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R Programming lecture notes——University of Oxford

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R Programming

Robin Evans

robin.evans@stats.ox.ac.uk

Michaelmas 2014

This version: November 5, 2014

Administration

The course webpage is at

http://www.stats.ox.ac.uk/

evans/teaching.htm

Lectures are at 10am on Mondays and Wednesdays, and practicals at 9am

on Tuesdays and Thursdays; in reality, there will be rather a lot of overlap

between these two formats.

Please bring your own laptop to use during all classes, and ensure that you

have R working (see below). If you don’t have access to a laptop, let me

know and we will try to provide one.

I will hold oﬃce hours each week during Michaelmas term on Wednesdays

between 12pm and 1pm; my oﬃce is on the ﬁrst ﬂoor of 2 SPR, room

204. I’m very happy to help with any diﬃculties or problems you are having

with R, but please take steps to help yourselves ﬁrst (see below for a

list of resources).

Software

You should install R on your own computer at the ﬁrst opportunity. Visit

http://cran.r-project.org/

for details. Ensure you have the latest version (as of the start of Michaelmas

2014, this was version 3.1.1). Try to spend some time getting used to the

basics of the software, including arithmetic operations and functions. There

are many excellent online tutorials for this purpose.

Resources

A strength of R is its help ﬁles, which we will discuss. These are accessed

with the ? and ?? commands.

The internet has almost all the answers, and knows much more about R

than I do. If you have a problem, it’s extremely likely that someone will

have had the same diﬃculty already, and posted a question on an internet

forum.

Books are useful, though not required. Here are a some of them with brief

comments.

1. Venables, W.N. and Ripley, B.D. (2002) Modern Applied Statistics with

S. Springer-Verlag. 4th edition.

The classic text.

2. Chambers (2010) - Software for Data Analysis: Programming with R,

Springer.

One of few books with information on more advanced programming (S4,

overloading).

3. Wickham, H. (2014) Advanced R. Chapman and Hall.

A great new book on the more advanced features: a good follow up to this

class.

4. Crawley, M. (2007) The R Book. Wiley.

Very thorough.

5. Fox, J. (2002) A R and S-PLUS Companion to Applied Regression. Sage.

Does what it says.

6. Ligges, U. (2009) Programmieren mit R. Third edition. Springer.

In German(!)

7. Rizzo, M. L. (2008) Statistical Computing with R. CRC/Chapman &

Hall.

More computational – diﬀerent examples to the other books.

8. Braun, W. J. and Murdoch, D. J. (2007) A First Course in Statistical

Programming with R. CUP.

Detailed and well written, but at a rather low level. A bit redundant given

the above.

9. Maindonald J. and Braun, W. J. (2003) Data Analysis and Graphics

using R Second or third edition CUP.

Advanced statistical graphics

10. Spector, P. (2008) Data Manipulation with R. Springer

Especially for data manipulation.

11. Dalgaard, P. (2009) Introductory Statistics with R. Second Edition. Springer.

Probably redundant given the above.

Getting the Most out of the Class

Learning R has much in common with learning a natural language: it’s easy

to get going with a few simple phrases, though you’ll ﬁnd some idiosyn-

crasies in the syntax, and occasional aspects are downright illogical. Once

you’ve mastered these few diﬃculties, the only barrier to ﬂuency is the vast

vocabulary of R: even in the basic packages there are many commands which

you will never use or understand, but the more you learn the more elegantly

you will be able to express yourself. There is a smaller core of ‘everyday’ lan-

guage which we will focus on, and which you will be expected to understand

in exams and practical assessments.

These lecture notes are intended for reference, and will (by the end of the

course) contain sections on all the major topics we cover. Lectures will not

follow the notes exactly, so be prepared to take your own notes; the practical

classes will complement the lectures, and you can be examined on anything

we study in either.

Don’t copy and paste the commands from this guide into R; you will ﬁnd

it very hard to remember the details of the language and will have to look

everything up when you come to code something yourself.

Make sure you try the exercises, and understand the code involved in

each one; if something doesn’t make sense, use R’s help functions, ask a

classmate, try using internet resources, or ask me for help (preferably in

that order). Some exercises are marked with an asterisk (*), which means

they are a little more advanced than is necessary for the class.

If you ﬁnd any mistakes or omissions in these notes, I’d be very grateful to

be informed.

1 Introduction

1.1 What R is good at

Statistics for relatively advanced users: R has thousands of packages, de-

signed, maintained, and widely used by statisticians.

Statistical graphics: try doing some of our plots in Stata and you won’t have

much fun.

Flexible code: R has a rather liberal syntax, and variables don’t need to be

declared as they would in (for example) C++, which makes it very easy to

code in. This also has disadvantages in terms of how safe the code is.

Vectorization: R is designed to make it very easy to write functions which

are applied pointwise to every element of a vector. This is extremely useful

in statistics.

R is powerful: if a command doesn’t exist already, you can code it yourself.

1.2 What R is not so good at

Statistics for non-statisticians: there is a steep learning curve, which puts

some people oﬀ. Try Stata, SAS or SPSS (if you must).

Numerical methods, such as solving partial diﬀerential equations; try Mat-

lab.

Analytical methods, such as algebraically integrating a function. Try Math-

ematica or Maple.

Precision graphics, such as might be useful in psychology experiments. Try

Matlab.

Optimization. Though it does have some very easy to use methods built-in.

Low-level, high-speed or critical code; use C, C++, Java or similar. (How-

ever note that such code can be called from R to give the ‘best of both

worlds’.

1.3 General Properties

R makes it extremely easy to code complex mathematical or statistical proce-

dures, though the programs may not run all that quickly. You can interface

R with other languages (C, C++, Fortran) to provide fast implementations

of subroutines, but writing this code (and making it portable) will typically

take longer. Where the advantage falls in this trade-oﬀ will depend upon

what you’re doing; for most things you will encounter during your degree, R

is suﬃciently fast.

R is open source and widely adopted by statisticians, biostatisticians, and

geneticists. There is a huge wealth of existing libraries so you can often

save time by using these, though it is sometimes easier to start from scratch

than to adapt someone else’s function to meet your needs. Contributing new

packages to the central repository (CRAN) is easy: even your lecturer has

managed it. As a result, R packages are not build to very high standards

(but see Bioconductor).

R is portable, and works equally well on Windows, OS X and Linux.

1.4 Interfaces

For Windows and OS X, the standard R download comes with an R GUI,

which is adequate for simple tasks. You can also run R from the command

line in any operating system.

There are a number of more powerful interfaces which you may like to try.

Here’s a few.

RStudio. Very popular, with a nice interface and well thought out, espe-

cially for more advanced usage: can be a bit buggy, so make sure you

update it regularly. Available on all platforms.

Emacs with ESS. (Emacs Speaks Statistics) is available on all platforms,

and is very powerful when you get used to it. Has a habit of freezing

in my experience, though.

TinnR. Alternative Windows interface.

I intend to demonstrate a few of these diﬀerent approaches during class.

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