Here are the notes I took while discovering and using the r programming statistics pdf environment R. Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document.
Generalized Linear Models: logistic regression, Poisson regression, etc. Abstract: If you are using R and you think you’re in hell, this is a map for you. A book about trouble spots, oddities, traps, glitches in R. Getting it You can get a free pdf of The R Inferno. Alternatively you can spend an extraordinary amount on the paperback of The R Inferno. Impatient R is an introduction to using R.
Reading and writing both ascii files and binary files. Sending an R data object to someone else, either in email or as a binary file. Make a “zoo” object, for handling time-series data. Plotting two series on one graph, one with a left y axis and another with a right y axis. Estimate beta of Sun Microsystems using data from Yahoo finance : Elaborate version, Terse version. All these examples in one tarfile.
Outright non-working code is unlikely, though occasionally my fingers fumble or code-rot occurs. More importantly, I am not an R guru. So I will always be happy if you alert me to mistakes or improvements. 3rd party software, written by people all over the world. This is called `CRAN’ for Comprehensive R Archive Network. You will be amazed at the quality and comprehensiveness of the code out there. The dynamism of R and of the surrounding 3rd party packages has thrown up the need for a newsletter, R News.
You should make it a point to look hard at back issues. The standard online documents associated with R tend to be reference manuals targeting someone who already knows quite a bit. The articles in R News are very valuable in taking you from scratch to understanding R. But you will learn a lot more by reading the article Resampling Methods in R: The boot package by Angelo J. Canty, which appeared in the December 2002 issue of R News. R programming help, code, and how-to’s.
Help with R Programming Finding R programming frustrating? Go from learning R to using R with examples, tips, code, help, and how-to’s. Ready to Get Started With R? Wondering if You Should Learn R? R is a free, open source statistical programming language that is available for Windows, Mac and Linux. It consists of the base or core R software as well as add-on packages that extend functionality. What Software is Similar to R?
R is comparable to SAS, SPSS and Stata. R can be used for statistical analysis, graphics, and reporting. R can be used to manipulate data, run statistical analyses such as descriptive statistics, t-tests, regressions, and produce charts. R can even be used to make maps and play minesweeper. Why You Should Use R R has a number of advantages over comparable commercial software packages.
The first and most obvious advantage is that it is free. For students and anyone on a budget this can be a major advantage. Another related advantage is that you can install it on as many computers as you want to. Want to install R on two computers in the office, your home desktop and your laptop? No complicated and expensive multi-computer license is needed.
A third advantage is that R is being used at many universities and businesses. This means that R users are able to collaborate and learn from other R users and that there are many opportunities for those who can program in R. One final advantage of R is that, because anyone can write an add-on package for it, R has the most advanced analyses and is always being added to. If the function you want isn’t available you can write your own package and share it with the world.
Why You Should Not Use R R has a steep learning curve if you don’t have computer programming experience. This is because R has a very limited graphical user interface. R programming is done almost exclusively through code. R’s dependence on programming can be both a benefit and a drawback.
It is a benefit because methods are very repeatable with little extra effort. Another problem with R is that there are few resources for learning the basics. There are many websites devoted to cutting edge analyses and graphics in R but very few that show how to manipulate data and conduct basic analyses. What Support is Available for R Programming?