R Programming Fundamentals

By Abhishek Kumar
In this course, you will learn various constructs of R programming and get hands-on practice in order to become an efficient and productive R programmer.
Course info
Rating
(616)
Level
Beginner
Updated
Oct 18, 2014
Duration
6h 59m
Table of contents
Getting Started
Course Introduction 3m R Overview 2m Why R? 6m Why Learn R Programming? 2m Install R on Windows 4m Install R on Linux 4m Install R on Mac 3m Hello World in R 7m Editors and IDEs for R 5m Install RStudio on Windows 2m Install RStudio on Linux 2m Install RStudio on Mac 2m RStudio Desktop Overview 8m Course Structure 1m
Getting Help for R
Introduction 1m Outline 1m Built-In Help 1m Built-In Help: Using Help Commands 6m Built-In Help: Using Demo Commands 4m Built-In Help: Using Vignettes 5m Web Search 9m Community Support 1m Community Support: Mailing List 2m Community Support: Forums 1m Community Support: Blogs 1m How to Ask a Question? 4m Summary 2m
R - Variables and Operators
Introduction 1m Outline 1m Variable 1m Naming Convention 1m Naming Guide 2m Assign Variable 3m Environments and Variables 7m Operators 1m Arithmetic Operators 7m Special Numbers : Inf, NaN, NA 6m Logical Operators 3m Vectorized Operations 2m Types of Vectorized Operations 9m Summary 2m
R - Data Structures (Part 1)
Introduction 1m Outline 1m Data Structures in R 4m Atomic Vector 11m Common Operations on Atomic Vectors 9m Factor 7m List 6m Common Operation on Lists 6m Summary 2m
R - Data Structures (Part 2)
Introduction 1m Outline 0m Data Frame 7m Common Operation on Data Frames 9m Matrix 7m Common Operation on Matrices 6m Array 5m Summary 2m
R - Functions
Introduction 1m Outline 1m Functions Overview 3m Demo: Functions Overview 4m Function Components 1m Function Naming Guidelines 1m Argument Matching 2m Arguments With Default Values 3m Additional Arguments Using Ellipsis 5m Lazy Evaluation 4m Multiple Return Values 3m Functions as Objects 4m Anonymous Function 3m Summary 2m
R - Flow Control
Introduction 1m Outline 1m If 5m If-Else 3m Multiple If-Else 4m Switch 6m Vectorized If 5m Repeat 2m Repeat With Break 2m Repeat With Next 2m While 3m For 2m Apply 2m Demo: Apply - Part 1 3m Demo: Apply - Part 2 5m Functions in Apply Family 1m Summary 3m
R - Packages
Introduction 1m Outline 0m About R Package 4m Load R Package 1m Demo: Load R Package - Part 1 4m Demo: Load R Package - Part 2 2m Install R Package 3m Demo: Install R Package 6m Manage R Package 4m Summary 2m
R - Import Data
Introduction 1m Outline 1m Working Directory 1m Demo: Working Directory 3m Import CSV Files 2m Demo: Import CSV Files 3m Import Table 2m Demo: Import Table 3m Import from URL 1m Demo: Import from URL 2m Import XML Files 0m Demo: Import XML Files 3m Import Excel Files 1m Demo: Import Excel Files 4m Import Other File Types 2m Import Built-In Datasets 1m Demo: Import Built-In Datasets 2m Import from Database 3m Demo: Import Database Using RODBC Package 3m Summary 2m
Exploring Data With R
Introduction 2m Outline 1m Types of Data 3m Overall Structure 1m Example Dataset 1m Demo: Overall Structure 2m Analysis of Continuous Data 0m Central Tendency (Mean) 1m Demo: Central Tendency (Mean) 1m Central Tendency (Median) 1m Demo: Central Tendency (Median) 1m Central Tendency: Why Not Sufficient? 2m Spread (Range) 1m Demo: Spread (Range) 1m Spread (Quartiles) 3m Demo: Spread (Quartiles) 2m Spread (Box Plot) 4m Demo: Spread (Box Plot) 3m Spread (Histogram) 1m Demo: Spread (Histogram) 1m Spread (Variance and Standard Deviation) 4m Demo: Spread (Variance and Standard Deviation) 1m Analysis of Categorical Data 1m Frequency Distribution 1m Demo: Frequency Distribution 2m Category Statistics 1m Demo: Category Statistics 7m Summary 2m
Description
Course info
Rating
(616)
Level
Beginner
Updated
Oct 18, 2014
Duration
6h 59m
Description

R is a powerful and widely used open source software and programming environment for data analysis. Companies across the globe use R as an essential tool for various types of analysis to get key insights from data and to make key decisions. This course will provide everything you need to know to get started with the R framework, and contains a number of demos to provide hands-on practice in order to become an efficient and productive R programmer. By the end of this course, you will also learn to play with data and to extract key information using various R functions and constructs.

Course FAQ
Course FAQ
What is R programming used for?

R programming is especially useful for importing and cleaning data in an open source programming environment. R is frequently used by quantitative analysts to help make key decisions from data. It gives a wide range of statistics that are useful for data science, including statistical computing and design.

What will I learn in this course?

You will learn about:

  • R variables and operators
  • R data structures
  • R functions
  • flow control in R
  • R packages
  • how to import data
  • much more
Is R better than Python?

R and Python serve different purposes in that R is mainly used for statistical analysis, whereas Python takes a broader approach to data science. If you have data that is already gathered and cleaned and you just want to analyze it, then R is most useful. But if you are scraping raw, messy data from some source, Python may be more fitting.

Are there prerequisites to this course?

Nope! This is a beginner level course for people just getting started learning R programming, explaining what R is, why you should learn it, and how to install it.

Is R free to download?

Yes! Just follow the instructions in the course to get the latest version downloaded and correctly installed for your specific operating system.

About the author
Abhishek Kumar
About the author

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.

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