Introduction to R Programming
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Introduction to R Programming

Practice and apply R programming concepts for effective statistical and data analysis
4.2 (10 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
112 students enrolled
Created by Packt Publishing
Last updated 6/2017
Current price: $10 Original price: $95 Discount: 89% off
5 hours left at this price!
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  • 4 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Create and master the manipulation of vectors, lists, dataframes, and matrices
  • Write conditional control structures, and debug and handle errors for efficient error handling
  • See how to use the apply family of functions and write functions used within the apply function
  • Handle dates using lubridate and manipulate strings with stringr package
  • Melt, reshape, aggregate, and cross-tabulate with dcast from dataframes
  • Make and customize various types of charts in base graphics for exceptional data representation
  • Perform univariate and bivariate analysis and do statistical tests
  • Work with databases without having to write SQL using the dplyr package
  • Write readable and expressive code using pipes from magrittr and dplyr’s verbs
  • Perform efficient, high-speed data munging with data.table
  • Work on a full-scale data analysis / data munging project
View Curriculum
  • This course is for everyone, right from college students using R for a project to statisticians, programmers from other platforms, or pure beginners without any prior programming experience who want to become data analysts or data scientists.

Data is everywhere, and statisticians and analysts everywhere need to handle this data efficiently and tactfully. In comes R, a powerful programming language, arming developers with the tools to cater to their needs. This course will give you everything you need to start making software that can unlock your statistics and data.

The course is broken down into three parts. The first part will introduce R Studio and the basics of R—using packages and teaching you programming concepts such as variables, vectors, arrays, loops, and matrices. By solving coding challenges, you will gain a strong foundation for data munging.

With the basics mastered, we will take you through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, writing functions, debugging, error handling, and writing an apply family of functions. When you’ve mastered data munging, we’ll focus on visualizing data using base graphics.

Naturally, the next step is to learn how to make statistical inferences. We walk you through the fundamentals of univariate and bivariate analysis, computing confidence intervals, interpreting p values, and working with statistical significance. You’ll see how and when to use some of the commonly used statistical tests. With that, you will be ready for your first full-scale data analysis project to test the skills you’ve learned.

Finally, you will glimpse two powerful packages for data munging, the dplyr and data.table, which have both seen a rise in the R community. It is imperative to learn about both of these packages because much modern R code has been written using them.

With the help of interesting examples and coding challenges, this course will ensure that you have all the hacks and tricks you need to get started with R.

About The Author

Selva Prabhakaran is a data scientist with a global e-commerce organization. During his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.

Who is the target audience?
  • If you are looking to start your data science career, or are already familiar with data science, statistics, and machine learning concepts, but want to switch to R, this course will be a great place to start
Compare to Other R Courses
Curriculum For This Course
49 Lectures
Installation and Setup
4 Lectures 18:03

This video provides an overview of the entire course.

Preview 04:54

The aim of this video is to show how to install R on our system. 

Installing R

To run and write code in R, we first need to focus on how to get and install the IDE.

Installing RStudio

We have installed R and RStudio. Now let’s check out how to install the packages. 

Installing Packages
Working with Vectors
5 Lectures 18:44

The aim of this video is to teach you what data types and data structures in R are. 

Preview 03:04

In this video, we will see how to work with vectors in R.


The aim of this video is to show how to work with random numbers and do rounding and binning. 

Random Numbers, Rounding, and Binning

Taking vectors a step ahead, let’s see how we can to handle missing values. 

Missing Values

We now know a lot about how vectors work, but how do we get specific items from a vector based on any condition? Let’s check out just that in this video.

The which() Operator
R Essentials
5 Lectures 14:25

This video will introduce a new data structure called list and how to work with it. 

Preview 04:34

In this video, our goal is to understand how to perform set operations in R. 

Set Operations

What is sampling and sorting and how to do it in R? 

Sampling and Sorting

Checking conditions is often a requirement for a programmer to write maintainable code. Let’s understand how we can check conditions in R. 

