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IRPUndergraduate courses

Introduction to R Programming

An easy first course in R for people with little or no programming experience — syntax, loops, vectors and data frames, written from scratch.

  • Introductory
  • 4 weeks
  • Approx. 15 hours per week
  • 3 semester hours

About this course

This course provides an easy introduction to programming in R for those who have little or no programming experience. It starts before R does: what a file format is, what pseudocode and a flow chart are for, and which text editor to write code in. From there it covers basic R syntax, reading data files, symbols and assignment, simple loops, and the data structures you will spend the rest of your R career using — vectors, lists, matrices and data frames, and how to subset each of them. By the end you can load a data set, manipulate it, and produce numerical summaries and basic graphs.

Who this course is for

Anyone who wants to start studying programming in R, especially people with no prior programming experience of any kind. Analysts, researchers and students who have been reading other people's R code and now need to write their own will find this the place to begin.

What you will learn

12 outcomes

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

  • Install R and RStudio
  • Write simple pseudocode and create flow charts
  • Document code
  • Use file management and version control tools
  • Perform simple arithmetic and statistical operations in R
  • Read data files into R
  • Create loops for iteration
  • Subset data vectors and lists
  • Use the apply family of functions for subsetting and computation
  • Use simple R functions for numerical analysis and basic graphs
  • Work confidently with R data structures, especially vectors and data frames
  • Manipulate, sort and merge data frames

Week by week

4-Week curriculum overview

This curriculum is identical across all start dates. Expand a week to explore the topics covered.

Weeks
4
Your week
Approx. 15 hours
Level
Introductory
Getting started with RWeek 1
  • Basic programming principles — flow charts and pseudocode
  • Installing, starting and stopping R
  • File operations and file formats
  • Writing code and text editors
  • Basic R syntax
  • Reading files
  • Symbols and assignment
Variables, loops and data structuresWeek 2
  • Variables
  • Sequences
  • Simple loops — iteration
  • Data structures
  • Exploring data
  • Subsetting data
apply and other functionsWeek 3
  • The apply function
  • Special values
  • Packages
  • Useful functions
Multidimensional dataWeek 4
  • Overview of vectors and vector manipulation
  • Factors
  • Attributes
  • Lists, matrices and arrays
  • Data frames

Instructors

Expert-Led Guidance

Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.

  • Mr. Kuber Deokar

    Mr. Kuber Deokar is Data Science Lead at UpThink EduTech Services. He holds a master's degree in Statistics from the University of Pune, India, where he previously taught undergraduate statistics. He co-authored Machine Learning for Business Analytics with Galit Shmueli, Peter Bruce and Nitin Patel. With over a decade of experience, he specializes in course design, development, delivery and management, coordinating online courses and the communication between course creators, instructors, teaching assistants and students. His interests are machine learning and responsible artificial intelligence.

Before you start

What you need

Everything to have to hand before the first week. Open a row for the detail.

Course Format & Schedule

This is a 4-week, 100% online, asynchronous course.

  • No mandatory live sessions: Log in and complete your work at times that fit your schedule.
  • Weekly Releases: At the start of each week, you will receive new lecture materials and answer keys for the previous week's exercises.
  • Interactive Community: Work through exercises, submit assignments, and engage with your instructor and peers via a private discussion board.
Homework

Short-answer questions testing the concepts, guided exercises in writing code, and guided data analysis problems using software.

Texts

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.

Software

You need a copy of R, which is free from r-project.org, and RStudio, which is the editor used throughout. Installation is covered in week one, but try it before the course starts so that any problem with your machine is found early rather than in the middle of the first assignment.

FAQ

I have never written a line of code in any language. Is that a problem?This course

No — that is who the course is written for. Week one deliberately spends its time on the things a first programming course usually assumes: what a file format is, how to sketch a procedure as pseudocode or a flow chart, and how to get code out of your head and into an editor. R itself comes next.

Do I need to install anything before the first week?This course

Installing R and RStudio is part of week one, so nothing is required in advance. Attempting the installation beforehand is still worth doing: it is free, it takes a few minutes, and if your machine puts up a fight you will have found out while there is time to sort it out.

Is there a money-back or satisfaction guarantee?Every course

We handle cancellation and refund requests on an individual basis. If a course is not meeting your expectations or your circumstances change, please reach out to our team (support@learnstatistics.org) so we can work with you on a solution.

Can I transfer to a later start date or withdraw after the course begins?Every course

Yes. If you need to defer your enrollment to a future start date, withdraw mid-course, or transfer your seat to a colleague, please contact us directly. We address these requests flexibly on a case-by-case basis.

Who teaches the course?Every course

Each cohort is led by dedicated instructors and assistant teachers who actively lead weekly discussions, provide personalized feedback and grade your assignments.

Something not answered here? Ask us before you book — and the terms set out what a purchase covers.

Credit, and where it counts

This course carries a verified figure of 3 semester hours. A credit recommendation from the American Council on Education says what a course is worth; the institution receiving it decides whether to award it.

Anyone can take this subject: there is no application, and you do not have to be studying at a university. It also counts toward a degree at Thomas Edison State University, which decides what to award for it. The full position — including the limits each university publishes is on its own page, and worth reading before you buy.