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!
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.
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.
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.
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.
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.
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.
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.
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.
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!
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