plot

plot(basedata1$iq, basedata$read_ab, main="Diagrama de Dispersión", xlab = "read_ab", ylab = "iq")

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 ```