Check Conditions

You may have come across several instances whilst coding where you need to perform repetitive operations through loops, right? In this video, we’ll see how to do that in R using for loops. 

For Loops
Dataframes and Matrices
6 Lectures 27:52

Let’s explore what data frames are and how to work with them.

Preview 08:30

In this video, we will check out how to import and export data in R. 

Importing and Exporting Data

The aim of this video is to check out how to work with matrices and frequency tables. 

Matrices and Frequency Tables

Our goal in this video is to use W to merge data frame

Merging Dataframes

How to do aggregation in R? 


In this video, we will look at how to de-aggregate data frames and create cross tabulations. 

Melting and Cross Tabulations with dcast()
Core Programming
6 Lectures 27:17

In this video, we will look at how to handle date variables in R. 

Preview 05:35

The goal of this video is to see how to perform string operations in R. 

String Manipulation

Let’s learn how to avoid code replication. 


The aim of this video is to understand how to debug and handle errors. 

Debugging and Error Handling

We’ll see in this video how to write fast loops with apply(). 

Fast Loops with apply()

Sometimes we’d want to iterate through lists. What do we do then? Let’s learn using fast loops with sapply, vapply and lapply to help us achieve this goal. 

Fast Loops with sapply(), lapply() and vapply()
Making Plots with Base Graphics
4 Lectures 17:20

How to make plots and customize them. 

Preview 07:03

Sometimes, just a single Y axis is not enough. It becomes difficult to depict the variations for two variables on different scales in the same chart. To solve this, we’ll look at how to make a plot with two Y axes. 

Drawing Plots with 2 Y Axes

In this video, we will learn how to make multiple plots and custom layout to get better at our analyzing skills. 

Multiplots and Custom Layouts

The aim of this video is to create different types of plots. 

Creating Basic Graph Types
Statistical Inference
6 Lectures 29:41

What are the steps and actions one needs to do as part of data analysis before jumping to predictive modeling? Let’s understand this better. 

Preview 06:16

The aim of this video is to teach you what normal distribution, central limit theorem, and confidence intervals are. 

Normal Distribution, Central Limit Theorem, and Confidence Intervals

In this video, we will understand correlation and Covariance, the concept behind them, and their implementation in R. c

Correlation and Covariance

What is the chi-square statistic, when is it used, and how to do the chi-sq test? 

Chi-sq Statistic

What is ANOVA, its purpose, when to use it, and how to implement it in R? 


What are the other commonly used statistical tests in R and how to implement them? 

Statistical Tests
R Very Own Project
3 Lectures 21:03

All knowledge is incomplete without being put to practice. We’ve got a good taste of the core concepts that govern statistical analysis with R. Let’s solve the challenges pertaining to data manipulation in this video. 

Preview 11:31

What is data if not represented visually! We have solved challenges related to data manipulation. Now it’s time to tackle visualization in this video. 

Project 2 – Visualization with Base Graphics

Practice solving exercises that involve making statistical inference

Project 3 – Statistical Inference
DPlyR and Pipes
5 Lectures 22:28

The aim of this video is to introduce the magrittr package, its significance, and features such as pipe operators. 

Preview 05:21

Understand and use the 7 data manipulation verbs. 

The 7 Data Manipulation Verbs

How to group datasets by one or more variables using dplyr. 

Aggregation and Special Functions

How to join two tables using the two table verbs of dplyr. 

Two Table Verbs

How to work with databases with DplyR. 

Working With Databases
5 Lectures 29:29

Understand the basics of data.table; do filter and select operations

Preview 07:34

Understand the syntax; create and update columns in a data.table. 

Understanding Syntax, Creating and Updating Columns

Learn how to aggregate data.tables. Also learn the .N and .I operators. 

Aggregating Data, .N, and .I

Understand and implement chaining, keys, functions, and .SD. 

Chaining, Functions, and .SD

How to write for-loops with set, set keys, and join data.tables? 

Fast Loops with set(), Keys, and Joins
About the Instructor
Packt Publishing
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