PADM-GP 4505 - NYU Wagner Graduate School of Public …
PADM-GP 4505R Coding for Public PolicyFall 2018Instructor InformationScarlett Sijia Wang Email: wang.scarlett@nyu.eduOffice Hours: by appointmentCourse Prerequisites CORE-GP.1011.001 Statistical Methods for Public, Nonprofit, and Health ManagementCourse DescriptionR is one of the most widely used in a new generation of statistical packages that is widely used in public policy settings from data management, visualization, and analysis, among many other applications. It also finds wide use in data science and statistics. R is a powerful open source language and environment for statistical computing and graphics. This 7-week mini course leads the students into the R world, helps them master the basics and establishes a platform for future self-study. The course offers students basic programing knowledge and effective data analysis skills in R in the context of public policy-making and policy evaluation.Course and Learning ObjectivesStudents will learn how to install R and RStudio, understand and use R data objects, become familiar with base R and several statistical and graphing packages. The course will also teach students to develop their own R functions, which they can use, improve or adapt in the future. What You Will Take Away:Upon completion of the course, you will be able toInstall and set up R and RStudioUnderstand data objects and how they relate to policy analysisRead in, index and manipulate data objectsFind, install and use R packagesPlot simple graphicsCalculate parameters such as mean, median, sum and standard deviation.Conduct T-test, ANOVA and Chi-Squared test, read the summary outputs and find the P-values.Conduct linear and logistic regressions and interpret the outputsDevelop an effective R function for policy analysisLearning ResourcesSoftware:R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL , J. E. (2015). Political analysis using R. Springer. Retrieved from: , E. (2002). R for Beginners. Retrieved from: Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. " O'Reilly Media, Inc." Retrieved from: Venables, W. N. (2017). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Retrieved from: ClassesAll announcements, resources, and assignments will be delivered through the NYU Classes site. I may modify assignments, due dates, and other aspects of the course as we go through the term with advance notice provided as soon as possible through the course website.Academic IntegrityAcademic integrity is a vital component of Wagner and NYU. All students enrolled in this class are required to read and abide by Wagner’s Academic Code. All Wagner students have already read and signed the?Wagner Academic Oath. Plagiarism of any form will not be tolerated and students in this class are expected to?report violations to me.?If any student in this class is unsure about what is expected of you and how to abide by the academic code, you should consult with me.Henry and Lucy Moses Center for Students with Disabilities at NYUAcademic accommodations are available for students with disabilities. Please visit the Moses Center for Students with Disabilities (CSD) website and click on the Reasonable Accommodations and How to Register tab or call or email CSD at (212-998-4980 or mosescsd@nyu.edu) for information. Students who are requesting academic accommodations are strongly advised to reach out to the Moses Center as early as possible in the semester for assistance.NYU’s Calendar Policy on Religious HolidaysNYU’s Calendar Policy on Religious Holidays states that members of any religious group may, without penalty, absent themselves from classes when required in compliance with their religious obligations. Please notify me in advance of religious holidays that might coincide with exams to schedule mutually acceptable alternatives.Student ResourcesWagner offers many quantitative and writing resources as well as skills workshops. The library also offers a variety of data services to students. Here is a short list of the resources most relevant to learning R.Wagner Quantitative Resources:Tutoring ScheduleMath Review: Resources and In-Person SessionNYU Library Data ServicesQuantitative Analysis Guide: RConsultationClassesIntroduction to RData Management in RCreating Graphics with RAssessment Assignments and EvaluationThe Course Grade is based on the following:Participation: 10%5 Assignments: 75%Final Function:15%The instruction for each assignment will be released after the lecture. The assignment is due the following week. Points will be deducted from late submissions. Answer keys will be posted 24 hours after the due date. After answer keys are posted, assignments will not be accepted. Grading Scale and Rubric:Students will receive grades according to the following scale:There is no A+A = 4.0 pointsA- = 3.7 pointsB+ = 3.3 pointsB = 3.0 pointsB- = 2.7 pointsC+ = 2.3 pointsC = 2.0 pointsC- = 1.7 pointsThere are no D+/D/D-F (fail) = 0.0 pointsStudent grades will be assigned according to the following criteria:(A) Excellent: Exceptional work for a graduate student. Work at this level is unusually thorough, well-reasoned, creative, methodologically sophisticated, and well written. Work is of exceptional, professional quality.(A-) Very good: Very strong work for a graduate student. Work at this level shows signs of creativity, is thorough and well-reasoned, indicates strong understanding of appropriate methodological or analytical approaches, and meets professional standards.(B+) Good: Sound work for a graduate student; well-reasoned and thorough, methodologically sound. This is the graduate student grade that indicates the student has fully accomplished the basic objectives of the course.