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Lecture 1: Programming with R


Renuka Sane

July 29, 2015
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The kinds of questions economists ask?

Has the mid-day meal scheme improved school attendance?

Does increase in police presence lead to a reduction in crime rate?Did the ban in commissions to mutual funds lead to a reduction in fundflows?

Do minimum wages cause unemployment?

Do FII inflows increase stock price volatility?
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Components of writing such papers

HypothesisEconometric model

Data!
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Statistical packages

Users supply data

Run pre-defined routines. Example regress Y XThe problems:

It is non-trivial to get Y and X in the same data-setOur work is actually 90% data handling & graphs and 10% estimation.What if the routine you want is not part of the package?

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Data handing example: MG-NREGA

L t 1 P i ith R
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Data handing example: Trading data on NIFTY

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Data handling example: Many files to be put in one

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The elements for a computational toolkit

Price

Freedom

Computer science

Network effects

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The three main alternatives

System Price Freedom CS Network

SAS Very high Zero Bad SmallStata High Zero Better than SAS High among

but not great economistsR Free Free Great Slowly growing

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Part I

R

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g g

The origins of S

The predecessor of R wasS. This was done at Bell Labs, and is a child of theUnix philosophy.

1970s Initial implementation (Fortran, mostly for internal use)

1980s Unix version, wider distribution in academia. New-S.

1990s Statistical modeling language. Licensing (S-PLUS). Addition offormal object-oriented programming.

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ACM Software System Award

John Chambers, awarded the Software Systems Award in 1998.For the S system, which has forever altered how people analyze, visualise,and manipulate data

http://awards.acm.org/software_system/year.cfm

The hall of fame includes:

1983 Unix1986 TeX1995 World-Wide Web

2002 Java

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From there we have R

R is the free S.

Started as a teaching tool by Robert Gentleman and Ross Ihaka at theUniversity of Auckland, around 1993.

Released as Free software around 1995

Version 1.0 released in 2000.

R is now the dominant statistics software in the world.

R is a GNU project.

R is available as Free Software under the terms of the Free SoftwareFoundations GNU General Public License in source code form.

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From there we have R

R is the free S.

Started as a teaching tool by Robert Gentleman and Ross Ihaka at theUniversity of Auckland, around 1993.

Released as Free software around 1995

Version 1.0 released in 2000.

R is now the dominant statistics software in the world.

R is a GNU project.

R is available as Free Software under the terms of the Free SoftwareFoundations GNU General Public License in source code form.

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A digression on free software

Free as in free speech, and not free beer

The freedom to run the program, for any purpose

The freedom to study how the program works, and adapt it to your needs.

The freedom to redistribute copies so that you can help your neighbour

The freedom to improve the program, and release your improvements tothe public, so that the whole community benefits

Free software can be commercial software

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Why open-source?

Do not need a license.Do not need a department.

Reproducible research.



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R is a programming language

Designed for interactive use

With a focus on data analysisBasic data structures are vectorsLarge collection of statistical functionsAdvanced statistical graphics capabilities

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R in the real world

.. It is becoming their lingua franca partly because data mining has entered a

golden age, whether being used to set ad prices, find new drugs more quickly orfine-tune financial models. Companies as diverse as Google, Pfizer, Merck,

Bank of America, the InterContinental Hotels Group and Shell use it.

Source: New York Times, 7 January, 2009

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Where do you get R?

http://www.r-project.org/

http://www.rstudio.com/

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Installing R

R can be installed on Windows, Mac or Linux.

Homework: Visit the R website and follow the installation directions. You

will want to install the base system.There are several additional user contributed add-on packages.

To install a package, be connected to the internet and type >install.packages("plm") You will be asked to select the mirror sitenearest to you. After that everything is automatic.

Load the package before using it. >

library(plm)

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Preferred operating system

Ubuntu on Linux: http://www.ubuntu.com/download

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Part II

Programming for projects

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Reusability of code

Dont design a chainsaw for only an oak treeThere might be a million species of trees

But they are all trees

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Automation: writing and coding

Frequent changes to the data-set

Frequent changes to the statistics we want reported

Embed coding in the writing of your paper

Tools: LaTeX, R, knitr.

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Writing code is like writing a proof

Each step follows the otherYou cannot jump!

Many days later you may not remember how you got there after all

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Part III

This course

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Goals

Learn R as a statistics toolbox with a strong emphasis on programminglanguage aspects

Learn automation using R, LaTeX and knitr



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Broad syllabus

Data manipulation in R: objects; tables and cross-classifications; array andmatrix operations

Writing functions

Graphics: Basic plots; lattice; ggplots

Univariate statistics: generating random data; univariate statistics;bootstrap and permutation methods

High performance computing: Introduction to high performance

computing



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Grading

Examination Marks

Class tests 20Midterm exam 40Final exam 40

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Resources

The R Manuals: www.r-project.org

Introductory Statistics with R, by Peter DalgaardModern Applied Statistics with S, by W. N. Venables and B. D. Ripley

Tutorials on the web: http://www.r-bloggers.com/google-developers-r-programming-video-lectures/



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Consultation

Tuesdays: 15:30-17:00, Room 204Email: [email protected]
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