single plot by passing graphical parameters mfrow and mfcol.
We can put multiple graphs in a single plot by setting some graphical parameters with the help of par()
function. R programming has a lot of graphical parameters which control the way our graphs are displayed.
The par()
function helps us in setting or inquiring about these parameters. For example, you can look at all the parameters and their value by calling the function without any argument.
You will see a long list of parameters and to know what each does you can check the help section ?par
. Here we will focus on those which help us in creating subplots.
Graphical parameter mfrow
can be used to specify the number of subplot we need.
It takes in a vector of form c(m, n)
which divides the given plot into m*n array of subplots. For example, if we need to plot two graphs side by side, we would have m=1
and n=2
. Following example illustrates this.
This same phenomenon can be achieved with the graphical parameter mfcol
.
The only difference between the two is that, mfrow
fills in the subplot region row wise while mfcol
fills it column wise.
Same plot with the change par(mfcol = c(2, 2))
would look as follows. Note that only the ordering of the subplot is different.
The graphical parameter fig
lets us control the location of a figure precisely in a plot.
We need to provide the coordinates in a normalized form as c(x1, x2, y1, y2)
. For example, the whole plot area would be c(0, 1, 0, 1)
with (x1, y1) = (0, 0)
being the lower-left corner and (x2, y2) = (1, 1)
being the upper-right corner.
Note: we have used parameters cex
to decrease the size of labels and mai
to define margins.
The numbers assigned to fig
were arrived at with a hit-and-trial method to achieve the best looking plot.