1. John D. Cook M. D. Anderson Cancer Center www.JohnDCook.com
Why and how people use R
John D. Cook
M. D. Anderson Cancer Center

2. What is R? Open source statistical language
De facto standard for statistical research
Grew out of Bell Labs’ S (1976, 1988)
Influenced by Scheme, Fortran
Quirky, flawed, and an enormous success
Fortran: unit offset vectors, column-major storage, cryptic names. As Dennis Ritchie said about C.

3. R in data analysis
Languages used in Kaggle.com data analysis competition 2011 Source:

4. R in bioinformatics (2012)
Bioinformatics means a lot of different things. Imagine that those using Perl are not doing statistics.

5. So what is using R like?

6. "Using R is a bit akin to smoking.
The beginning is difficult, one may get headaches and even gag the first few times.
But in the long run, it becomes pleasurable and even addictive.
Yet, deep down, for those willing to be honest, there is something not fully healthy in it.“
-- Francois Pinard

7. “… R has a unique and somewhat prickly syntax and tends to have a steeper learning curve than other languages.”
Drew Conway
John Myles White

8. The R Inferno
by Patrick Burns
pages/Tutor/R_inferno.pdf

9. What about speed?
Refer to Julia slides

10. Maybe 100x slower than C++, though it varies greatly.

11. What about tool support?

12. Limited compared to, for example, first release of Visual Studio (1995).

13. So why do statisticians use R?
“The best thing about R is that it was written by statisticians.
The worst thing about R ...”
Bo Cowgill, Google

14. R is a DSL To understand a DSL, start with D, not L.
The alternative to R isn’t Python or C#, it’s SAS.
People love their DSL, and will use it outside of its domain.

15. What are statisticians like?
Different priorities than software developers
Different priorities than mathematicians
Learn bits of R in parallel with statistics

16. Why a statistical DSL? Statistical functions easily accessible
Convenient manipulation of tables
Vector operations
Smooth handling of missing data
Patterns for common tasks
Bruce Payette: It’s a shell!

17. Some advantages of R Batteries included, one namespace
Contrast Python + matplotlib + SciPy + IPython
Designed for interactive data analysis
Easier to program than, e.g., SAS
Open source, interpreted, portable
Succinct notation for querying and filtering
Succinct notation for linear regression
Open source: i.e. can avoid purchasing bureaucracy, students and colleagues in poor areas can use same software

18. Examples Set all NA elements of x to 0.
x[ is.na(x) ] <- 10
Fit a linear regression model to w as a function of x, y, and z,
including a constant term and
all first order interaction terms except xz.
model ~ lm(w ~ (x + y + z)^2 – x:z)

19. Simple regression
growth tannin
12 0
10 1
8 2
11 3
6 4
7 5
2 6
3 7
3 8

20. Regression example
> data <- read.table("regression.txt", header=T)
> attach(data)
> names(data)
[1] "growth" "tannin"
> model <- lm( growth ~ tannin )
> summary(model)
...
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) e-06 ***
tannin ***
Residual standard error: on 7 degrees of freedom
Multiple R-squared: , Adjusted R-squared:
F-statistic: on 1 and 7 DF, p-value:

21. Recycling
Strange, dangerous, or convenient, depending on your perspective
If you add two vectors of different lengths, the shorter will be recycled to be as long as the longer vector.
Example: > 1:6 + c(10, 100)

22. Recycling cont. > x <- matrix(1:6, ncol = 2) > x [,1] [,2]
[1,]
[2,]
[3,]
> x + c(10, 100)
[1,]
[2,]
[3,]

23. Lessons from R for language designers
Users may have very different priorities than computer scientists.
Users will put up with a lot of crap to get their work done.
Users will a familiar tool over a better tool if at all feasible.

24. Resources http://www.r-project.org/
R_language_for_programmers.html
“The Art of R Programming” by Normal Matloff
@RLangTip