Alternatively, Statistics Dept graduate students can run R on the utstat research computer servers.

And, undergraduate students can run R on the computers in the FASIIT lab rooms in SS 561 and RW 107/109 (see schedules) by logging in with your UTORid (at least after "checking" it). There are also computers with R available for student use in the Data Library lab.

In a pinch, R can also be run remotely online.

Alternatively, you can install and use Rstudio, which is an integrated package designed to make R simpler and more intuitive to use. (I don't use Rstudio myself -- I always use traditional R -- but some people prefer using the Rstudio interface.)

**Simple arithmetic**
may be done directly, including simple expressions like:

3 + 4or more complicated expressions like:

3.7 + 11 * exp(2) - 17^2 + sqrt(5) / 3

w = 7.2Alternatively, the "=" may be replaced by "<-" (i.e. "less-than" followed by "dash"), as in "w <- 7.2".

**Lists**
of data may be entered directly using the "c( )" function, for example:

x = c(2, 4, 1.7, -3)Once this is done, then typing just "x" will output x as a list (i.e. vector). Or, typing e.g. "x[3]" will output the 3rd element, 1.7. Also "length(x)" is 4, so e.g. "x[length(x)]" will output -3.

Once x is a list, then its **sum**, **mean**
(x-bar), **variance** (s^2), **standard
deviation** (s), etc. can be computed directly:

sum(x)[Of course, "sd(x)" is the same as "sqrt(var(x))".] If you wish, you can assign these values to other variables, e.g. "mu = mean(x)", "s = sd(x)", etc.

mean(x)

var(x)

sd(x)

Lists can themselves be operated on. In the example above, "x^2" would produce the list (4, 16, 2.89, 9), so that e.g. "mean(x^2)" would give 7.9725. Equivalently, you could first do "z = x^2", and then "mean(z)" would also be 7.9725.

When typing commands in R, you can use the up-arrow key to retrieve
**previous commands**, and the left-arrow key to
**edit** a command that you've already typed.

R can also **generate pseudorandom values**.
For example, to generate 50 i.i.d. draws from a standard normal
distribution, type "rnorm(50)". To save them as a list called "y", type
"y = rnorm(50)". Then you can compute their mean etc. by "mean(y)",
"var(y)", "sd(y)", etc.

To generate from other distributions, use e.g. "rbinom(50, 10, 0.3)" for the Binomial(10, 0.3) distribution, "rpois(50, 14)" for the Poisson(14), "runif(10, 2, 4)" for Uniform[2,4], "rexp(50, 0.3)" for Exponential(0.3), etc. To compute cdf's, use e.g. "pnorm(1.2)" for the standard normal, "pexp(1.2, 0.3)" for Exponential(0.3), "pchisq(74.22, 100)" for the chisquared(100) distribution, "pt(2.228, 10)" for the t(10) distribution, "pf(5.7, 2, 10)" for the F(2, 10) distribution, etc.; for densities use "dnorm(1.2)", "dexp(1.2, 0.3)", etc.

R also has excellent **plotting** features. For example,
"plot(x)" will plot the individual values of x above, while "hist(x)"
will display a histogram. Also, "pie(x^2)" will display a pie-chart of
x^2 (or any other non-negative list). Try them! [If you prefer, you
can save your plot as a pdf file, by typing "pdf()", then "plot(x)" (or
whatever), and then "dev.off()". If you want a png or jpeg or postscript
file instead of pdf, then type "png()" or "jpeg()" or "postscript()"
instead of "pdf()".]

Once you have typed "y = rnorm(50)", then you can e.g. get a histogram of this sample by typing

hist(y)To then e.g. get a second histogram, of a fresh sample, overlaid on the first, in a different colour, you could type

hist(rnorm(50), add=TRUE, border=2)Points and lines can be added to plots using the commands "points" and "lines", respectively.

**Longer sequences of R commands** (e.g. full
**computer programs**) can be saved to a file, and then
executed by typing

source("filename")

The number sign # indicates a **comment** (useful for
explaining what you are doing); everything from # to the end of the line
is ignored by R.

**When you are done** with your computations, type
"quit()" or "q()" to quit R. (It might then ask you if you want to save
your workspace image; I always reply "n", but either option is fine.)

In addition, there is
lots of **documentation** about R available on the web, see e.g.
here or here or here
or here.
There are also lots of free online instructional videos, for example
this R video series
or this free online course.

Finally, R is a full-featured programming language (with "if", "for", "while", etc.), and has a huge number of other features and options not mentioned here. There is lots to learn and read and investigate and use -- try it out, and have fun!

-- Jeffrey S. Rosenthal, Department of Statistics, University of Toronto