Software Tools, Part 1: introduction to R software
- R project homepage.
- CRAN (the Comprehensive R Archive Network) for downloading
versions of R for
Windows, Mac and Linux computers.
Online material on R for the Software Tools course
- Getting to know R: how to use R in room C128; general instructions
on using R; how to install R on your own computer.
The material consists of web pages and files containing R code. You should
download the R code files and put them in a suitable directory.
Then you are supposed to run the code a few lines at time, and
inspect the results.
(There is more material here than what will be covered during the course.)
- R code: Using R as a calculator. Variables and
assignment statements. (31 Jan)
- R code: Function calls; getting help. (31 Jan)
- R code: Vectors and indexing. (31 Jan and 3 Feb)
- R code: Lists. (3 Feb)
- R code: Matrices and
arrays. (3 Feb)
- R code: Factors. (3 Feb)
- R code: Data frames. (3 Feb and 7 Feb)
- R code: Data input.
Copy also the files e1.dat, e2.dat and
e3.dat. (7 Feb)
- R code: Function apply() and related functions. (7 Feb)
- R code: Writing your own functions. (7 Feb and 10 Feb)
- R code: Conditional execution. (10 Feb)
- R code: Loops. (10 Feb)
- R code: Functions source(), sink(), save() and
load(). (10 Feb)
- R code: Classes and methods. (10 Feb)
- R code: Dates. (Skipped at lectures)
- R code: Introduction to R graphics. (10 Feb)
- R code: Saving graphics to a file. (14 Feb)
- Traditional R graphics
- R code: Function plot(). (14 Feb)
- R code: Arguments to plot(). (14 Feb)
- R code: Interaction with plots. (Skipped)
- R code: Adding material to plots. (17 Feb)
- R code: Other high-level functions in
traditional graphics. (17 Feb)
- R code: Lattice graphics. (Skipped)
- R code: Probability distributions and their
simulation. (Began on 17 Feb)
- R code: Summary statistics. (Skip)
- R code: Traditional tests and
- R code: Simple linear regression.
- R code: Linear regression when the predictors are
numeric. File cement.dat.
- R code: Factors in linear models. Files
bilirubin.dat and uffi.dat.
There will be four sets of exercises with 5 or 6 problems in a set.
You will get the credits for the first part of the course by sending
a sufficient number of solutions to the problems by email.
In order to get the credits, send solutions to
- at least half of the total number of problems and
- at least two problems from each of the four sets.
(but if you give me a good reason, I may make an exception to these rules).
The lecturer's solutions will be
discussed during the lectures.
- Set 1 - due to 7 Feb, 2011. Suggested solutions:
- Set 2 - due to 14 Feb, 2011. Suggested solutions:
- Set 3 - due to 21 Feb, 2011.
Suggested solutions: r3sol.html.
- Set 4 - due to 3 March (notice that deadline is on
Thursday). The solutions will be discussed on 3 March.
Suggested solutions: r4sol.html.
Here are your points.
Online documentation for R
- The R program comes with its manuals, which you can read through
the help menu of Rgui. They are also available
on the page
The R Manuals
of CRAN. See especially the manual An Introduction to R.
- In addition to the official documentation, CRAN contains a
lot of contributed documentation, which you can find on the page
For instance, the documents written by Maindonald, Verzani, Faraway,
and Paradis are very useful.
- You should print one of the following reference cards
- Seasoned users of Octave or Matlab should take a look at
- The 'official' R Wiki (at R project homepage -> Wiki)
- Rtips: tips and tricks
collected by Paul Johnson.
Suomenkielistä materiaalia R:n käytön tueksi
Books on R
See R project -> Books
for a comprehensive list of books.
I have seen and can recommend the following books.
- P. Dalgaard. Introductory Statistics with R, 2nd ed., Springer, 2008.
A compact book which shows the beginner how to use R for typical
- J. Maindonald and J. Braun.
Data Analysis and Graphics Using R: An Example-based Approach,
Cambridge University Press, 2003.
Another book which shows the beginner how to use R for making
statistical analyses. Slightly more advanced and lot bulkier
than the book by Dalgaard.
- J. Adler.
R in a Nutshell: A Desktop Quick Reference,
A refence book which also suits the needs of a beginner.
- W. J. Braun and D. J. Murdoch.
A First Course in Statistical Programming with R,
Cambridge University Press, 2007.
A compact book which introduces R and statistical programming in general.
- W. N. Venables and B. D. Ripley.
Modern Applied Statistics with S.
Fourth Edition. Springer, 2002.
How to make a host of modern statistical analyses
with S or R. The library MASS is connected with it.
- B. S. Everitt and T. Hothorn,
A Handbook of Statistical Analyses Using R, 2nd ed.,
CRC Press, 2010.
How to analyze various statistical
models with R.
- M. Aitkin and B. Francis and J. Hinde and R. Darnell,
Statistical Modelling in R,
Oxford University Press, 2009.
How to analyze various statistical models with R.
- P. Murrell. R Graphics. Chapman & Hall/CRC, 2005.
The best description of the graphics facilities of R.
- D. Sarkar. Lattice: Multivariate Data Visualization with R.
The best available resource on lattice graphics.
- W. N. Venables and B. D. Ripley.
S Programming. Springer, 2000.
A thorough exposition of programming in the S or R language
for people who already know how to use the system.
- U. Ligges. Programmieren mit R. 3rd edition, Springer, 2008.
A compact exposition of programming in the R language for
people who are not afraid to read German.
Last updated 2011-03-03 10:06
petri.koistinen 'at' helsinki.fi