R Programming - Intermediate

Instructor(s):

Dates:

October 25, 2013 to November 22, 2013 April 04, 2014 to May 02, 2014

R Programming - Intermediate

taught by Olivia Lau

Aim of Course:

This course is intended for experienced data analysts looking to unlock the power of R.  It provides a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code.  After completing the course, students should be able to manipulate data programmatically using R functions of their own design.

Course Program:

SESSION 1: Data

• Quick review of R data types and data structures
• Importing data
• Recoding data

SESSION 2: Loops

• Measuring and monitoring R’s performance
• Different types of loops
• Fast loops

SESSION 3: Functions

• Creating user-defined functions
• Proper lexical scoping

SESSION 4: Avoiding loops

• Using user-defined functions to avoid loops

HOMEWORK:

Homework in this course consists of guided exercises in writing code.

R Programming - Intermediate

Instructor(s):

Dates:
October 25, 2013 to November 22, 2013 April 04, 2014 to May 02, 2014
Course Fee: $499

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R Programming - Intermediate

taught by Olivia Lau

Who Should Take This Course:

Statistical analysts with at least one year of daily R use under their belts, who want to use R as a serious statistical computing tool.

Level:

Intermediate

Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:

The course text is R Cookbook (O'Reilly Cookbooks).  You can order it from Amazon here.

The following texts are not required for the course, but provide useful background and a handy reference once you are done. Each session will provide pointers to relevant material in the books.

• R in a Nutshell: A Desktop Quick Reference, by Joseph Adler.
• Data Manipulation with R, by Phil Spector.

Software:

Participants should be familiar with and have access to R. Click Here for information on obtaining a free copy.

The recommended R editor in this course is eMacs (while RStudio is used in other courses, it uses a different R engine, resulting in functionality discrepancies that can be distracting in class).