QF104 Data Management in R

Course Catalog Description


The course provides an introduction to R, with a focus on how to use R to manage data through the data life cycle.

Campus Fall Spring Summer
On Campus X X
Web Campus X X


Professor Email Office
Mohamad Afkhami mafkhami@stevens.edu
Olorundamilola `Dami' Kazeem
okazeem@stevens.edu Babbio 109

More Information

Course Outcomes

After successful completion of this course, students will have solid foundation in R and on how to use R to:

  • import data
  • tidy data
  • transform data
  • visualize data
  • model data
  • communicate data

Additionally, students will be able to apply R, which is one of many tools, to solve data-related problems they may encounter beyond this course.

Course Resources


G. Grolemund and H. Wickham,\R for Data Science", https://r4ds.had.co.nz/

Additional Resources

W. N. Venables, D. M. Smith, and the R Core Team,\An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.5.2, 2018-12-20", https://cran.r-project.org/

The R Project for Statistical Computing, https://www.r-project.org/

R-Bloggers,", https://www.r-bloggers.com/


Course Requirements

Homework Assignments & Final Project: Students are required to submit their respective homework assignments via Canvas. No other form of submission will be accepted.

Attendance & Participation: Students are required to attend all lectures to complete the 12 In-Class Drills and 2 Pop-Up Quizzes. Students are required to submit their respective homework assignments via Canvas. No other form of submission will be accepted.

Grading Policies

Grading will be based upon your attendance, in-class drills, homework assignments, pop-up quizzes, and final project. The percentage breakout of these components is indicated below. An average score of 60% or above will indicate a passing grade, and below 60% will be a failing grade.

5 Homework Assignments - 50%

1 Final Project - 20%

12 In-Class Drills - 20%

2 Pop-Up Quizzes - 10%

Late Policy: All late submissions will be punished unless prior notice is given before the due date and it is approved. If your submission passes the due date for less than 24 hours, your highest score will be 67 %; between 24 and 48 hours, your highest score will be 33 %; and after 48 hours your submission will not be accepted. If outside circumstances are a ecting your ability to perform in the course, you must contact the instructor before you fall behind in the course.

Lecture Outline

Week Topic Reading
  • Syllabus Review
  • Course Introduction
  • RMarkdown
  • Workflow Basics - r4ds Ch. 4
  • R Basics
3 Data Visualization - r4ds Ch. 3 Sec. 1-4 and 7
4 Data Transformation - r4ds Ch. 5 1-4
5 Data Transformation - r4ds Ch. 5 5-7
6 Data Import - Part I
7 Data Import - Part II
8 Tidying Data
9 See You After the Break!
  • Strings
  • Factors
11 Date and times
12 Writing Functions - Part I
13 No Class
14 Writing Functions - Part II
  • Packages in R
  • Working with Quantmod
16 Review
17 No Class
18 No Class
19 Correlation and Regression
20 Lab Tutorial
21 Lab Assignment 2
22 Class Exam 2
23 Project Data Analysis
24 Project Data Analysis
25 Final Presentations
26 Final Presentations