## Software Tools, Part 1: introduction to R software## R links- 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: Lattice graphics. (Skipped)
- R code: Probability distributions and their simulation. (Began on 17 Feb)
- R code: Summary statistics. (Skip)
- R code: Traditional tests and confidence intervals.
- R code: Simple linear regression. File e1.dat.
- R code: Linear regression when the predictors are numeric. File cement.dat.
- R code: Factors in linear models. Files bilirubin.dat and uffi.dat.
## ExercisesThere 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 The lecturer's solutions will be discussed during the lectures. - Set 1 - due to 7 Feb, 2011. Suggested solutions: r1sol.html.
- Set 2 - due to 14 Feb, 2011. Suggested solutions: r2sol.html.
- 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 Contributed Documentation. For instance, the documents written by Maindonald, Verzani, Faraway, and Paradis are very useful.
- You should print one of the following reference cards
for yourself.
- R reference card by Jonathan Baron. A basic reference card (one page).
- R reference card by Tom Short. A more advanced reference card (four pages).
- Seasoned users of Octave or Matlab should take a look at
- R and Octave by Robin Hankin.
- The 'official' R Wiki (at R project homepage -> Wiki)
- Rtips: tips and tricks collected by Paul Johnson.
## Suomenkielistä materiaalia R:n käytön tueksi- Jari Oksanen, R: Opas ekologeille.
- Marja-Leena Hannila ja Vesa Kiviniemi,
*R-opas: Alkeista tilastollisiin perusmenetelmiin*. - Lauri Nikkinen: http://www.r-ohjelmointi.org/ (suomenkielinen blogi R-ohjelmoinnista).
## Books on RSee 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 statistical analyses. - 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*, O'Reilly, 2009.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*. Springer, 2008.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 |