The aim of this course is to teach you to master one of the most powerful and popular tools in social science.
The goal of this class is not to teach you a particular statistical method, but to teach you how to explore and present your data. Classes will be split between lecture, discussion, and lots of hands-on practice. In the lecture and discussion, we will walk through the logic of each weeks material. In the practicum, we will work on exercises. The course is therefore useful for those who hope or expect to be a quantitative analyst, inside or outside of academia.
R_wav is both theoretical and practically oriented. In each session, we will discuss the readings and concept for that week, focusing on a particular aspect of data science, whether it is the philosophy of visualization, exploratory data analysis, or the reproducible research.
There are no required books for this course, but there are many useful books out there:
R Cookbook by Paul Teeter
Advanced R by Hadley Wickham
The Art of R Programming by Norman Matloff
The weekly problem sets (30 percent), your presentation (10 percent) and an individual final assignment (60 percent) will determine your grade for the course. The problem sets will consist of questions related to that weeks material. Problem sets will be assigned by Friday and be due the following Tuesday.
In the the final project and presentation you will apply wrangling, analysis, and visualization to a dataset of your choosing. More details to come. You are encouraged to help each other during the practicum, but problem sets are individual assignments.
As always, you need a 6 to pass the course. If your final mark is below 5.5, you will have to redo the assignment (you cannot retake the presentation). Assignment retakes can earn a maximum of 6.
Participation As will be obvious from the above, participation in the meetings is crucial to the success of this course – possibly more so than in other courses. You may miss one class. Additional absences must always be discussed with me, in advance. Repeated absence will mean you will fail the course.
Communication Additional information and emails will be sent through the github site, so make sure you keep an eye on that and on your student email. For direct communication or if you'd like to make an appointment with me, let me know during or after class, or contact me by email. My contact details are:
Yphtach Lelkes REC-C Level 8 Email: [email protected]
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Week 1
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Session 1 * Slides * Course Introduction * Data hygiene * Installing R and Rstudio * Libraries, packages * Readings: * Code and Data for the Social Sciences: A Practitioners Guide, pp 1-44(Gentzkow and Shapiro) * An Introduction to R, Chapter 1
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Session 2
- Slides
- Introduction to R objects
- Using R for arithmetic
- Getting data into R
- Readings
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Week 2
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Week 3
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Week4
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Week 5
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Week 6
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Session 1
- Latex, Beamer, Knitr
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Session 2
- Maps in ggplot, maybe github
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Week 7 NO CLASS
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Week 8
- Session 1
- Presentations
- Session 2
- Presentations
- Session 1