R Programming for Beginners- Includes R Mini-Project!

R Programming for Beginners: Includes R Mini-Project!, available at $59.99, has an average rating of 4.13, with 73 lectures, 11 quizzes, based on 8 reviews, and has 85 subscribers.

You will learn about What R is and how it is used in Data Science Data types in R, coding style, and comments How to use Vectors in R How to use Matrices in R, including matric operations and modification How to use Arrays in R About using Lists in R including how to select list elements All about Factors in R How to use Loops in R and IF, ELSE statements How to use Functions in R How to use Data Frames including tidyverse and tibbles in R This course is ideal for individuals who are People looking to learn the R Programming Language or People learning data science It is particularly useful for People looking to learn the R Programming Language or People learning data science.

Enroll now: R Programming for Beginners: Includes R Mini-Project!

Summary

Title: R Programming for Beginners: Includes R Mini-Project!

Price: $59.99

Average Rating: 4.13

Number of Lectures: 73

Number of Quizzes: 11

Number of Published Lectures: 73

Number of Published Quizzes: 11

Number of Curriculum Items: 84

Number of Published Curriculum Objects: 84

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

What R is and how it is used in Data Science

Data types in R, coding style, and comments

How to use Vectors in R

How to use Matrices in R, including matric operations and modification

How to use Arrays in R

About using Lists in R including how to select list elements

All about Factors in R

How to use Loops in R and IF, ELSE statements

How to use Functions in R

How to use Data Frames including tidyverse and tibbles in R

Who Should Attend

People looking to learn the R Programming Language

People learning data science

Target Audiences

People looking to learn the R Programming Language

People learning data science

In this R Programming for Beginners course, we start at the very beginning and introduce you to the R programming language. After that, we get you set up in R Studio and show you how to prepare the R Workspace.

We also show you how to import data into R Studio from various file formats before launching into the essential R components – Vectors, Matrices, Arrays, Lists, Factors, Loops, Functions, Dataframes and so much more.

This course includes downloadable challenges throughout to help you implement in real life what you are learning. At the end of the course, you pull everything you have learned together and complete a mini-project to help develop your skills.

This course is about hands-on learning, not just theory!

In this course you will learn:

What R is and how it is used in Data Science

Data types in R, coding style, and comments

How to use Vectors in R

How to use Matrices in R, including matric operations and modification

How to use Arrays in R

About using Lists in R including how to select list elements

All about Factors in R

How to use Loops in R and IF, ELSE statements

How to use Functions in R

How to use Data Frames including tidyverse and tibbles in R

To complete your first R programming assignment

This course includes:

1. 6 hours of video tutorials

2. 70+ individual video lectures

3. Exercise files to practice what you learned

4. An R projectat the end of the course to implement what you have learned

Course Curriculum

Chapter 1: Welcome

Lecture 1: Welcome!

Lecture 2: WATCH ME: Essential Information for a Successful Training Experience

Lecture 3: DOWNLOAD ME: Course Exercise Files

Lecture 4: DOWNLOAD ME: Course Instructor Files

Chapter 2: Introduction to R

Lecture 1: Why R?

Lecture 2: R for Data Science

Lecture 3: Preparing Workspace

Lecture 4: Guide to RStudio

Lecture 5: Exercise 1 – Introduction to R

Chapter 3: Hello World! – Basics of R programming

Lecture 1: Operations-and-variables

Lecture 2: Data Types in R

Lecture 3: Coding Style

Lecture 4: Comments

Lecture 5: Exercise 2 – Basics of R programming

Chapter 4: Vectors

Lecture 1: Vector Creation

Lecture 2: Selecting Components from a Vector

Lecture 3: Labeling Vector Elements

Lecture 4: Calculations with Vectors

Lecture 5: Base R functions to use with vectors

Lecture 6: Comparing two Vectors

Lecture 7: Modifying Vector Components

Lecture 8: Exercise 3 – Vectors

Chapter 5: Matrices

Lecture 1: Matrix Introduction and Creation

Lecture 2: Matrix Metrics and Naming

Lecture 3: Selecting Elements

Lecture 4: Matrix Arithmetic

Lecture 5: Matrices Operations

Lecture 6: Matrix Modification

Lecture 7: Exercise 4 – Matrices

Chapter 6: Arrays

Lecture 1: Array Introduction and Creation

Lecture 2: Array Similarities to Matrices

Lecture 3: Other Array Operations

Lecture 4: Exercise 5 – Arrays

Chapter 7: Lists

Lecture 1: List Introduction and Creation

Lecture 2: List Naming

Lecture 3: Selecting List Elements

Lecture 4: List Manipulation

Lecture 5: List Operations

Lecture 6: Exercise 6 – Lists

Chapter 8: Factors

Lecture 1: Factor Introduction and Creation

Lecture 2: Setting Factor Levels

Lecture 3: Ordering Factors

Lecture 4: Converting Factors

Lecture 5: Other Considerations

Lecture 6: Exercise 7 – Factors

Chapter 9: Loops

Lecture 1: Loop Introduction and Creation

Lecture 2: If-Else Statements

Lecture 3: For Loops

Lecture 4: While Loops

Lecture 5: Repeat Loops

Lecture 6: Loop Comparison

Lecture 7: Exercise 8 – Loops

Chapter 10: Functions

Lecture 1: Function Introduction and Creation

Lecture 2: Function Arguments

Lecture 3: Nested Functions

Lecture 4: Global vs. Local Variables

Lecture 5: Exercise 9 – Function

Chapter 11: Data Frames

Lecture 1: Dataframe Introduction and Creation

Lecture 2: Tidyverse

Lecture 3: Tibbles

Lecture 4: Tidy Data

Lecture 5: Dplyr and Data Transformation

Lecture 6: Summarizing Dataframes

Lecture 7: Exercise 10 – Dataframe

Chapter 12: Mini-project

Lecture 1: Introduction to Mini-Project

Lecture 2: Importing Data

Lecture 3: Comprehending the Dataset

Lecture 4: Tidying Data

Lecture 5: Grouping Time Series Analysis Data

Lecture 6: Data Visualization

Lecture 7: Statistical Analysis

Lecture 8: Exercise 11 Mini-project

Chapter 13: Course Wrap-up

Lecture 1: Great Job and Farewell!

Instructors

Simon Sez IT
870,000+ Students, 260+ Courses, Learners in 180+ Countries

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5 stars: 2 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?

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