IE6600 Summer 2021 Northeastern University • Boston 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 01:30am – 15:10am (ET)

05/10/2021 – 08/18/2021

Room 129, Forsyth Building, Boston

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: Tue 10:00am to 11:00am ET

Lekha Cheruku

Teaching Assistant

Zoom

Office Hours: Thur 9:00am to 10:00am

Course Schedule

Date	Topic	Materials	Assignments
Module 1: Introduction
May 10	Course introduction and expectations	Instructor's Brain
May 12	Basics of R I	R4E Basics of R R4E Advanced Data Structure
May 17	Basics of R II	R4E Basics of R R4E Advanced Data Structure
May 19	R functions and the grammar of visualization I	R4DS Workflow Link Wickham A layered grammar of graphics Paper (optional) R4E Writing R functions
May 24	No classes
May 26	R functions and the grammar of visualization II	R4DS Workflow Link Wickham A layered grammar of graphics Paper (optional) R4E Writing R functions
Reminder: 5/30 Last day to drop a class w/o a W grade
May 31	No classes - Memorial Day
Module 2: Basic Visualization and Data Engineering
Jun 2	Data Visualization Concepts I		HW1 released Group arrangement due Due Jun 1 @23:59 ET
Jun 7	Data Visualization Concepts II Basic data visualization in R I	R4DS Data Visualization with ggplot2 Link (optional) RGC Quickly Exploring Data
Jun 9	Basic data visualization in R II	R4DS Data Visualization with ggplot2 Link (optional) RGC Quickly Exploring Data	HW2 released HW1 Due Jun 8 @23:59 ET
Jun 14	Data transformation with dplyr I	R4DS Data Transformation with dplyr Link
Jun 16	Data transformation with dplyr II	R4DS Data Transformation with dplyr Link	HW3 released HW2 Due Jun 15 @23:59 ET
Jun 21	Data wrangling with tibbles, readr and tidyr I	R4DS Tibbles with tibble Link R4DS Data Import with readr Link R4DS Tidy Data with tidyr Link
Jun 23	Data wrangling with tibbles, readr and tidyr II (Optional): Data wrangling with stringr, forcats, and lubridate	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, Dates with lubridate Link	HW4 released HW3 Due Jun 22 @23:59 ET
Jun 28	Visualizing relational data	R4DS Relational Data with dplyr Link	Project proposal Due Jun 28 @23:59 ET
Jun 30	Introduction to Shiny interactive visualization web app I	R Shiny Shiny tutorial Link	HW5 released HW4 Due Jun 29 @23:59 ET
Jul 5	No classes - Independence Day Weekend
Module 3: Advanced Visualization
Jul 7	Introduction to Shiny interactive visualization web app II	R Shiny Shiny tutorial Link
Jul 12	Introduction to Shiny interactive visualization web app III	R Shiny Shiny tutorial Link
Jul 14	Data analytics web apps with Shiny	R Shiny Shiny tutorial Link
Jul 19	Exploratory data analysis and more data visualization I	R4DS Exploratory Data Analysis Link	HW6 released HW5 Due Jul 18 @23:59 ET
Jul 21	Exploratory data analysis and more data visualization II	R4DS Exploratory Data Analysis Link
Jul 26	Additional R Workshop
Jul 28	Preparing for the project presentation
Aug 2	Preparing for the project presentation
Reminder: Project and Slides due 08/03 @ 11:59pm ET
Aug 4	Project presentation
Aug 9	Project presentation
Aug 11	Project presentation
Module: Happy Holidays!

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.