= R Plot =

'''`plot`''' is a built-in R function for [[R/BuiltinFunctions#Plotting_functions|plotting]].

<<TableOfContents>>

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== Example ==

{{{
x <- seq(0, 2*pi, length.out=100)
y <- sin(x)
plot(x,y)
}}}

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== Plots ==

The `plot` function takes some object and creates a plot.

Most generically, this function takes two numeric vectors and creates a scatterplot.



=== Configuration ===

There are many optional arguments to the `plot` function. These include:
 * `type="p"` to draw points. Alternatively...
   * `"l"` for lines connecting points
   * `"h"` for 'histogram like vertical lines'; this draws a line from the x-axis up to the plotted point
   * `"s"` for 'stair steps'; this draws lines connecting points, but as a horizontal line and a vertical line rather than a single direct line
 * `main="Main title"`
 * `sub="Subtitle"`
 * `xlab="x-axis"`
 * `ylab="x-axis"`
 * `pch=1` to draw points as empty circles. Alternatively...
   * `0` as empty squares
   * `5` as empty diamonds
   * `2` as empty upward triangles
   * `6` as empty downward triangles
   * `16` as filled circles
   * `15` as filled squares
   * `18` as filled diamond
   * `17` as filled upward triangles
   * `"*"` as asterisks
     * can be any single character
 * `col="black"` to draw points in black. Alternatively...
   * `"red"` in red, "`blue"` in blue, and so on, any color name that is recognized by R
     * run `colors()` to see the list of recognized color names
   * `"#000000"` in black, or any hex code
 * `lty=1` to draw a solid line. Alternatively...
   * `"solid"`
   * `2` or "dashed" for a dashed line
   * `3` or "dotted" for a dotted line
   * `4` or "dotdash" for a dash-dotted line



=== density ===

Kernel density estimates can be plotted using the `plot` function. Try:

{{{
plot(density(x, na.rm=TRUE), main="Distribution of x")
}}}



=== lm ===

[[R/BuiltinFunctions/Lm|Linear model]] estimates have several plots associated.
 1. a [[Statistics/TukeyAnscombePlot|Tukey-Anscombe]] (a.k.a. residuals vs. fitted) plot
 2. a [[Statistics/Residuals|residuals]] [[Statistics/QQPlot|Q-Q plot]]
 3. a scale-location plot
 4. a Cook's distance plot
 5. a residuals vs. leverage plot
 6. a Cook's distance vs leverage plot

These plots are selected using the `which` option. For example:

{{{
plot(lm(y ~ x), which=1)
}}}


=== emmeans ===

The [[R/Emmeans|pairs]] function calculates pairwise comparisons. The confidence intervals can be plotted like:

{{{
plot(pairs(emmeans(mod, spec = ~ treatment)))
}}}

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== Lines ==

The `abline` function bundles a regression line into a plot. Try:

{{{
plot(x, y)
abline(lm(y ~ x))
}}}

This function takes some of the same options as above; in particular `col` and `lty`.

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== Legends ==

The `legend` function bundles a legend into a plot. The first arguments (one or two) set the position. These can be coordinates (in the same units as the plot itself), or it can be a single descriptive word ("topleft", "topright", "bottomleft", or "bottomright"). 

For example, with two overlaid plots, try:

{{{
plot(x, y1, pch=1, col="red")
plot(x, y2, pch=0, col="blue")
legend("topright", pch=c(1,0), col=c("red", "blue"), legend=c("y1", "y2"), text.col=c("red", "blue"))
}}}


Beyond that, the `legend` function takes these options:
 * `pch=...` for how to draw the points of the legend. If a scalar is provided, it is used for all points.
 * `col=...` for the color to draw the points of the legend. If a scalar is provided, it is used for all points.
 * `legend=...` for the text that labels the points of the legend.
 * `text.col=...` for the color to write the text labels. If a scalar is provided, it is used for all points.



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CategoryRicottone