R Programming – Intermediate

R Programming – Intermediate

This course will teach experienced data analysts 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.

This course will teach experienced data analysts 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.

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Overview

This course is intended for experienced data analysts looking to unlock the power of R.   Students should have at least one year of daily R use under their belts and a goal of using R as a serious statistical computing tool. This is an advanced course with an open, independent learning style.  Students should be comfortable with a trial and error approach emphasizing good programming practices and the development of clear, concise code.

Learning Outcomes

After completing this course students should be able to work with various data types, recognize different types of loops, and create and apply user-defined functions.

In contrast with the detailed step-by-step approach in an introductory course, this more advanced course will use a more open and independent learning style, and students should expect to occasionally wrestle a bit with the concepts and be comfortable with a trial and error approach.

  • Efficiently deal with different data types and structures
  • Recognize and code different types of loops
  • Create user-defined functions
  • Use functions to avoid loops
  • Properly apply lexical scoping

Who Should Take This Course

Statistical analysts with at least one year of daily R experience and who want to use R as a serious statistical computing tool. 

Instructors

dr-hongcheng-li

Dr. Hongcheng Li

Hongcheng Li is an instructor at Kent State University. He worked with SPSS China as a consultant and Director of Marketing.  Formerly associate professor at Shanghai Finance University, he lectured  on predictive data analysis and statistical software.  He published three texts on data analysis, and two others  on Java programming in Chinese. Dr. Li also translated several  popular machine learning and R books into Chinese, including "R Cookbook", "Data Mining with R: Learning with Case Studies", "Advanced R" and "Machine Learning with R".
See Instructor Bio

Course Syllabus

Week 1

Data

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

Week 2

Loops

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

Week 3

Functions

  • Creating user-defined functions
  • Proper lexical scoping

Week 4

Avoiding Loops

  • Using user-defined functions to avoid loops

Class Dates

2019

Sep 20, 2019 to Oct 16, 2019

2020

Mar 27, 2020 to Apr 24, 2020

Sep 18, 2020 to Oct 16, 2020

2021

Mar 26, 2021 to Apr 23, 2021

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Prerequisites

Statistical analysts with at least one year of daily R experience or have used R as a statistical computing tool. 

 If you are new to R, you should start with R Programming Intro 1.

The courses listed below are prerequisites for enrollment in this course:

R Programming – Introduction Part 1

This course provides an easy introduction to programming in R.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Jan 10, 2020, May 15, 2020, Sep 11, 2020

What Our Students Say​

This course is an excellent follow up to the R basic course. A lot of new and helpful material was added and contributed to a more advanced understanding of R programming

David LaBarre
Risk Analyst at USDA-FSIS

Great explanations provided by the course instructor in the videos and on the discussion board. The teacher assistant was very diligent and professional in reaching out to me and giving me every opportunity to be successful in this course. Definitely a wonderful experience!

Jonathan Jenkins
Cobb County Government

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

R Programming – Advanced

This course will teach you key concepts for writing advanced R code, emphasizing the design of functional and efficient code. After completing the course, students should be able to read, understand, modify, and create complex functions to perform a variety of tasks.
Topic: Data Science, Using R | Skill: Advanced | Credit Options: CAP, CEU
Class Start Dates: Jan 3, 2020, Jun 19, 2020, Jan 1, 2021

R Programming – Introduction Part 2

This course is a continuation of the introduction to R programming.
Topic: Data Science, Using R | Skill: Introductory, Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 13, 2020, Jul 17, 2020, Nov 13, 2020, Mar 12, 2021

Additional Course Information

Organization of 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.

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.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of assigned readings, guided exercises in writing code, narrated slides, and supplemental readings available online.

Course Text

The course text is The Art of R Programming: A Tour of Statistical Software Design, by Norman Matloff.

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.

Please order a copy of your course textbook prior to course start date.

Software

Participants should be familiar with and have access to R.

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.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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R Programming – Intermediate
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