(R-intro.info)A sample session

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Appendix A A sample session
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The following session is intended to introduce to you some features of
the R environment by using them.  Many features of the system will be
unfamiliar and puzzling at first, but this puzzlement will soon
disappear.

'Start R appropriately for your platform (Note: Invoking R).'

     The R program begins, with a banner.

     (Within R code, the prompt on the left hand side will not be shown
     to avoid confusion.)

'help.start()'
     Start the HTML interface to on-line help (using a web browser
     available at your machine).  You should briefly explore the
     features of this facility with the mouse.

     Iconify the help window and move on to the next part.

'x <- rnorm(50)'
'y <- rnorm(x)'
     Generate two pseudo-random normal vectors of x- and y-coordinates.

'plot(x, y)'
     Plot the points in the plane.  A graphics window will appear
     automatically.

'ls()'
     See which R objects are now in the R workspace.

'rm(x, y)'
     Remove objects no longer needed.  (Clean up).

'x <- 1:20'
     Make x = (1, 2, ..., 20).

'w <- 1 + sqrt(x)/2'
     A 'weight' vector of standard deviations.

'dummy <- data.frame(x=x, y= x + rnorm(x)*w)'
'dummy'
     Make a _data frame_ of two columns, x and y, and look at it.

'fm <- lm(y ~ x, data=dummy)'
'summary(fm)'
     Fit a simple linear regression and look at the analysis.  With 'y'
     to the left of the tilde, we are modelling y dependent on x.

'fm1 <- lm(y ~ x, data=dummy, weight=1/w^2)'
'summary(fm1)'
     Since we know the standard deviations, we can do a weighted
     regression.

'attach(dummy)'
     Make the columns in the data frame visible as variables.

'lrf <- lowess(x, y)'
     Make a nonparametric local regression function.

'plot(x, y)'
     Standard point plot.

'lines(x, lrf$y)'
     Add in the local regression.

'abline(0, 1, lty=3)'
     The true regression line: (intercept 0, slope 1).

'abline(coef(fm))'
     Unweighted regression line.

'abline(coef(fm1), col = "red")'
     Weighted regression line.

'detach()'
     Remove data frame from the search path.

'plot(fitted(fm), resid(fm),'
'     xlab="Fitted values",'
'     ylab="Residuals",'
'     main="Residuals vs Fitted")'
     A standard regression diagnostic plot to check for
     heteroscedasticity.  Can you see it?

'qqnorm(resid(fm), main="Residuals Rankit Plot")'
     A normal scores plot to check for skewness, kurtosis and outliers.
     (Not very useful here.)

'rm(fm, fm1, lrf, x, dummy)'
     Clean up again.

   The next section will look at data from the classical experiment of
Michelson to measure the speed of light.  This dataset is available in
the 'morley' object, but we will read it to illustrate the 'read.table'
function.

'filepath <- system.file("data", "morley.tab" , package="datasets")'
'filepath'
     Get the path to the data file.

'file.show(filepath)'
     Optional.  Look at the file.

'mm <- read.table(filepath)'
'mm'
     Read in the Michelson data as a data frame, and look at it.  There
     are five experiments (column 'Expt') and each has 20 runs (column
     'Run') and 'sl' is the recorded speed of light, suitably coded.

'mm$Expt <- factor(mm$Expt)'
'mm$Run <- factor(mm$Run)'
     Change 'Expt' and 'Run' into factors.

'attach(mm)'
     Make the data frame visible at position 3 (the default).

'plot(Expt, Speed, main="Speed of Light Data", xlab="Experiment No.")'
     Compare the five experiments with simple boxplots.

'fm <- aov(Speed ~ Run + Expt, data=mm)'
'summary(fm)'
     Analyze as a randomized block, with 'runs' and 'experiments' as
     factors.

'fm0 <- update(fm, . ~ . - Run)'
'anova(fm0, fm)'
     Fit the sub-model omitting 'runs', and compare using a formal
     analysis of variance.

'detach()'
'rm(fm, fm0)'
     Clean up before moving on.

   We now look at some more graphical features: contour and image plots.

'x <- seq(-pi, pi, len=50)'
'y <- x'
     x is a vector of 50 equally spaced values in the interval [-pi\,
     pi].  y is the same.

'f <- outer(x, y, function(x, y) cos(y)/(1 + x^2))'
     f is a square matrix, with rows and columns indexed by x and y
     respectively, of values of the function cos(y)/(1 + x^2).

'oldpar <- par(no.readonly = TRUE)'
'par(pty="s")'
     Save the plotting parameters and set the plotting region to
     "square".

'contour(x, y, f)'
'contour(x, y, f, nlevels=15, add=TRUE)'
     Make a contour map of f; add in more lines for more detail.

'fa <- (f-t(f))/2'
     'fa' is the "asymmetric part" of f.  ('t()' is transpose).

'contour(x, y, fa, nlevels=15)'
     Make a contour plot, ...

'par(oldpar)'
     ... and restore the old graphics parameters.

'image(x, y, f)'
'image(x, y, fa)'
     Make some high density image plots, (of which you can get
     hardcopies if you wish), ...

'objects(); rm(x, y, f, fa)'
     ... and clean up before moving on.

   R can do complex arithmetic, also.

'th <- seq(-pi, pi, len=100)'
'z <- exp(1i*th)'
     '1i' is used for the complex number i.

'par(pty="s")'
'plot(z, type="l")'
     Plotting complex arguments means plot imaginary versus real parts.
     This should be a circle.

'w <- rnorm(100) + rnorm(100)*1i'
     Suppose we want to sample points within the unit circle.  One
     method would be to take complex numbers with standard normal real
     and imaginary parts ...

'w <- ifelse(Mod(w) > 1, 1/w, w)'
     ... and to map any outside the circle onto their reciprocal.

'plot(w, xlim=c(-1,1), ylim=c(-1,1), pch="+",xlab="x", ylab="y")'
'lines(z)'
     All points are inside the unit circle, but the distribution is not
     uniform.

'w <- sqrt(runif(100))*exp(2*pi*runif(100)*1i)'
'plot(w, xlim=c(-1,1), ylim=c(-1,1), pch="+", xlab="x", ylab="y")'
'lines(z)'
     The second method uses the uniform distribution.  The points should
     now look more evenly spaced over the disc.

'rm(th, w, z)'
     Clean up again.

'q()'
     Quit the R program.  You will be asked if you want to save the R
     workspace, and for an exploratory session like this, you probably
     do not want to save it.