326_212-2019fall

326.212 Statistical Computing and Labs @ SNU

This is the course website for 326.212: “Statistical Computing and Labs” at Seoul National University in Fall 2019. Assignments, lecture notes, and open source code will all be available on this website.

Announcements

2018-11-20: Final project Q2-1 has been changed. Please check this link.
2018-11-20: Homework 4 typos are fixed now. Please check this link.
2018-11-15: Homework 4 is posted.
2018-10-27: Homework 3 is posted.
2018-10-21: Final project has been posted. Please check this link.
2018-10-09: Homework 2 has been updated with some clarifications. Please check this link.
2018-09-23: Homework 2 is posted.
2018-09-02: This is section where future announcement will be posted.

Instructor

Joong-Ho (Johann) Won

Email: wonj AT stats DOT snu DOT ac DOT kr

Class Time: Mondays 13:00 - 14:50 in 28-101 (lecture); Wednesdays 13:00 - 14:50 in 26-102 (lab),

Office Hours: By appointment.

Textbook: R for Data Science by Hadley Wickham and Garret Grolemund

References: Advanced R (Korean translation); The Art of R Programming (Korean translation)

Syllabus: Link, in Korean

Course Objectives

By the end of this course, you will be able to

acquire basic programming skills using the R programming language;
proficiently wrangle, manipulate, and explore data using R;
utilize contemporary R packages, especially the tidyverse;
visualize, present, and communicate trends in a variety of data types;
formulate data-driven hypotheses using exploratory data analysis and introductory model building techniques.

Course Overview

Assessment

The course will be graded based on the following components:

Attendance (10%): Includes both the lecture and the lab sessions.
Assignments (30%): You will be assigned a computational assignment to be completed using RStudio and the package knitr regularly throughout class.
Quizzes (20%): Unscheduled quizzes will be frequently given throughout the class during the lab sessions.
Final project (40%): The project will be a computational case study that brings together the techniques learned throughout the semester. The description of the project will be provided towards the midpoint of the semester.

Schedule

Overall, this course will be split into two main parts: (1) lecture sessions on the basics of how to code in R and (2) lab sessions for performing hands-on data analysis on real case studies and examples using R.

The following schedule is tentative, and is subject to change over the course.

Week	Topic	Reading	Assignment	Due Date
1 (9/2, 9/4)	Introduction, Data Visualization	Chs. 1, 2, 3, 28	Homework 1	9/22/2019
2 (9/9, 9/11)	Workflows, R Markdown	Chs. 4, 6, 8, 27
3 (9/16, 9/18)	Data Transformation	Ch. 5
4 (9/23, 9/25)	Exploratory Data Analysis I	Ch. 7	Homework 2	10/13/2019
5 (9/30, 10/2)	Exploratory Data Analysis II, Import and Tidy Data I	Chs. 7, 10, 11, 12
6 (10/7)	Import and Tidy Data II	Chs. 10, 11, 12
7 (10/15, 10/17)	Relational Data, Strings	Ch. 13	Final Project	12/11/2019
8 (10/21, 10/23)	Strings, Factors, Date and Times	Chs. 14, 15, 16	Homework 3	11/10/2019
9 (10/28, 10/30)	Vectors	Chs. 20
10 (11/4, 11/6)	Functions I	Ch. 19
11 (11/11, 11/13)	Functions II, Pipes	Chs. 19, 18	Homework 4	12/1/2019
12 (11/18, 11/20)	Iteration	Ch. 20
13 (11/25, 11/27)	Model Basics	Ch. 23
14 (12/2, 12/4)	Model Building	Ch. 24
15 (12/9, 12/11)	Final Project

Acknowledgment

Lecture notes for this course were arranged from the source code of the textbook available at https://github.com/hadley/r4ds.