Intro to R - 4. Base R Plots OIT/SMU Libraries Data Science Workshop - - PowerPoint PPT Presentation

Intro to R - 4. Base R Plots

OIT/SMU Libraries Data Science Workshop Series Michael Hahsler

OIT, SMU

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1

Simple Plots

2

High-level Graphics Functions

3

Low-level Graphics Functions

4

Exercises

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Section 1 Simple Plots

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Introduction

Ploting is an integral part of R. R plots on devices (e.g., X11(), quarz(), windows(), pdf(), png()) Plotting commands are divided into three basic groups:

1

High-level plotting functions create a new plot on the graphics device, possibly with axes, labels, titles and so on.

2

Low-level plotting functions add more information to an existing plot, such as extra points, lines and labels.

3

Interactive graphics functions allow you interactively add information to, or extract information from, an existing plot, using a pointing device such as a mouse. We will only discuss “base” graphics here. A very popular alternative is ggplot.

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Section 2 High-level Graphics Functions

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plot

x <- 1:100 y <- x^2 plot(x, y)

20 40 60 80 100 2000 4000 6000 8000 10000 x y

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plot

# use type l = line, line width 3 and red plot(x, y, type = "l", lwd = 3, col = "red") # add a vertical dashed (line type 2) line abline(v=50, lty=2) # add a vertical line

20 40 60 80 100 2000 4000 6000 8000 10000 x y

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Getting help for plot

? plot Shows that plot is a so called generic function. Generic functions have implementations dor different data types which get “dispatched” at call-time. ? plot.default This is the default function for plot. ? par Graphical parameters which typically can be passed on as ... to plot.

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Scatterplot

mlb <- read.csv(paste0("https://michael.hahsler.net/SMU/", "DS_Workshop_Intro_R/examples/MLB_cleaned.csv")) plot(x = mlb$Height.inches., y = mlb$Weight.pounds., xlab = "Height", ylab = "Weight", main = "Weight by Height")

70 75 80 160 180 200 220 240 260 280

Weight by Height

Height Weight

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Scatterplot matrix (pairs plot)

plot() is generic which means that it produces different results depending on the type of data. For example, data frames result in a pairwise scatterplot matrix. data(iris) head(iris, n=1) # iris is a data.frame ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa plot(iris[,-5], col= iris[,5]) # use Species (col 5) for color

Sepal.Length

2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 0.5 1.0 1.5 2.0 2.5 2.0 2.5 3.0 3.5 4.0

Sepal.Width Petal.Length

1 2 3 4 5 6 7 0.5 1.0 1.5 2.0 2.5 4.5 5.5 6.5 7.5 1 2 3 4 5 6 7

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barplot - Bar Charts

gpa <- c(Sandra = 3.8, Michael = 4.0, Peter = NA) gpa.sort <- sort(gpa, na.last = TRUE) barplot(gpa.sort, ylab = "GPA", xlab = "Student")

Sandra Michael Peter Student GPA 1 2 3 4

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barplot - Bar Charts

How many players do the Texas Rangers have for each position?

mlb_tex <- mlb[mlb$Team == "TEX", ] # use par to make space for labels see ? par and look for mar (plot margin)

• ldpar <- par(mar = c(10, 4, 4, 1) + .1)

barplot(sort(table(mlb_tex$Position), decreasing = TRUE), las = 2, ylab = "Count", main = "Texas Rangers: Players per position")

Relief Pitcher Starting Pitcher Outfielder Catcher Shortstop Designated Hitter First Baseman Second Baseman Third Baseman

Texas Rangers: Players per position

Count 2 4 6 8 10

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hist - Histogram

hist(mlb$Age, breaks=20)

Histogram of mlb$Age

mlb$Age Frequency 20 25 30 35 40 45 50 20 40 60 80 100

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hist - Histogram with estimated density

hist(mlb$Age, breaks=20, prob=TRUE) lines(density(mlb$Age), col="red")

Histogram of mlb$Age

mlb$Age Density 20 25 30 35 40 45 50 0.00 0.02 0.04 0.06 0.08 0.10

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image

volcano is a R data set with elevation measurements of Maunga Whau on a 10m by 10m grid. data(volcano) dim(volcano) ## [1] 87 61 image(volcano)

0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0

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Commmon arguments for plot functions

add=TRUE: add to an existing plot? axes=FALSE: plot axes? log="x", log="y" or log="xy": Use a logarithmic scale? type="l": plot lines instead of points xlab, ylab: axis labels main: figure title sub: sub-title

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Section 3 Low-level Graphics Functions

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Some low-level functions

These functions can be used to add elements to a plot. points(x, y) lines(x, y) text(x, y, labels, ...) abline(a, b) or abline(h=y) or abline(v=x) polygon(x, y, ...) legend(x, y, legend, ...) title(main, sub) axis(side, ...)

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Graphical parameter list: par

R maintains a list of graphics parameters to control line style, colors, figure arrangement and text justification. A separate list of graphics parameters is maintained for each active device.

• ldpar <- par(col = "blue", pch=4, cex = 2)

plot(x, y, axes = FALSE) axis(1); axis(2)

x y 20 40 60 80 100 4000 8000

par(oldpar) # restore the original parameters Many parameters from par() can also be passed to plot(). Try par() and ?par

Danger

Do not forget to reset par to the original settings! You can also reset par by closing the plotting device or with the little broom symbol in the Plots tab of RStudio.

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Important parameters in par

pch=4: Plotting symbol (0-25) lty=2: Line type lwd=2: Line width col=2: Color for points, lines, etc. cex=1.5: Character expansion (e.g., 50% larger than default text size) mai=c(1, 0.5, 0.5, 0): Widths of the bottom, left, top and right margins, respectively, measured in inches. mar is the same, just measured in rows. mfcol, mfrow: Put multiple plots next to each other.

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Saving a plot as an image

png(file="plot.png") # open device plot(x, y) dev.off() # close device ## pdf ## 2 Other devices are ‘jpeg(), tiff(),‘ ‘pdf(), postscript(),‘ ‘win.metafile()‘ (Windows). Use ‘?Devices‘ for a complete list.

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Advanced and InteractiveGraphics

The most populare advanced graphics packages are: ggplot2: Grammar of graphics. Produces elegant visualizations (see http://ggplot2.org/). grid: Advanced graphics can be programmed using flexible low level ploting functions (viewports, different coordinate systems and units, lines, points, text, etc.) See also package lattice. Interactive Graphics are available via several extension packages. Here are some examples: https://www.r-graph-gallery.com/interactive-charts.html

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Section 4 Exercises

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Exercises

1

Plot a ‘sin(x)/x‘. Hint: Trigonometric functions in R use angles in radians (see ‘sin‘)

2

The “cars” data set gives the speed of cars and the distances taken to stop. Note that the data were recorded in the 1920s. Plot the “cars” data set as a scatter plot. Plot all data points with distances taken to stop greater than 80 in red.

3

Plot histograms for speed and dist in “cars”.

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