P.Mean >> Category >> R software (created 2007-08-22).

These pages discuss how to program using R software, an open source package for statistical analysis. Also see Category: SPSS software, Category: Statistical computing.

2010

  1. P.Mean: Lessons learned the hard way: don't presume to know how your software handles missing value codes (created 2010-05-28). I'm working on an interesting project that involves summing up rvu's (resource value units) across certain records for a given patient. Some of the rvu's are missing. How should the program handle these missing rvu's. We discussed this by email and agreed to ignore missing rvu's in the sum. This is effectively the same as replacing the missing rvu's with zero. There is two cases worth worrying about, though, and handling those cases makes me realize just how tricky missing values are.
  2. P.Mean: Should I learn R instead of SAS (created 2010-04-05). I got a question from a statistician beginning her career asking whether she should learn SAS or R. That's a very personal question and there is no perfect answer. Here is what I wrote.

    2009
     
  3. P.Mean: Randomly generating simple math problems using R (created 2009-11-30). To help drill simple concepts in math for my second grade son, I developed a series of R programs to generate these problems randomly. It makes use of the sample function on a sequence of integers and allows you to limit or expand the scope of the problems generated. It is far from perfect, but it shows a few simple tricks in R.

Other resources:

Creative Commons License All of the material above this paragraph is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-05-28. The material below this paragraph links to my old website, StATS. Although I wrote all of the material listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright ownership of this material. The brief excerpts shown here are included under the fair use provisions of U.S. Copyright laws.

2008

Stats: Running R on a web server (June 17, 2008). I'm working on a project for planning and monitoring accrual patterns in clinical trials. This will eventually lead, I hope, to a grant to support this work. I have some existing R scripts and want to examine the possibility of running those scripts on a web page.

Stats: Stair step interpolation in R (November 15, 2007). I am working on some charts that show discrete (sudden) jumps at specific time points. This requires the use of stair step interpolation, because if you just connected the lines, it would imply a linear transition between consecutive points.

Stats: useR! 2007 conference in Ames, Iowa (April 6, 2007). I may not be able to go to it, but the R community has an annual meeting, useR!, that will be held this year in Ames, Iowa from August 8-10. The web site for this conference (user2007.org) provides some of the details.

Stats: R Wiki (March 27, 2007). I use R software for a lot of my complex data analyses and have written up a few web pages about various things you can do with R. It turns out that R has a Wiki site, R Wiki: wiki.r-project.org/rwiki/doku.php.

Stats: Randomly dividing a dataset in R (March 16, 2007). I'm working with someone who wants to do a simple cross-validation of a statistcal procedure. One simple way to do this is to randomly divide a data set into two piece. Assume that you have a matrix or data frame (x) that has n rows and you want to split the data set into a group that has proportion p of the rows and a group that has the remaining proportion (1-p). You want to do this randomly. Here is the code in R to do this.

Stats: Modular arithmetic and rounding in R (February 1, 2007). In certain programming situations, you need to perform calculations involving division that produce whole numbers as a result. For example, if you divided 27 by 4, you would get 6.75, but if you were using whole numbers only, then your result would be 6 with a remainder of 3. In R, the operator %/% produces an integer division, and the operator %% computes the remainder. So in R, the result of 27%/%4 would be 6 and the result of 27%%4 would be 3.

Stats: Graphics options in R (September 12, 2006). When you are producing graphics in R, the default option does not save your graphs for later review. You can change this in several ways. My comments will discuss the options for R running under Microsoft Windows. There are similar approaches that work for other systems.

Stats: Colors for R graphs (June 28, 2006). I tend to use color sparingly in graphs because most of my graphs end up in black and white in the final production. Even on my web pages, which appear in color, I try to avoid too much use of color because I often print these pages on a black and white printer.

Stats: Object oriented features of R (December 19, 2005). If you want to do any serious data analysis in R, you need to learn some of the object oriented features that this program has.

Stats: Group Sequential Monitoring of Clinical Trials in R (December 13, 2005). It is very expensive to purchase software that performs group sequential monitoring of clinical trials (sometimes called interim analysis). Group sequential monitoring is looking at a trial at selected time points during the study to see if you should stop the study early. There are a couple of functions in R that will do simple calculations, and the price, of course, is free.

Stats: Two nice R libraries (October 14, 2005). I found a couple of nice libraries in R available from CRAN (Comprehensive R Achive Network). The first, vcd, was recommended by a regular contributor to the epidemio-l list. This library provides visualization techniques with special emphasis on categorical data. I found the second library, epitools, when I went searching on the web for resources to calculate an exact confidence interval for a Poisson rate. In addition to the exact Poisson intervals, the package can perform age standardization, draw epidemic curves, and has a variety of useful utility functions and interesting data sets.

Stats: A simple trick in R (October 11, 2005). There may be times when you have a string in R that represents a specific R command. How would you run this command?

Stats: Dates in Excel and R (August 10, 2005). Every program uses a slightly different method for calculating date values. Excel, for example, counts the number of days since the start of 1900 (January 1, 1900=1) for Windows, but for the Macintosh it uses 1904 instead of 1900. R counts the number of days since the start of 1970 (January 1, 1970=0). It ignores fractional portions of the day.

Stats: Moving R objects (July 28, 2005). I regularly work from home on my laptop, and when I need to re-run some analyses in R, I usually just re-create the original data sets. But there are several ways you can transfer objects from one R system to another.

Stats: String manipulations in R (May 10, 2005). As part of my efforts to analyze microarray data, I am finding that I need to do simple string manipulations in R. Here is a list of functions that might help.

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