R Programming For Absolute Beginners

R Programming For Absolute Beginners, available at $79.99, has an average rating of 4.5, with 119 lectures, based on 3097 reviews, and has 155671 subscribers.

You will learn about Work with vectors, matrices and lists Work with factors Manage data frames Write complex programming structures (loops and conditional statements) Build their own functions and binary operations Work with strings Create charts in base R This course is ideal for individuals who are Wannabe data scientists or Academic researchers or Doctoral researchers or Students or Anyone who wants to master R It is particularly useful for Wannabe data scientists or Academic researchers or Doctoral researchers or Students or Anyone who wants to master R.

Enroll now: R Programming For Absolute Beginners

Summary

Title: R Programming For Absolute Beginners

Price: $79.99

Average Rating: 4.5

Number of Lectures: 119

Number of Published Lectures: 119

Number of Curriculum Items: 119

Number of Published Curriculum Objects: 119

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

Work with vectors, matrices and lists

Work with factors

Manage data frames

Write complex programming structures (loops and conditional statements)

Build their own functions and binary operations

Work with strings

Create charts in base R

Who Should Attend

Wannabe data scientists

Academic researchers

Doctoral researchers

Students

Anyone who wants to master R

Target Audiences

Wannabe data scientists

Academic researchers

Doctoral researchers

Students

Anyone who wants to master R

If you have decided to learn R as your data science programming language, you have made an excellent decision!

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R is the most widely used tool for statistical programming. It is powerful, versatile and easy to use. It is the first choice for thousands of data analysts working in both companies and academia. This course will help you master the basics of R in a short time, as a first step to become a skilled R data scientist.

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The course is meant for absolute beginners, so you don’t have to know anything about R before starting. (You don’t even have to have the R program on your computer; I will show you how to install it.) But after graduating this course you will have the most important R programming skills – and you will be able to further develop these skills, by practicing, starting from what you will have learned in the course.? ?

This course contains about 100 video lectures in nine sections.

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In the first section of this course you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory. Moreover, you will learn how to perform simple operations in R and how to work with variables.

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The next five sections will be dedicated to the five types of data structures in R: vectors, matrices, lists, factors and data frames. So you’ll learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), how to apply functions to data and much more. These are very important topics, because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling.

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After finishing with the data structures we’ll get to the programming structures in R. In this section you’ll learn about loops, conditional statements and functions. You’ll learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later. We will also study some practical examples of functions.

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The next section is about working with strings. Here we will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string and especially how to create regular expressions that identify patterns in strings.

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In the following section you’ll learn how to build charts in R. We are going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line and boxplot. Moreover, you will learn how to plot a function of one variable and how to export the charts you create.?

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Every command and function is visually explained: you can see the output live. At the end of each section you will find a PDF file with practical exercises that allow you to apply and strengthen your knowledge.

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?So if you want to learn R from scratch, you need this course. Enroll right now and begin a fantastic R programming journey!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Getting Started with R

Lecture 1: Installing R and RStudio

Lecture 2: The RStudio Interface

Lecture 3: Installing and Activating R Packages

Lecture 4: Setting the Working Directory

Lecture 5: Basic Operations in R

Lecture 6: Working With Variables

Chapter 3: Vectors

Lecture 1: Creating Vectors With the c() Function

Lecture 2: Creating Vectors Using the Colon Operator

Lecture 3: Creating Vectors With the rep() Function

Lecture 4: Creating Vectors With the seq() Function

Lecture 5: Creating Vectors of Random Numbers

Lecture 6: Creating Empty Vectors

Lecture 7: Indexing Vectors With Numeric Indices

Lecture 8: Indexing Vectors With Logical Indices

Lecture 9: Naming Vector Components

Lecture 10: Filtering Vectors

Lecture 11: The Functions all() and any()

Lecture 12: Sum and Product of Vector Components

Lecture 13: Vectorized Operations

Lecture 14: Treating Missing Values in Vectors

Lecture 15: Sorting Vectors

Lecture 16: Minimum and Maximum Values

Lecture 17: The ifelse() Function

Lecture 18: Adding and Multiplying Vectors

Lecture 19: Testing Vector Equality

Lecture 20: Vector Correlation

Lecture 21: Bonus Lecture: Learn Statistics with R

Lecture 22: Practical Exercises

Chapter 4: Matrices and Arrays

Lecture 1: Creating Matrices With the matrix() Function

Lecture 2: Creating Matrices With the rbind() and cbind() Functions

Lecture 3: Naming Matrix Rows and Columns

Lecture 4: Indexing Matrices

Lecture 5: Filtering Matrices

Lecture 6: Editing Values in Matrices

Lecture 7: Adding and Deleting Rows and Columns

Lecture 8: Minima and Maxima in Matrices

Lecture 9: Applying Functions to Matrices (1)

Lecture 10: Applying Functions to Matrices (2)

Lecture 11: Applying Functions to Matrices (3)

Lecture 12: Adding and Multiplying Matrices

Lecture 13: Other Matrix Operations

Lecture 14: Creating Multidimensional Arrays

Lecture 15: Indexing Multidimensional Arrays

Lecture 16: Practical Exercises

Chapter 5: Lists

Lecture 1: Create Lists With the list() Function

Lecture 2: Create Lists With the vector() Function

Lecture 3: Indexing Lists With Brackets

Lecture 4: Indexing Lists Using Objects Names

Lecture 5: Editing Values in Lists

Lecture 6: Adding and Removing List Objects

Lecture 7: Applying Functions to Lists

Lecture 8: Practical Example of List: the Regression Analysis Output

Lecture 9: Bonus Lecture: Data Analysis in R

Lecture 10: Practical Exercises

Chapter 6: Factors

Lecture 1: Working With Factors

Lecture 2: Splitting a Vector By a Factor Levels

Lecture 3: The tapply() Function

Lecture 4: The by() Function

Lecture 5: Practical Exercises

Chapter 7: Data Frames

Lecture 1: Creating Data Frames

Lecture 2: Loading Data Frames From External Files

Lecture 3: Writing Data Frames in External Files

Lecture 4: Indexing Data Frames As Lists

Lecture 5: Indexing Data Frames As Matrices

Lecture 6: Selecting a Random Sample of Entries

Lecture 7: Filtering Data Frames

Lecture 8: Editing Values in Data Frames

Lecture 9: Adding Rows and Columns to Data Frames

Lecture 10: Naming Rows and Columns in Data Frames

Lecture 11: Applying Functions to Data Frames

Lecture 12: Sorting Data Frames

Lecture 13: Shuffling Data Frames

Lecture 14: Merging Data Frames

Lecture 15: Practical Exercises

Chapter 8: Programming Structures

Lecture 1: For Loops

Lecture 2: While Loops

Lecture 3: Repeat Loops

Lecture 4: Nested For Loops

Lecture 5: Conditional Statements

Lecture 6: Nested Conditional Statements

Lecture 7: Loops and Conditional Statements

Lecture 8: User Defined Functions

Lecture 9: The Return Command

Lecture 10: More Complex Functions Examples

Lecture 11: Checking Whether an Integer Is a Perfect Square

Lecture 12: A Custom Function That Solves Quadratic Equations

Lecture 13: Binary Operations

Lecture 14: Practical Exercises

Chapter 9: Working With Strings

Lecture 1: Creating Strings

Lecture 2: Printing Strings

Instructors

Bogdan Anastasiei
University Teacher and Consultant

Rating Distribution

1 stars: 24 votes

2 stars: 53 votes

3 stars: 300 votes

4 stars: 1133 votes

5 stars: 1587 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!