R Programming – Introduction Part 1

R Programming – Introduction Part 1

This course provides an easy introduction to programming in R.

This course provides an easy introduction to programming in R.

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Overview

This course provides an easy introduction to programming in R for those who have little or no programming experience. Topics include understanding file formats, basic R syntax, and how to use text editors to write code. You will learn to read in files, use symbols and assignments, and iterate simple loops, and the course closes with a discussion of data structures and subsetting.

Learning Outcomes

After taking this course you should be able to install and read data files in R. You will learn to perform various operations and apply common functions to manipulate and analyze data using basic R syntax.

  • Install R
  • Read data files into R
  • Understand assignment operations
  • Create loops for iteration
  • Subset data
  • Apply functions 

Who Should Take This Course

Those who want to start their study of programming in R, especially those with no prior programming experience.  If you do have some programming experience and want to learn R, you could consider starting directly with R Programming Intro 2.

Instructors

dr-tal-galili

Dr. Tal Galili

Dr. Tal Galili has unbounded enthusiasm for teaching and sharing his expertise in R and statistics. He's a Lecturer at Tel Aviv University in Israel, taught courses in introduction to computer science with R and various statistics courses. Tal has received multiple awards for his teaching there.  In addition to writing peer-reviewed articles, Tal is also an active blogger in the R and statistics communities. He's the founder of R-bloggers, a meta-blog which serves over 45,000 subscribers by aggregating R related posts from over 700 blogs. He has published several R packages, and writes the blog R-statistics himself.
See Instructor Bio

Course Syllabus

Week 1

Getting Started with R

  • Installing, starting and stopping R
  • File operations and file formats
  • Writing code and text editors
  • Basic R syntax
  • Reading files
  • Symbols and assignment

Week 2

Loops

  • Sequences
  • Simple loops (iteration)

Week 3

Data Structures

  • Data structures
  • Subsetting
  • Assigning to subsets

Week 4

Data Manipulation

  • Apply Functions

Class Dates

2020

Jan 10, 2020 to Feb 7, 2020

May 15, 2020 to Jun 12, 2020

Sep 11, 2020 to Oct 9, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

There are no prerequisites for this course.

If you do have some programming experience and want to learn R, you could consider starting directly with R Programming – Introduction Part 2.

What Our Students Say​

This course was extremely helpful in facilitating my understanding of programming in R fundamentals.  I feel confident moving forward onto the next section of R programming

Trish Shewokis
Drexel University

This was a great introduction to Programming in R. I feel like I have some basic concepts down and I am looking forward to taking more courses to keep developing my skills in this programming language. I can definitely see the potential for data analysis in my work!

Peter Weddel
Stratus Ag Research

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 for Statistical Analysis

This course will teach you how to use R for basic statistical procedures.
Topic: Data Science, Using R | Skill: Intermediate | Credit Options: CAP, CEU

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: Jun 19, 2020, Jan 8, 2021

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.
Topic: Data Science, Using R | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 27, 2020, Sep 18, 2020, Mar 26, 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

The homework in this course consists of short answer questions to test concepts, guided exercises in writing code, and guided data analysis problems using software.

Course Text

The course text is Introduction to Data Technologies by Paul Murrell. It may be purchased from the publisher Chapman and Hall/CRC Press. The text is also available online here in both PDF and HTML formats.

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

Software

You must have a copy of R for the course. Click Here for information on obtaining a free copy, including installation instructions.  Installation will be covered in the first week of the course, but you should try installing R before the course starts, so that any issues you encounter can be addressed early.

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 – Introduction Part 1
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