Introduction to C

All statisticians should be proficient in C (for speed), perl (for data manipulation), and R (for interactive analyses and graphics). Think "CPR".

I use C for all computationally intensive tasks, but I no longer write stand-alone C programs. All of my C code is written within add-on packages for R. This makes the data input/output easy, and we don't need to worry about the interface. Plus, the results come out within R, and so they are immediately available for further analyses. Also, the R package incorporates documentation. Most importantly, R has an extensive library of mathematical and statistical functions that we can use directly from our C code.

While I intended this page to provide an introduction similar to my Introduction to Perl page, instead, I am leaving this as a list of links. The Writing R Extensions manual is particularly important, as it discusses package creation, the interface to C, and how to get at the R's library of code.

Resources

Books

• S Oliveira and D Stewart (2006) Writing scientific software: A guide to good style. Cambridge University Press.
• BW Kernighan and DM Ritchie (1988) The C programming language, 2nd edition. Prentice Hall, Englewood Cliffs, NJ.
• PJ Plauger (1992) The standard C library. Prentice Hall, Englewood Cliffs, NJ.
• WH Press et al. (1992) Numerical recipes in C: The art of scientific computing, 2nd edition. Cambridge University Press.
• BW Kernighan and R Pike (1999) The practice of programming. Addison-Wesley, Reading, MA.
• S Oualline (1992) C Elements of Style. M&T Books, San Mateo, CA.
• S Oualline (1997) Practical C programming, 3rd edition. O'Reilly & Associates, Sebastopol, CA.
• K Loudon (1999) Mastering algorithms with C. O'Reilly & Associates, Sebastopol, CA.
• MK Loukides et al. (1997) Programming with GNU software. O'Reilly & Associates, Sebastopol, CA.

• The complete list
• Writing R Extensions

All I need is available with R.