R programming presentation

A Seminar
on
R-Programming
Presented By:
Akshat Sharma
CSE-1, 4th Year
1200112029
mailidakshat@gmail.com

Introduction to R
• The R language is a project designed to create a free, open source language
which can be used as a replacement for the S language, R was created by Ross
Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and
is currently developed by the R Development Core Team, of which Chambers
is a member. R is named partly after the first names of the first two R authors
and partly as a play on the name of S.
• R is an open source implementation of S, and differs from S-plus largely in
its command-line only format.
• R first appeared in 1993; 22 years ago.
• S is a statistical programming language developed primarily by John
Chambers and Rick Becker and Allan Wilks of Bell Laboratories.

• R has a broad set of facilities for doing statistical analyses. In addition, R is
also a very powerful statistical programming language. The open-source
nature of R means that as new statistical techniques are developed, new
packages for R usually become freely available very soon after.
• R is designed to be very portable: it will run on Microsoft Windows, Linux,
Solaris, Mac OSX, and other operating systems, but different binary versions
are required for each.
• The R system is mainly command-driven, with the user typing in test and
asking R to execute it.
• R is polymorphic, which means that the same function can be applied to
different types of objects, with results tailored to the different object types.
Such a function is called generic function.

Installing R
• R is freely downloadable from http://cran.r-project.org/ , as is a wide range
of documentation.
• Most users should download and install a binary version. This is a version
that has been translated (by compilers) into machine language for execution
on a particular type of computer with a particular operating system.
Installation on Microsoft Windows is straightforward.
• A binary version is available for windows 98 or above from the web page
http://cran.r-project.org/bin/windows/base .

The R Console
• The R console is the most important tool for using R. The R console is a tool
that allows you to type commands into R and see how the R system responds.
• The commands that you type into the console are called expressions. A part
of the R system called the interpreter will read the expressions and respond
with a result or an error message.
• Sometimes, you can also enter an expression into R through the menus.
• If you’ve used a command line before or a language with an interactive
interpreter such as LISP, this should look familiar. If not: don’t worry.
Command-line interfaces aren’t as scary as they look.

• R provides a few tools to save you extra typing, to help you find the tools
you’re looking for, and to spot common mistakes.
• By default, R will display a greater-than sign (“>”) in the console (at the
beginning of a line, when nothing else is shown) when R is waiting for you to
enter a command into the console. R is prompting you to type something, so
this is called a prompt.

Basic Arithmetic and Objects
• R has a command line interface, and will accept simple commands to it.
• This is marked by a > symbol, called the prompt. If you type a command and
press return, R will evaluate it and print the result for you.
> 6 + 9
[1] 15
> x <- 15
> x - 1
[1] 14
• The expression x <- 15 creates a variable called x and gives it the value 15. This is called
assignment; the variable on the left is assigned to the value on the right. The left hand
side must contain only contain a single variable.

Assignment can also be done with = (or ->).
> x + 4 <- 15 # doesn't work
> x = 5
> 5*x -> x
> x
[1] 25
The operators = and <- are identical, but many people prefer <- because it
is not used in any other context, but = is, so there is less room for confusion.

Program Example
Short R code calculating Mandelbrot set through the first 20 iterations
of equation z = z2 + c plotted for different complex constants c.
This example demonstrates:
• use of community-developed external libraries (called packages), in this case
caTools package
• handling of complex numbers
• multidimensional arrays of numbers used as basic data type, see
variables C, Z and X.

install.packages("caTools") # install external package
library(caTools) # external package providing write.gif function
jet.colors <- colorRampPalette(c("green”, "blue", "red", "cyan", "#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000"))
m <- 1000 # define size
C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ),
imag=rep(seq(-1.2,1.2, length.out=m), m ) )
C <- matrix(C,m,m) # reshape as square matrix of complex numbers
Z <- 0 # initialize Z to zero
X <- array(0, c(m,m,20)) # initialize output 3D array
for (k in 1:20) { # loop with 20 iterations
Z <- Z^2+C # the central difference equation
X[,,k] <- exp(-abs(Z)) # capture results
}
write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=900)

Programming with Big Data in R
Programming with Big Data in R (pbdR) is a series of R packages and an
environment for statistical computing with Big Data by using high-performance
statistical computation. The pbdR uses the same programming language as R
with S3/S4 classes and methods which is used among statisticians and data
miners for developing statistical software.
The significant difference between pbdR and R code is that pbdR mainly
focuses on distributed memory systems, where data are distributed across
several processors and analyzed in a batch mode, while communications
between processors are based on MPI that is easily used in large high-
performance computing (HPC) systems. R system mainly focuses on
single multi-core machines for data analysis via an interactive mode such as GUI
interface.

