Plots Béton Point P

Generic X-Y Plotting

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

This plot is a simple point A to point B and back to point A plot. The protagonist sets off on a journey, only to return to his or her starting point having gained wisdom and experience (and sometimes treasure too). Paulo Coelho's The Alchemist is a beloved contemporary illustration of this plot. How to Outline a Story Plot: Three-Act Structure. Question 1003804: Consider the point P(6, −2). (a) Plot the image of the point with respect to the y-axis. (b) Plot the image of the point with respect to the x-axis. (c) Plot the image of the point with respect to the origin. Answer by MathLover1(17568) (Show Source): You can put this solution on YOUR website!

For simple scatter plots, plot.default will be used. However, there are plot methods for many R objects, including functions, data.frames, density objects, etc. Use methods(plot) and the documentation for these.

Arguments

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:

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.

an overall title for the plot: see title.

a sub title for the plot: see title.

a title for the x axis: see title.

a title for the y axis: see title.

the (y/x) aspect ratio, see plot.window.

Details

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.

See Also

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.

Aliases

• plot

Examples

Community examples

```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 ```

• Label points in the scatter plot
• Scatter plots with multiple groups

This article describes how create a scatter plot using R software and ggplot2 package. The function geom_point() is used.

Related Book:

mtcars data sets are used in the examples below.

Simple scatter plots are created using the R code below. The color, the size and the shape of points can be changed using the function geom_point() as follow :

Note that, the size of the points can be controlled by the values of a continuous variable as in the example below.

Read more on point shapes : ggplot2 point shapes

The function geom_text() can be used :

Read more on text annotations : ggplot2 - add texts to a plot

Add regression lines

The functions below can be used to add regression lines to a scatter plot :

• geom_smooth() and stat_smooth()
• geom_abline()

geom_abline() has been already described at this link : ggplot2 add straight lines to a plot.

Only the function geom_smooth() is covered in this section.

A simplified format is :

• method : smoothing method to be used. Possible values are lm, glm, gam, loess, rlm.
  • method = “loess”: This is the default value for small number of observations. It computes a smooth local regression. You can read more about loess using the R code ?loess.
  • method =“lm”: It fits a linear model. Note that, it’s also possible to indicate the formula as formula = y ~ poly(x, 3) to specify a degree 3 polynomial.
• se : logical value. If TRUE, confidence interval is displayed around smooth.
• fullrange : logical value. If TRUE, the fit spans the full range of the plot
• level : level of confidence interval to use. Default value is 0.95

Change the appearance of points and lines

This section describes how to change :

• the color and the shape of points
• the line type and color of the regression line
• the fill color of the confidence interval

Note that a transparent color is used, by default, for the confidence band. This can be changed by using the argument alpha : geom_smooth(fill=“blue”, alpha=1)

Read more on point shapes : ggplot2 point shapes

Read more on line types : ggplot2 line types

This section describes how to change point colors and shapes automatically and manually.

Change the point color/shape/size automatically

In the R code below, point shapes, colors and sizes are controlled by the levels of the factor variable cyl :

Add regression lines

Regression lines can be added as follow :

Note that, you can also change the line type of the regression lines by using the aesthetic linetype = cyl.

The fill color of confidence bands can be changed as follow :

Change the point color/shape/size manually

The functions below are used :

• scale_shape_manual() for point shapes
• scale_color_manual() for point colors
• scale_size_manual() for point sizes

It is also possible to change manually point and line colors using the functions :

• scale_color_brewer() : to use color palettes from RColorBrewer package
• scale_color_grey() : to use grey color palettes

Read more on ggplot2 colors here : ggplot2 colors

The function geom_rug() can be used :

sides : a string that controls which sides of the plot the rugs appear on. Allowed value is a string containing any of “trbl”, for top, right, bottom, and left.

The functions geom_density_2d() or stat_density_2d() can be used :

Read more on ggplot2 colors here : ggplot2 colors

The function stat_ellipse() can be used as follow:

The number of observations is counted in each bins and displayed using any of the functions below :

• geom_bin2d() for adding a heatmap of 2d bin counts
• stat_bin_2d() for counting the number of observation in rectangular bins
• stat_summary_2d() to apply function for 2D rectangular bins

The simplified formats of these functions are :

• geom : geometrical object to display the data
• bins : Number of bins in both vertical and horizontal directions. The default value is 30
• fun : function for summary

The data sets diamonds from ggplot2 package is used :

Change the number of bins :

Or specify the width of bins :

Step 1/3. Create some data :

Step 2/3. Create the plots :

Create a blank placeholder plot :

Step 3/3. Put the plots together:

To put multiple plots on the same page, the package gridExtra can be used. Install the package as follow :

Arrange ggplot2 with adapted height and width for each row and column :

Read more on how to arrange multiple ggplots in one page : ggplot2 - Easy way to mix multiple graphs on the same page

Change colors manually :

Read more on ggplot2 colors here : ggplot2 colors

This analysis has been performed using R software (ver. 3.2.4) and ggplot2 (ver. 2.1.0)

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