The R page


I have been involved in statistics since taking a minor in mathematics at University and later in graduate school applied descriptive statistics to much of my work.  Since that time in the mid-1950's I have used statistics regularly, initially developing my own FORTRAN code and later using SAS.  Undoubtedly SAS is an excellent system and I like it a great deal but in order to continue to use it I had to become the SAS officer for the Department as no one else was interested in taking on the job - this was too much trouble after my retirement so I looked at the public domain statistical package R.  I had a little advantage because I had an early license to use S-plus on my Silicon Graphics Unix machine when at LSU.   R is an excellent mathematical statistical package, well supported and, like all open source programs, has many people willing to give free advice.  I like it and highly recommend it.

I got involved with R because I was interested in a problem involving classification systems. I have used discriminant function analysis to develop classification systems since the early 1960's and since moving over to R have had the chance to explore the use of that statistical package for classifying unknowns.  For  small to medium sized datasets R is an excellent and very speedy tool.  Moreover, it can be integrated into web-sites for online data analysis and tied into the GRASS GIS system for data analysis.  Once the confusion of data input is mastered using the actual statistical procedures is a joy: R procedures are easy to use.  Simple graphical output also is easy using the more complex graphical procedures requires some practice and understanding of the basic programming methodology used by R.

'R' resource eBook: WWW links for 'R'

1. R for data analysts

A. publications

  1. data input

  2. data-frames

  3. tables

  4. simple plots uni- and bi-variate]

  5. summaries

  6. regression, linear and additive models

  7. ANOVA

  8. covariance

  9. compositional data

  10. classification procedures

  11. time-series

  12. non-linear models

B. manuals

  1. data input

  2. data-frames

  3. tables

  4. simple plots uni- and bi-variate]

  5. summaries

  6. regression, linear and additive models

  7. ANOVA

  8. covariance

  9. compositional data

  10. classification procedures

  11. time-series

  12. non-linear models

C. slide presentations

  1. data input

  2. data-frames

  3. tables

  4. simple plots uni- and bi-variate]

  5. summaries

  6. regression, linear and additive models

  7. ANOVA

  8. covariance

  9. compositional data

  10. classification procedures

  11. time-series

  12. non-linear models

D. coding examples

  1. data input

  2. data-frames

  3. tables

  4. simple plots uni- and bi-variate]

  5. summaries

  6. regression, linear and additive models

  7. ANOVA

  8. covariance

  9. compositional data

  10. classification procedures

  11. time-series

  12. non-linear models

2 R for mathematicians/statisticians

A. publications

  1. functions

  2. continuous distributions

  3. discrete distributions

  4. matrix algebra

  5. calculus

  6. simulation models

B. manuals

  1. functions

  2. continuous distributions

  3. discrete distributions

  4. matrix algebra

  5. calculus

  6. simulation models

C. slide presentations

  1. functions

  2. continuous distributions

  3. discrete distributions

  4. matrix algebra

  5. calculus

  6. simulation models

D. coding examples

  1. continuous distributions

  2. discrete distributions

  3. matrix algebra

  4. calculus

  5. simulation models

3  R programming

A. publications

  1. style

  2. syntax

  3. classes, methods and objects

  4. vectors

  5. matrices

  6. arrays

  7. lists

  8. factors

  9. data-frames

  10. writing functions

  11. debugging functions

B. manuals

  1. style

  2. syntax

  3. classes, methods and objects

  4. vectors

  5. matrices

  6. arrays

  7. lists

  8. factors

  9. data-frames

  10. writing functions

  11. debugging functions

C. slide presentations

  1. style

  2. syntax

  3. classes, methods and objects

  4. vectors

  5. matrices

  6. arrays

  7. lists

  8. factors

  9. data-frames

  10. writing functions

  11. debugging functions

D. coding examples

  1. style

  2. syntax

  3. classes, methods and objects

  4. vectors

  5. matrices

  6. arrays

  7. lists

  8. factors

  9. data-frames

  10. writing functions

  11. debugging functions

4 R advanced graphics

A. publications

  1. graphic objects

  2. graphic commands

  3. plot types

  4. multiple plots

  5. layout

  6. low level functions

  7. trellis graphics

  8. spatial distributions

  9. GRASS gis

B. manuals

  1. graphic objects

  2. graphic commands

  3. plot types

  4. multiple plots

  5. layout

  6. low level functions

  7. trellis graphics

  8. spatial distributions

  9. GRASS gis

C. slide presentations

  1. graphic objects

  2. graphic commands

  3. plot types

  4. multiple plots

  5. layout

  6. low level functions

  7. trellis graphics

  8. spatial distributions

  9. GRASS gis

D. coding examples

  1. graphic objects

  2. graphic commands

  3. plot types

  4. multiple plots

  5. layout

  6. low level functions

  7. trellis graphics

  8. spatial distributions

  9. GRASS gis