Big Data Strategies in R
• If Big Data has to be tackled with R, five different strategies can be considered:
1. Sampling: If data is too big to be analyzed in complete, its’ size can be reduced by sampling. Naturally, the question arises
whether sampling decreases the performance of a model significantly. Much data is of course always better than little data.
But according to Hadley Wickham’s useR! talk, sample based model building is acceptable, at least if the size of data crosses
the one billion record threshold.
2. Bigger Hardware: R keeps all objects in memory. This can become a problem if the data gets large. One of the easiest
ways to deal with Big Data in R is simply to increase the machine’s memory. Today, R can address 8 TB of RAM if it runs on 64-
bit machines. That is in many situations a sufficient improvement compared to about 2 GB addressable RAM on 32-bit
machines.
3. Store objects on hard disk and analyze it chunkwise: As an alternative, there are packages available
that avoid storing data in memory. Instead, objects are stored on hard disk and analyzed chunkwise. As a side effect, the
chunking also leads naturally to parallelization, if the algorithms allow parallel analysis of the chunks in principle. A downside
of this strategy is that only those algorithms (and R functions in general) can be performed that are explicitly designed to deal
with hard disk specific datatypes.
4. Integration of higher performing programming languages like C++ or Java: Small parts
of the program are moved from R to another language to avoid bottlenecks and performance expensive procedures.
The aim is to balance R’s more elegant way to deal with data on the one hand and the higher performance of other
languages on the other hand.
5. Alternative interpreters: A relatively new direction to deal with Big Data in R is to use alternative
interpreters. The first one that became popular to a bigger audience was pqR (pretty quick R). Duncon Murdoc from the
R-Core team preannounced that pqR’s suggestions for improvements shall be integrated into the core of R in one of the
next versions.

Applications of R Programming
• R applications span the universe from theoretical computational
statistics and the hard sciences such as astronomy, chemistry and
genomics to practical applications in business, drug development,
finance, health care, marketing, medicine and much more.
• Because R has nearly 5,000 packages (libraries of functions) many
of which are dedicated to specific applications you don’t have to be
an R genius to begin developing your own applications. All it takes to
get started is some modest experience with R, some examples to
emulate and the right libraries of R functions.

• Following are R’s application area
for which there are packages
containing the tools and functions
you need:
1. Clinical Trials
2. Cluster Analysis
3. Computational Physics
4. Differential Equations
5. Econometrics
6. Environmental Studies
7. Experimental Design
8. Finance
9. Genetics
10. Graphical Models
11. Graphics and Visualizations
12. High Performance Computing
13. High Throughput Genomics
14. Machine Learning
15. Medical Imaging
16. Meta Analysis
17. Multivariate Statistics
18. Natural Language Processing
19. Official Statistics
20. Optimization

Companies Using R
Social Search Awareness: Jesse Bridgewater works on
"social search awesomeness" for the Bing search engine, and is setting up his
dev environment with the necessary tools including python, vim, and R.
Facebook uses R for:
• Analysis of Facebook Status Updates
• Facebook's Social Network Graph
• Predicting Colleague Interactions with R

R is widely used at the FDA on a daily basis. FDA scientists
have written R packages for use by other scientists (within and outside the
FDA) involved in the submissions process.
Google uses R to: calculate ROI on advertising
campaigns; to predict economic activity; to analyze effectiveness of TV
ads and make online advertising more effective.
Other notable companies include-

What R is not so good at
• Statistics for non-statisticians
• Numerical methods, such as solving partial differential equations. (Try
Matlab)
• Analytical methods, such as algebraically integrating a function. (Try
Mathematica)
• Precision graphics, such as might be useful in psychology experiments. (Try
Matlab)
• Optimization
• Low-level, high-speed or critical code

Conclusion
A couple of years ago, R had the reputation of not being able to
handle Big Data at all – and it probably still has for users sticking on
other statistical software. But today, there are a number of quite
different Big Data approaches available. Which one fits best depends
on the specifics of the given problem. There is not one solution for all
problems. But there is some solution for any problem.
R is the best at what I does- letting experts quickly and easily
interpret, interact with, and visualize data.
R continues to help shape the future of statistical analysis, and
data science.

R programming presentation

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