(B) Adequate: Competent work for a graduate student even though some weaknesses are evident. Demonstrates competency in the key course objectives but shows some indication that understanding of some important issues is less than complete. Methodological or analytical approaches used are adequate but student has not been thorough or has shown other weaknesses or limitations.(B-) Borderline: Weak work for a graduate student; meets the minimal expectations for a graduate student in the course. Understanding of salient issues is somewhat incomplete. Methodological or analytical work performed in the course is minimally adequate. Overall performance, if consistent in graduate courses, would not suffice to sustain graduate status in “good standing.”(C/-/+) Deficient: Inadequate work for a graduate student; does not meet the minimal expectations for a graduate student in the course. Work is inadequately developed or flawed by numerous errors and misunderstanding of important issues. Methodological or analytical work performed is weak and fails to demonstrate knowledge or technical competence expected of graduate students.(F) Fail: Work fails to meet even minimal expectations for course credit for a graduate student. Performance has been consistently weak in methodology and understanding, with serious limits in many areas. Weaknesses or limits are pervasive.Overview of the SemesterWeek 1Date: 9/10/18Topic: Introduction to R and RstudioDeliverable: Install R and RStudioWeek 2Date: 9/17/18Topic: Exploring data objects and PackagesDeliverable: Assignment 1 Week 3Date: 9/24/18Topic: Application I: Parameters and TestsDeliverable: Assignment 2Week 4Date: 10/1/18Topic: Graphics in RDeliverable: Assignment 3Week 5Date: 10/8/18Topic: Application II: Using SQL in R Deliverable: Assignment 4Week 6Date: 10/15/18Topic: Developing an R Function IDeliverable: Assignment 5Week 7Date: 10/22/18Topic: Developing an R Function IIDeliverable: Written FunctionsDetailed Course OverviewWEEK 1: INTRODUCTION TO R AND RSTUDIOReadings: Paradis, E. (2002). R for Beginners (Chapter 1. Preamble and Chapter 2. A Few Concepts before Starting. Monogan, J. E. (2015). Political analysis using R. (Chapter 1. Obtaining and Downloading Packages)Extra Resources:Up and Running with R (a 2.5-hour introductory tutorial to R) URL: , W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 1. Introduction to Preliminaries. Retrieved from: WEEK 2: EXPLORING DATA OBJECTS AND PACKAGES IN RReadings: John Blischak, Daniel Chen, Harriet Dashnow, and Denis Haine (eds):"Software Carpentry: Programming with R." 13. Data Types and Structures. Version 2016.06, June 2016, Paradis, E. (2002). R for Beginners (Chapter 3.1. Data with R – Objects and 5.4 Statistical Analysis with R – Packages)Monogan, J. E. (2015). Political analysis using R. (Chapter 1. Obtaining and Downloading Packages, Chapter2. Loading and Manipulating Data and Chapter 8. Using Packages to Apply Advanced Models)Assignment due: Assignment 1Extra Resources: Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. Chapter 5 Data Structures O'Reilly Media, Inc.Venables, W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 2. Simple Manipulations; Numbers and Vectors. Retrieved from: WEEK 3: APPLICATION I: PARAMETERS AND STATISTICAL TESTSReadings: Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. Chapter 11. Linear Regression and ANOVA" O'Reilly Media, Inc.".Paradis, E. (2002). R for Beginners (Chapter 5. Statistical Analyses with R)Monogan, J. E. (2015). Political analysis using R. (Chapter 6. Linear Models and Regression Diagnostics and Chapter 7. Generalized Linear Models)Assignment due: Assignment 2Extra Resources: John Blischak, Daniel Chen, Harriet Dashnow, and Denis Haine (eds):"Software Carpentry: Programming with R." Version 2016.06, June 2016, , W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 11. Statistical Models in R. Retrieved from: 4: GRAPHICS IN RReadings: Paradis, E. (2002). R for Beginners (Chapter 4. Graphics with R)Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. Chapter 10. Graphics O'Reilly Media, Inc.Monogan, J. E. (2015). Political analysis using R. (Chapter 3. Visualizing Data)Assignment due: Assignment 3Extra Resources: Chang, W. (2012). R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly Media, Inc.Venables, W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 12. Graphical Procedures. Retrieved from: WEEK 5: APPLICATION II: USING SQL IN RReadings: Grothendieck, G. (2017). Manipulate R Data Frames Using SQL. Retrieved from , J. (2011) SQL Pocket Guide: a Guide to SQL Usage. Retrieved from due: Assignment 4Extra Resources: Torgerson, D. Introduction to Data Analytics for Business – 1. Introduction to SQL. University of Colorado Boulder. Online Course. Retrieved from: 6: DEVELOPING AN R FUNCTION IReadings: Venables, W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 10. Writing Your Own Functions. Retrieved from: John Blischak, Daniel Chen, Harriet Dashnow, and Denis Haine (eds):"Software Carpentry: Programming with R." Chapter 2. Creating Functions Version 2016.06, June 2016, . Paradis, E. (2002). R for Beginners (Chapter 6. Programming with R in Practice.)Assignment due: Assignment 5Extra Resources: Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. Chapter 2.12 Defining a Function O'Reilly Media, Inc.WEEK 7: DEVELOPING AN R FUNCTION II Readings: Venables, W. N. (2006). An Introduction to R Notes on R: A Programming Environment for Data Analysis and Graphics. Chapter 10. Writing Your Own Functions. Retrieved from: John Blischak, Daniel Chen, Harriet Dashnow, and Denis Haine (eds):"Software Carpentry: Programming with R." Chapter 2. Creating Functions Version 2016.06, June 2016, . Paradis, E. (2002). R for Beginners (Chapter 6. Programming with R in Practice.)Assignment due: Written FunctionExtra Resources: Teetor, P. (2011). R Cookbook: Proven recipes for data analysis, statistics, and graphics. Chapter 2.12 Defining a Function O'Reilly Media, Inc ................
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