IE6600 • Fall 2020 • Northeastern University • Boston

Zhenyuan Lu

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.

Course goals

Textbooks

R-related Materials

Policies

Please post questions, or discussion only via Piazza. The visibility of questions and discussion are expected to set for public view (to the Entire class on Piazza). Please feel free to send instructor/TAs emails regarding any personal or other private issues/concerns.

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

Accommodations for Students with Disabilities

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

Take care of yourself

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

If you have difficulty to keep up with any materials or homework for personal reasons please let me know early. If you or your friends/classmates who appears 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.

Projects

Course Evaluation

Schedule

Date	Lecture	Content	Logistics
Module 1: Introduction
9/11	Course introduction and expectations	Instructor's brain
9/15	Basic of R I	R4E "Basics of R" R4E "Advanced Data Structure"
9/18	Basic of R II	R4E "Basics of R" R4E "Advanced Data Structure"
9/22	R functions and the grammar of visualization I	R4DS Workflow Wickham A layered grammar of graphics (optional) R4E "Writing R functions"
9/25	R functions and the grammar of visualization II	R4DS Workflow Wickham A layered grammar of graphics (optional) R4E "Writing R functions"	HW1 out
Module 2: Basic Visualization and Data Engineering
9/29	R functions and the grammar of visualization II	R4DS Workflow Wickham A layered grammar of graphics (optional) R4E "Writing R functions"	Group arrangement due 9/28 @ 11:59pm
9/29, Last day to drop a full-semester fall class without a W grade
10/2	Data Visualization Concepts I
10/6	Guest Lecturer: Visualization and Color Design Data Visualization Concepts II Basic data visualization in R I Instructor: Guest Lecturer - Nan	R4DS Data Visualization with ggplot2 (optional) RGC "Quickly Exploring Data"	HW1 due 10/5 @ 11:59pm
10/9	Basic data visualization in R II	R4DS Data Visualization with ggplot2 (optional) RGC "Quickly Exploring Data"	HW2 out
10/13	Data transformation with dplyr I	R4DS Data Transformation with dplyr
10/16	Data transformation with dplyr II	R4DS Data Transformation with dplyr	HW2 due 10/15 @ 11:59pm HW3 out
10/20	Data wrangling with tibbles, readr and tidyr I	R4DS Tibbles with tibble R4DS Data Import with readr R4DS Tidy Data with tidyr
10/23	Data wrangling with tibbles, readr and tidyr II Optional: Data wrangling with stringr, forcats and lubridate	R4DS Tibbles with tibble R4DS Data Import with readr R4DS Tidy Data with tidyr (optional) R4DS "Strings with stringr", "Factors with forcats", "Dates and Times with lubridate"	Project proposal due 10/22 @ 11:59pm HW3 due 10/22 @ 11:59pm HW4 out
10/27	Visualizing relational data	R4DS Relational Data with dplyr
Module 3: Advanced Visualization
10/30	Introduction to Shiny interactive visualization web app I	R Shiny Shiny tutorial	HW4 due 10/29 @ 11:59pm HW5 out
11/3	Introduction to Shiny interactive visualization web app II	R Shiny Shiny tutorial
11/6	Introduction to Shiny interactive visualization web app III	R Shiny Shiny tutorial
11/10	Data analytics web apps with Shiny	R Shiny Shiny tutorial
11/13	Exploratory data analysis and more data visualization I	R4DS Exploratory Data Analysis	HW5 due 11/12 @ 11:59pm HW6 out
11/17	Exploratory data analysis and more data visualization II	R4DS Exploratory Data Analysis
11/20	Moved to 11/24		HW6 due 11/23 @ 11:59pm
11/24	Guest Lecturer: Find jobs in industry Additional topics and Workshop Instructor: Guest Lecturer - Jinhui Zhao, Qibin Tan, and Wenxin Liu
11/27	No classes - Thanksgiving
Project and Slides due Mon 11/30 @ 11:59pm
12/1, 12/4, 12/8, 12/11	Project presentation

Computation and Visualization for Analytics