R Programming R Language for Absolute Beginners

R Programming – R Language for Absolute Beginners, available at $84.99, has an average rating of 4.6, with 113 lectures, 22 quizzes, based on 2611 reviews, and has 14694 subscribers.

You will learn about Installing R and R Studio Manipulating R Vectors, Arrays and Matrixes Manipulating R Data Frames Plotting Data Using R Analyzing Real Datasets using R Organizing your Code in R Develop your own Functions in R Summarizing Data with R Load Excel Files with R Compute Basic Statistics about a Dataset Install External Libraries to Power up R Aggregate and Sort Data Subset Data This course is ideal for individuals who are Data Analysts or Business Analysts or Financial Managers or Statisticians or Researchers or Software Engineering Undergraduates or Data Scientists or Data Engineers or R learners or Entry-level Data Scientists It is particularly useful for Data Analysts or Business Analysts or Financial Managers or Statisticians or Researchers or Software Engineering Undergraduates or Data Scientists or Data Engineers or R learners or Entry-level Data Scientists.

Enroll now: R Programming – R Language for Absolute Beginners

Summary

Title: R Programming – R Language for Absolute Beginners

Price: $84.99

Average Rating: 4.6

Number of Lectures: 113

Number of Quizzes: 22

Number of Published Lectures: 112

Number of Published Quizzes: 22

Number of Curriculum Items: 135

Number of Published Curriculum Objects: 134

Original Price: 84.99

Quality Status: approved

Status: Live

What You Will Learn

Installing R and R Studio

Manipulating R Vectors, Arrays and Matrixes

Manipulating R Data Frames

Plotting Data Using R

Analyzing Real Datasets using R

Organizing your Code in R

Develop your own Functions in R

Summarizing Data with R

Load Excel Files with R

Compute Basic Statistics about a Dataset

Install External Libraries to Power up R

Aggregate and Sort Data

Subset Data

Who Should Attend

Data Analysts

Business Analysts

Financial Managers

Statisticians

Researchers

Software Engineering Undergraduates

Data Scientists

Data Engineers

R learners

Entry-level Data Scientists

Target Audiences

Data Analysts

Business Analysts

Financial Managers

Statisticians

Researchers

Software Engineering Undergraduates

Data Scientists

Data Engineers

R learners

Entry-level Data Scientists

So, you’ve decided that you want to learn R or you want to get familiar with it, but don’t know where to start? Or are you a data/business analyst or data scientist that wants to have a smooth transition into R programming?

Then, this course was designed just for you!

Hear what other students have to say first:

“I came to this course after an online university course *cough coursera cough* left me crying. The entire curriculum that this class covers was what they covered in week 1, and from what I’ve heard, R has a very steep learning curve. The extra time taken to even walkthrough installation is clearer here. It is much appreciated. You get lots of opportunities to practice, follow along, and really build your knowledge step by step.”5 star review by a Udemy user

“A really really good course for beginners in R, highly recommended. Everything is well explained in detail and enough coding exercises to get you familiarize with concepts before moving to the next section.

Thanks for this Ivo, appreciated.”5 star review by a Udemy user

“Easy to understand, calm and clear explanation. Covered points which are ignored by other tutors. Great”5 star review by a Udemy user

This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups: 

1. Introductory slides lectures with the most well-known commands for each type of R object.

2. Code along lectures where you will see how we can implement the stuff we will learn!

3. Test your knowledge with questions and practical exerciseswith different levels of difficulty!

4. Analyze real datasetsand understandthe thought process from question to R code solution!

This course was designed to be focused on the practical side of coding in R – instead of teaching you every function and method out there, I’ll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects. 

At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis.

Here are some examples of things you will be able to do after finishing the course:

Load CSV and Excel files into R;

Do interesting line plotsthat enable you to draw conclusions from data.

Plot histograms of numerical data.

Create your own functionsthat will enable you to reutilize code.

Slice and dice Data Frames, subsetting data for specific domains.

Join thousands of professionals and students in this R journey and discover the amazing power of this statistical open-source language.

This course will be constantly updated based on students feedback.

Course Curriculum

Chapter 1: Course Intro

Lecture 1: Welcome!

