Packages can have namespaces, and currently all of the base and recommended packages do expect the datasets package. Namespaces do three things: they allow the package writer to hide functions and data that are meant only for internal use, they prevent functions from breaking when a user (or other package writer) picks a name that clashes with one in the package, and they provide a way to refer to an object within a particular package.
For example, t() is the transpose function in R, but users might define their own function named t. Namespaces prevent the user’s definition from taking precedence, and breaking every function that tries to transpose a matrix.
There are two operators that work with namespaces. The double-colon operator :: selects definitions from a particular namespace. In the example above, the transpose function will always be available as base::t, because it is defined in the base package. Only functions that are exported from the package can be retrieved in this way.
The triple-colon operator ::: may be seen in a few places in R code: it acts like the double-colon operator but also allows access to hidden objects. Users are more likely to use the getAnywhere() function, which searches multiple packages.
Packages are often inter-dependent, and loading one may cause others to be automatically loaded. The colon operators described above will also cause automatic loading of the associated package. When packages with namespaces are loaded automatically they are not added to the search list.
Appendix A A sample session
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.
Login, start your windowing system.
$ R Start R as appropriate for your platform.
The R program begins, with a banner.
(Within R, 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$y)
Add in the local regression.
abline(0, 1, lty=3)
The true regression line: (intercept 0, slope 1).
abline(coef(fm))
Unweighted regression line.
abline(coef(fm1), col = "red") Weighted regression line.
detach() Remove data frame from the search path.
plot(fitted(fm), resid(fm), xlab="Fitted values", ylab="Residuals",
main="Residuals vs Fitted")
A standard regression diagnostic plot to check for heteroscedasticity. Can you see it?
qqnorm(resid(fm), main="Residuals Rankit Plot")
A normal scores plot to check for skewness, kurtosis and outliers. (Not very useful here.)
rm(fm, fm1, lrf, x, dummy) Clean up again.
The next section will look at data from the classical experiment of Michaelson and Morley to measure the speed of light. This dataset is available in the morley object, but we will read it to illustrate the read.table function.
filepath <- system.file("data", "morley.tab" , package="datasets") filepath Get the path to the data file.
file.show(filepath)
Optional. Look at the file.
mm <- read.table(filepath)
mm Read in the Michaelson and Morley data as a data frame, and look at it. There are five experiments (column Expt) and each has 20 runs (column Run) and sl is the recorded speed of light, suitably coded.
mm$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 −π ≤ x ≤ π. 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 + x2).
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), . . .
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.