1. Hands-on Introduction to R

2. We live in oceans of data. Computers are essential to record and help analyse it. Competent scientists speak C/C++, Java, MATLAB, Python, Perl, R and/or Mathematica Data collection and analysis very important in Forensic Science since NAS 2009 Using the above languages, codes can easily be made available for review/discovery Why Leaning Programing?

3. All machines understand is on/off! High/low voltage High/low current High/low charge 1/0 binary digits (bits) To make a computer do anything, you have to speak machine language to it: Getting a computer to do anything useful 000000 00001 00010 00110 00000 100000 Add 1 and 2. Store the result. Wikipedia

4. Machine language is not intuitive and can vary a great deal over designs The basic operations operations however are the same, e.g.: Move data here Combine these values Store this data Etc. “Human readable” language for basic machine operations: assembly language Getting a computer to do anything useful

5. Assembly is still cumbersome for (most) humans Getting a computer to do anything useful MOV AL, 61h 10110000 01100001 Assembly A machine encoding Move the number 97 over to “storage area” AL

6. Better yet is a more “Englishy”, “high-level” language Enter: C, C++, Fortran, Java, … Higher level languages like these are translated (“compiled”) to machine language Not exactly true for Java, but it’s something analogous… Getting a computer to do anything useful

7. Even more “Englishy” and “high-level” are interpreted languages Enter: R MATLAB, Perl, Python, Mathematica, Maple, … The “code” of these languages are “interpreted” as commands by a program that is already running They make many assumptions behind the scenes Much easier to program with Much slower than compiled languages Getting a computer to do anything useful

8. R is not a black box! Codes available for review; totally transparent! R maintained by a professional group of statisticians, and computational scientists From very simple to state-of-the-art procedures available Very good graphics for exhibits and papers R is extensible (it is a full scripting language) Coding/syntax similar to Python and MATLAB Easy to link to C/C++ routines Why ?

9. Where to get information on R : R: http://www.r-project.org/http://www.r-project.org/ Just need the base RStudio: http://rstudio.org/http://rstudio.org/ A great IDE for R Work on all platforms Sometimes slows down performance… CRAN: http://cran.r-project.org/http://cran.r-project.org/ Library repository for R Click on Search on the left of the website to search for package/info on packages Why ?

10. Finding our way around R/RStudio Script Window Command Line

11. Basic Input and Output Handy Commands: x <- 4 x <- “text goes in quotes” variables: store information Numeric input Text (character) input :Assignment operator

12. Get help on an R command: If you know the name: ?command name ?plot brings up html on plot command If you don’t know the name: Use Google (my favorite) ??key word Handy Commands:

13. R is driven by functions: Handy Commands: func(arguement1, argument2) x <- func(arg1, arg2) function name input to function goes in parenthesis function returns something; gets dumped into x

14. Input from Excel Save spreadsheet as a CSV file Use read.csv function Needs the path to the file Handy Commands: "/Users/npetraco/latex/papers/data.csv” Mac e.g.: “C:\Users\npetraco\latex\papers\data.csv” Windows e.g.: *Exercise: basicIO.R

15. Matrices: X X[,1] returns column 1 of matrix X X[3,] returns row 3 of matrix X Handy functions for data frames and matrices: dim, nrow, ncol, rbind, cbind User defined functions syntax: func.name <- function(arguements) { do something return(output) } To use it: func.name(values) Handy Commands:

16. User defined function example: Compute the intensities of the Planck distribution Let the user input a Temperature Let the user input endpoint. Assume it is in nm Careful here. Make sure wavelength units are consistent with the other constants. What is the “easiest” thing to do?? Handy Commands:

17. Hints: Have the user set the wavenumber range and generate a sequence from it: The “wavenumber axis” Handy Commands: nut <- seq(from=nut.min, to=nut.max, length.out=2500)