R Plot

plot is a built-in R function for plotting.

Example

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

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

Linear model estimates have several plots associated.

1. a Tukey-Anscombe (a.k.a. residuals vs. fitted) plot

2. a residuals 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 pairs function calculates pairwise comparisons. The confidence intervals can be plotted like:

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

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.

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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