IE6600 Spring 2022 Northeastern University • Seattle Past

Computation and Visualization for Analytics

r shiny tableau

This course covers basic of the R, and R Shiny for data preprocessing, and visualization. It introduces students to static and interactive visualization, dashboard, and platform that reveal information, patterns, interactions, and comparisons by paying attention to details such as color encoding, a shape selection, spatial layout, and annotation. Based on these fundamentals of analytical and creative thinking, the course then focuses on data visualization techniques and the use of the most current popular software tools that support data exploration, analytics-based storytelling and knowledge discovery, and decision-making in engineering, healthcare operations, manufacturing, and related applications.

Mon, Wed 11:00am – 14:00am ET

01/19/2022 – 04/27/2022

Room 416, 225 Terry Ave, Seattle

4 Credits

Upcoming Deadlines

No upcoming deadlines at this time

Course Platform

Canvas

Course materials and grades

Piazza

Course discussions

Course Materials

R For Data Science (R4DS)

Required

Author: Wickham, Hadley, and Garrett Grolemund Url

Shiny Tutorial

Additional

RStudio Shiny Tutorial Url

Tableau Public Knowledge Base

Additional

Tableau Public Knowledge Base Url

R For Everyone (RFE)

Additional

Author: Lander, Jared P. Url

R Markdown (RMD)

Recommended

Author: Xie, Yihui, et al. Url

R Graphics Cookbook (RGC)

Additional

Author: Chang, Winston Url

Advanced R (ADR)

Recommended

Author: Wickham, Hadley Url

R Packages (RPK)

Additional

Author: Wickham, Hadley Url

Text Mining with R (TM)

Recommended

Authors: Silge, Julia, and David Robinson Url

Teaching Staff

Zhenyuan Lu, PhD

Instructor

Deep Learning Scientist and AI/ML Adjunct Faculty

Zoom

Office Hours: Mon 9:00 AM - 10:00 AM PT

Kushal Upadhyay

Teaching Assistant

Zoom

Office Hours: Thur 10:00am - 11:00am

Course Schedule

Date	Topic	Materials	Assignments
Module 1: Introduction
Jan 18	Course introduction and expectations	Instructor's Brain Text
Jan 25	Basic of R	T02: Basic R I Slides T02: Intro R I Slides R4E: Basics of R Text R4E: Advanced Data Structure Text
Reminder: 01/31 - Last Day to Drop a Class Without W Grade
Feb 1	R functions and the grammar of visualization	T03: R Grammar Slides R4DS: Workflow Link Wickham: A layered grammar of graphics Paper (optional) R4E: Writing R functions Text
Module 2: Basic Visualization and Data Engineering
Feb 8	Data Visualization Concepts		HW1 Released Group Arrangement Due Feb 7 @23:59 PT
Feb 15	Basic data visualization in R	T05: Data Viz Slides R4DS: Data Visualization with ggplot2 Link (optional) RGC: Quickly Exploring Data Text	HW2 Released HW1 Due Feb 14 @23:59 PT
Feb 22	Data transformation with dplyr	T06: Data Transformation with dplyr Slides R4DS: Data Transformation with dplyr Link	HW3 Released HW2 Due Feb 21 @23:59 PT
Mar 1	Data wrangling with tibbles, readr and tidyr	T07.1: Data Wrangling Slides T07.2: Data Wrangling Slides R4DS: Tibbles with tibble Link R4DS: Data Import with readr Link R4DS: Tidy Data with tidyr Link (optional) R4DS: Strings with stringr, Factors with forcats Text	HW4 Released HW3 Due Feb 28 @23:59 PT
Mar 8	Visualizing relational data	T08: Visualizing Relational Data Slides R4DS: Relational Data with dplyr Link	HW4 Due Mar 7 @23:59 PT
Module 3: Advanced Visualization
Mar 15	No classes - Spring Break
Mar 22	Introduction to Shiny interactive visualization web app I	R Shiny: Shiny tutorial Link	HW5 Released Project Proposal Due Mar 21 @23:59 PT
Mar 29	Introduction to Shiny interactive visualization web app II	R Shiny: Shiny tutorial Link
Apr 5	Exploratory data analysis and more data visualization	R4DS: Exploratory Data Analysis Link	HW6 Released HW5 Due Apr 4 @23:59 PT
Apr 12	Additional R Workshop - Visualization by PCA	James, Gareth, et al. An Introduction to Statistical Learning, with Applications in R Link Kilian Weinberger: PCA for Visualization Link	HW6 Due Apr 11 @23:59 PT
Apr 19	Project presentation
Happy Summer!

Polices

Plagiarism, cheating, and any form of unauthorized collaboration will not be tolerated and will be handled in accordance with University policies described in the Student Handbook. For additional information on Northeastern University’s Academic Integrity Policy.

Course Goals

R For Data Science, Wickham, Hadley, and Garrett Grolemund

Accommodations for Students with Disabilities

If you have a disability, I encourage you to contact the Disability Resource Center to register and request accommodations. Also, please discuss your needs with me as early in the semester as possible.

Taking Care of Yourself

Eating healthy food, having regular exercise, avoiding alcohol and drugs, getting adequate sleep, and taking time to relax will help you achieve your goals and manage stress.

If you have difficulty keeping up with any materials or homework for personal reasons, please let me know early. If you or your friends/classmates appear to be struggling or having trouble coping with stress, we strongly encourage you to seek support at the We Care program at NEU. At Northeastern, a student is never alone when struggling with a demanding situation.

Homework

There are 6 individual homework assignments. Due day will be posted with the homework. Late submission would not be accepted. Please let me know 72 hours in advance before the due day if you need extensions with a reasonable justification. Requests for regrades in writing will only be accepted no less than 10 days after receiving grade. Please send the instructor your NUID, and name with title “Request for regrade: HW+number” via email. The new grade may be lower than the original one.

Please feel free to refer to any materials from my slides. You may discuss homework with your classmates, but all the assignments are supposed to completed by your own. Sharing of completed solutions will not be tolerated. Plagiarism will be considered, if solutions and project documentations with a very high degree of similarity with other student’s or materials online. Such academic dishonesty will be handled in accordance with university policies.