Lecture 2: Course Materials

Chapter 2: Installing R and R Studio

Lecture 1: Installing R

Lecture 2: Installing R Studio

Chapter 3: Introduction – Basic Operations in R

Lecture 1: [Slides] – R as a Calculator and Vectors

Lecture 2: Using R as a Calculator – Simple Calculations

Lecture 3: Using R as a Calculator – Functions

Lecture 4: Practical Exercises – Time to test your skills!

Lecture 5: Link to Exercise Solutions

Chapter 4: Vectors and the Environment

Lecture 1: Creating Vectors and Knowing the Environment

Lecture 2: Vector Indexing and Slicing

Lecture 3: Calculations with Vectors

Lecture 4: More Functions and Dealing with Unexpected Values

Lecture 5: Comparison Operators

Lecture 6: Vectors Names Property

Lecture 7: Modifying Vector Elements

Lecture 8: Comparing R with Excel and SQL

Lecture 9: [TUTORIAL] – Completing and Debugging Coding Exercises on Udemy Platform

Lecture 10: Practical Exercises – Time to test your skills on Vectors!

Chapter 5: R Data Types

Lecture 1: [Slides] – R Data Types

Lecture 2: Underlying Data Types and Types at the Class Level

Lecture 3: Checking Data Types of Objects

Lecture 4: Converting Data Types

Lecture 5: Introduction to Factors

Lecture 6: Dealing with Dates

Lecture 7: Practical Exercises – Time to test your skills on Data Types!

Chapter 6: R Arrays

Lecture 1: [Slides] – Arrays and Matrices

Lecture 2: Creating Arrays

Lecture 3: Indexing and Modifying Arrays

Lecture 4: Array Operations

Lecture 5: Array Dimnames Property

Lecture 6: Combining Arrays

Lecture 7: Practical Exercises – Time to test your skills on Arrays!

Chapter 7: R Matrices

Lecture 1: Creating Matrices

Lecture 2: Matrix Operations

Lecture 3: Practical Exercises – Time to test your skills on Matrices!

Chapter 8: Data Frames – Introduction

Lecture 1: [Slides] – Data Frames & Lists

Lecture 2: Creating a Data Frame

Lecture 3: Indexing and Modifying Data Frames

Lecture 4: Expanding Data Frames

Lecture 5: Removing Elements from Data Frames

Chapter 9: R Lists

Lecture 1: Creating Lists

Lecture 2: List Indexing

Lecture 3: Changing and Adding elements to Lists

Lecture 4: Deleting List Elements

Lecture 5: Combining Lists

Lecture 6: Practical Exercises – Time to test your skills on Lists!

Chapter 10: Course Break

Lecture 1: Course Break

Chapter 11: Libraries

Lecture 1: Installing Libraries

Lecture 2: Loading Libraries

Chapter 12: Working with Data Frames

Lecture 1: Introduction

Lecture 2: Inspecting Data Frames

Lecture 3: Filtering Data Frames

Lecture 4: Creating New Columns in a Data Frame

Lecture 5: Apply Family

Lecture 6: New Column with Sapply

Lecture 7: Aggregating and Sorting

Lecture 8: Merging Data Frames

Lecture 9: Extra – Merging Data Frames using a SQL-Like Library

Lecture 10: Plotting Overview (Base R)

Lecture 11: GGPlot 2 Overview

Lecture 12: Practical Exercises – Time to test your skills on manipulating Data Frames!

Chapter 13: Loading External Data

Lecture 1: Exploring Working Directories

Lecture 2: Loading CSV Files

Lecture 3: Loading Excel (xls|xlsx) Files

Lecture 4: Loading data from a MySQL Database

Chapter 14: Real World Data Frame Analysis – Walmart Data

Lecture 1: Introduction

Lecture 2: Loading the Data

Lecture 3: Extracting the Number of Rows

Instructors

Ivo Bernardo
Partner and Senior Data Scientist @ Daredata Engineering

Rating Distribution

1 stars: 10 votes

2 stars: 17 votes

3 stars: 154 votes

4 stars: 909 votes

5 stars: 1521 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!