A quick tour and overview of toolkits in R, Python and C++ for analytics applications.

1. A Programming
Language/Toolkit Tour
Rory Winston
Monday, 27 February 2012

2. Agenda
• A quick overview and tour of:
•R
• Python
• Java/C++
• For data analysis/analytics applications
• Comparison
Monday, 27 February 2012

3. Purpose
• To give a feeling for the relative advantages
and disadvantages of each approach
• Understand the tradeoffs involved
• See some demos
Monday, 27 February 2012

4. R
• R is a domain-specific-language (DSL) for statistics
and data analysis
• Functional-based language
• Based on an earlier language called S
• Core engine written in C
• Open-source
• Popularity has exploded in the last few years
• Some commercial support
Monday, 27 February 2012

5. Pros
• R is the de facto standard in statistical analysis tooling
• Incredible range of functionality via contributed libraries
• Powerful interactive analysis environment and
visualization tools
• Large number of built-in datasets
• Cross-platform
• Broad user community
• Wide range of resources (books, tutorials, papers)
available
Monday, 27 February 2012

6. Cons
• Performance limitations
• Single-threaded interpreter
• Language limitations and quirks
• Initial learning curve may be steep
• R gives you a lot of power, but assumes
you know how to use it!
Monday, 27 February 2012

7. Language Features
• R is vectorized:
• Loops are not required for many operations
(and are actually discouraged)
• R is functional:
• Functions can be passed around like other
variables
• R integrates with a BLAS:
• high-performance numerical operations
Monday, 27 February 2012

8. Demo
• Console R
• R GUI
• RStudio
Monday, 27 February 2012

9. Tips
• Learn how to use ggplot2 (http://had.co.nz/
ggplot2/)
• Consider using RStudio (http://
www.rstudio.org)
Monday, 27 February 2012

10. Python
• Initially developed in the late 1980s
• Object-oriented / functional support
• Open-source
• Initially popular in web applications, now
popular across a number of domains
Monday, 27 February 2012

11. Pros
• Very readable, simple and clear syntax
• Well-supported (many libraries and
extensions)
• Easy to integrate with other languages (e.g.
C)
• Very efficient environment to develop in
Monday, 27 February 2012

12. Cons
• Language syntax is not universally popular
• In terms of analytics, many libraries are still
slightly immature
• Performance can be lacking (although there
are many options to tune it)
• Interpreter is effectively single-threaded
Monday, 27 February 2012

13. Python + Analytics
• There are a number of excellent libraries available
for analytics applications:
• NumPy + SciPy
• matplotlib
• pandas
• scikits
• Some packages (e.g. pandas) are designed to replicate
the ‘feel’ and functionality of analysis operations in R
Monday, 27 February 2012

14. NumPy + SciPy
• Using NumPy + SciPy + matplotlib provides
an experience similar to using an
interactive R/Matlab environment
• Supports vectorization and BLAS
integration
• Add ipython for more goodness
Monday, 27 February 2012

15. Tips
• Use ipython!
• Check out:
• http://pandas.pydata.org/
• http://statsmodels.sourceforge.net/
• http://scikit-learn.org
Monday, 27 February 2012

16. Comparisons
x <- 1:10
x = arange(1,11)
x <- seq(1, 2, .2)
x = arange(1,2,.2)
x <- seq(1,2,
x = linspace(1,2,15)
length.out=15)
M <- arange
M <- matrix(1:100, 10,
(1,101).reshape(10,10)
10)
x[x < 1.5]
x[ x < 1.5 ]
X = colstack((a,b))
X <- cbind(a,b)
Monday, 27 February 2012

17. Java/C++
• The ultimate in power/flexibility
• Also the ultimate in development time and
effort
• Lets just look at C++ briefly
Monday, 27 February 2012

18. C++
• Old but still very popular
• Just had a revamp (C++11, was C++0x)
• Mostly competes with Java on the server side
• Everything else (JVM, R, Python) is written in C/C
++
• Both R and Python provide easy ways to interface
with C/C++ code
• This is used a lot
Monday, 27 February 2012

19. Pros
• Flexibility
• Lots of libraries available
• Control of resources for performance-
critical apps (e.g. memory)
• C++11 adds a lot of nice stuff (finally)
Monday, 27 February 2012

20. Cons
• Lots of effort
• Lots of hidden traps for the unwary
• Initial experience may be a large productivity
hit
• Effort in porting between systems
• There is “modern” C++ (which is actually
pretty nice) and everything else (which isn’t so
nice)
Monday, 27 February 2012

21. Examples
• Lets look at a sample library
• This one is called Armadillo (http://
arma.sourceforge.net/)
• Developed in Australia (NICTA / Univ.
Queensland)
• Contains functions for numerical applications
and some statistical functions
• Modern, efficient use of C++
Monday, 27 February 2012

22. Armadillo
• Armadillo supports vectorized operations
• Also integrates with a BLAS
• Example (see console)
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23. Simple Example
• Using the Box-Jenkins airline passenger
data
• Classic dataset
• 12 years of monthly airline passenger
observations (144 in all)
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24. Passenger Dataset
Monday, 27 February 2012

25. Linear Model
• We will use a simple linear model (explains
85% of the variance of this data)
Ax = b
 
1 t1
1 t2 
A=
1

t3 
... ...
Monday, 27 February 2012

26. Conclusion
• Use the toolkit that’s most appropriate for
you
• Common approches are to use e.g. R for
prototyping and model selection and (if
required) switch to a higher-performance
implementation for production
• If you have time, learn all of them!
Monday, 27 February 2012

27. Language Map
Dynamic Typing Static Typing
Interactivity
R
Python Java
Octave
Ruby C/C++
Performance, complexity
Monday, 27 February 2012

28. Resources
Monday, 27 February 2012