FE515 Introduction to R

Course Catalog Description


This course is designed for graduate students. Starting from 2018 fall semester, this course is extended to 2 hours each week. Upon completion the students will gain an understanding of the programming syntax and should be able to use R in any future courses.

Campus Fall Spring Summer
On Campus X X
Web Campus


Professor Email Office
Ziwen Ye
zye2@stevens.edu Altorfer 301

More Information

Course Resources


Lecture Notes and Code

The art of R programming: a tour of statistical software design. Norman Matloff, First Edition, 2011. ISBN-10: 1593273843, ISBN-13: 978-1593273842

An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN-10: 0470890819, ISBN-13: 978-0470890813

Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN-13: 978-1464158933, ISBN-10: 1464158932

Additional References

CRAN: http://www.wikibooks.org

R-help Info: https://stat.ethz.ch/mailman/listinfo/r-help

R-help Archive: http://r.789695.n4.nabble.com

Quick R: http://www.statmethods.net


Grading Policies

The plan is to schedule 5 assignments for this semester. The assignments will due exactly before the next class. All LATE SUBMISSION will be punished unless you send me an email BEFORE DUE and get approved. If your submission passes the due for less than 24 hours, your highest score will be 67%; between 24 and 48 hours, your highest score will be 33%; after 48 hours this assignment will be graded as 0. If the assignments I give out is more than 5, the lowest grade will be dropped in final grading calculation.

For this course, all students will have the midterm and final exams. Both exams are 2 hours length and will be held during the class. As a coding class, we only test the coding skill from students. Therefore, both exams will be open book. Students can use any materials during exams (such as notes, Google search engine and etc.) to help them answer all questions. However, any communication tools (such as Skype, email and etc.) and tutoring websites are NOT allowed.

If students have any concern or questions regarding to the teaching contents and homework, they are encouraged to seek help from the instructor. Discussing homework with classmates are prohibited for this course. All code and reports must be written by yourself. Copying solutions from sources other than your brain is strictly forbidden. This kind of behavior will be considered as academic dishonesty/misconduct and will be dealt with according to the Stevens honor board policy.

  • 30% Assignments
  • 30% Midterm
  • 40% Final

Bonus – TBD (Bonus includes but not limited to attendance and bonus questions)

Lecture Outline

Topic Reading
Week 1 R basics(1)
Data structures & Loops
Week 2 R basics(2)
Self-defined functions ”apply” functions
Week 3 R basics(3)
Generating random variables Discreet distribution & Sampling
Week 4 Date and time objects Simple return and compounded return Plots A2
Week 5 Data downloading packages in R: rblpapi & quantmod
Week 6 Basic statistics and time series A3
Week 7 Linear regression models Stepwise selection & goodness criteria
Week 8 On campus Midterm (10 am to 12 pm)
Week 9 T-test and ANOVA Intro. to machine learning (Tentative)
Week 10 Bisection method, Newton’s method and gradient descent
Week 11 Realized Volatility & Implied Volatility
GBM and BS Model
Week 12 GGplot
Week 13 Rmarkdown and Basic LaTeX Transition from R to Python (Tentative) Review session A6
Week 14 Advanced Topic (Tentative): High-frequency data analysis
Week 15 On-campus Final exam (10 am to 12 pm)