Introduction to the R Programming for Data Science

• Some background on R, how to obtain and how to install it
• R documentation and available help and web resources
• R Console and R GUI
• Data import, data types and variables
• Elementary import functions
• Data types
• Variable generation, inspection and modification
• Graphics in R for Data Science
• Command “plot” and where to plot to
• Setting the setting: par
• High-level and low-level plotting functions legends
• Math in plots
• Hands-on Session—data handling & graphics
• Simulations in R for Data Analytics
• Simulating from given distributions
• Risk management basics
• Data Structures in R
• Review of types of objects
• Additional object types
• Basics of the R Language
• Conditionals & Loops
• Writing R Functions:  Basic functions
• Object-orientation
• Debugging
• Application in Finance
• Calculating Value at Risk
• Packages for Portfolio Optimization

Divisions:

Course Description:

One on One course Introductory course for R. 4 hours of basics of R to get you started in R. Flexible timings with personalized approach by Experienced Faculty. Online tests to prepare yourself better. This course will help you in:

• Add to the profile by learning R.
• Provides a base for learning advanced R.
• Gives you a perfect platform for launching a career in R.
• Gain the knowledge of  integration with excel  .

Course Structure:

The Benefits of Using R :

• R is free, open-source code
• R programming helps you runs R anywhere
• R supports extensions
• R provides an engaged community
• R connects with other languages

Tutorials contributed by learners and tutors-

Other Details –

Some links to get yourself acquainted on R:

http://www.unicom.co.uk/quant-finance/R-for-Finance.php

http://www.optirisk-systems.com/papers/FinancewithR.pdf

http://tradeblotter.files.wordpress.com/2012/06/uw_r_seminar_may_2012.pdf

Functions of R / R Commands to be covered in the course:

Caution:

Always save workspace

Use “c” when you want to use it a signal in black schools formula

 read.table function will be used to get the data into R (/\ used opposite than the one used in windows)

http://www.ats.ucla.edu/stat/r/faq/inputdata_R.htm

Example 1

> test.txt <- read.table (“C:/Documents and Settings/shivbhakta.joshi/My

Documents/test.txt”, header=T)

> print(test.txt)

> column <- test.txt[,c(‘make’,’price’)]

Example 2

grb <- read.table (“C:/Documents and Settings/shivbhakta.joshi/My

Documents/GRB_afterglow.dat”, header=T, skip=1)

x <- (grb[,1])

Grabbing data from tables into an single dimensional arrayx <- (grb[,1])

y <- log(grb[,1])

Another way to create returns for stock daily return is

f=dft$age/dft$price (using dollar sign extraction)

Deleting column http://www.sr.bham.ac.uk/~ajrs/R/r-manipulate_data.html

> add <- add[-2] to delete the element 2nd

Adding data to array busing column command

> add <- c(1,d)

Using Data frames

Data.frame is used to combine the arrays back into table format

people = data.frame (x,y)

Write.CSV command executed

write.csv(people,”filename.csv”)

grb <- read.table (“C:/Documents and Settings/satyadhar.joshi/My

Documents/GRB_afterglow.dat”, header=T, skip=1)

x <- (grb[,1])

y <- log(grb[,1])

people = data.frame (x,y)

write.csv(people,”filename.csv”)

These methods can be used to compute beta from columns of a csv file

Working formula of Blach Scholes

BS <-

function(S, K, T, r, sig, type=”C”){

d1 <- (log(S/K) + (r + sig^2/2)*T) / (sig*sqrt(T))

d2 <- d1 – sig*sqrt(T)

if(type==”C”){

value <- S*pnorm(d1) – K*exp(-r*T)*pnorm(d2)

}

if(type==”P”){

value <- K*exp(-r*T)*pnorm(-d2) – S*pnorm(-d1)

}

return(value)

}

blackscholes <- function(s, k, r=.1, t=5, sigma=.9,call=TRUE) {

#calculate call/put option

d1 <- (log(s/k)+(r+sigma^2/2)*t)/(sigma*sqrt(t))

d2 <- d1 – sigma * sqrt(t)

ifelse(call==TRUE,s*pnorm(d1) – k*exp(-r*t)*pnorm(d2),k*exp(-r*t)

* pnorm(-d2) – s*pnorm(-d1))

}

Predefined Black schools model:

http://shafik.net/~shafik/FinancialEngineering/Code/

black_scholes (“c”,5600,5600,.1,.08,.22 )

this depicts use of functions in R

Merton Distance to Default (different as per needs)

(modified from Black Scholes formula, still

unsure about pnorm() function)

d1 <- (log(s/k)+(sigma^2/2))/(sigma)

pnorm(d1)

3 files needed to run the demos are available on internet by various authors:

References:

http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf

R code for Breaking X/Y axis

One on One Customized Training:

qcfinance.in believes in personalized touch so that our clients are completely satisfied with our service. In this regard, we offer One on One Customized Training to our clients.

These Training are provided on requests by our clients & are customized according to their individual needs at various cities.

Tutors and Volunteers available in Manhattan, New York.

The course structure & timings for these training are highly flexible, classes are scheduled as per the convenience of our clients.

 R programming tutors in Manhattan New York for Data Science 

Note: If you miss a scheduled one on one class, then don’t worry, you can reschedule it at your convenient time & the missed class won’t be counted. You may defer a class any number of time. Also, you get life time access to materials which gets constantly updated.

Course Instructor:

Contact Us: info@learnprogrammingnyc.com

Depreciated: COURSE LINK – Introduction to R