R Programming for Absolute Beginners

R Programming for Absolute Beginners, available at $19.99, has an average rating of 4.25, with 69 lectures, 9 quizzes, based on 2 reviews, and has 115 subscribers.

You will learn about Learn the fundamental principles of R programming Learn how to create and assign variables Learn how to install packages in R, how to use Jupyter Notebook, Anaconda distribution Understand what is Data and different Types of Data Learn various data types and data structures in R Learn how to manipulate data in R Learn how to deal with NAs Understand what is Tidy Verse in R Practice working with dplyr packages and functions in R Practice working with Palmer Archipelago data set in R Learn to work with ggplot2() and its layers in R Learn how to read plots, convey results in R Learn how to visualize data in R, how to make aesthetically appealing plots This course is ideal for individuals who are Anyone who wants to learn R or Anyone without prior coding or programming knowledge or experience or Anyone who wants to understand R from scratch, basic fundamentals and concepts or Anyone who is at a Beginner stage and wants to reach an intermediate level or Anyone without Technical background It is particularly useful for Anyone who wants to learn R or Anyone without prior coding or programming knowledge or experience or Anyone who wants to understand R from scratch, basic fundamentals and concepts or Anyone who is at a Beginner stage and wants to reach an intermediate level or Anyone without Technical background.

Enroll now: R Programming for Absolute Beginners

Summary

Title: R Programming for Absolute Beginners

Price: $19.99

Average Rating: 4.25

Number of Lectures: 69

Number of Quizzes: 9

Number of Published Lectures: 69

Number of Published Quizzes: 9

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: ?3,299

Quality Status: approved

Status: Live

What You Will Learn

Learn the fundamental principles of R programming

Learn how to create and assign variables

Learn how to install packages in R, how to use Jupyter Notebook, Anaconda distribution

Understand what is Data and different Types of Data

Learn various data types and data structures in R

Learn how to manipulate data in R

Learn how to deal with NAs

Understand what is Tidy Verse in R

Practice working with dplyr packages and functions in R

Practice working with Palmer Archipelago data set in R

Learn to work with ggplot2() and its layers in R

Learn how to read plots, convey results in R

Learn how to visualize data in R, how to make aesthetically appealing plots

Who Should Attend

Anyone who wants to learn R

Anyone without prior coding or programming knowledge or experience

Anyone who wants to understand R from scratch, basic fundamentals and concepts

Anyone who is at a Beginner stage and wants to reach an intermediate level

Anyone without Technical background

Target Audiences

Anyone who wants to learn R

Anyone without prior coding or programming knowledge or experience

Anyone who wants to understand R from scratch, basic fundamentals and concepts

Anyone who is at a Beginner stage and wants to reach an intermediate level

Anyone without Technical background

R Programming for Absolute Beginners

You will Learn:

Learn the fundamental principles of R programming

Learn how to create and assign variables

Learn about integer, double, logical, character, and other types in R

Learn all data structures in R

Learn how to build data structures like vectors, matrices, factors in R

Learn how to work with data structures in R

Learn how to install packages in R

Learn how to use Jupyter Notebook, Anaconda distribution

Learn how to handle various files in R like CSV, Excel, and JSON files.

Learn how to create your own data frame in R

Understand various packages in R

Understand what is Tidy Verse in R

Practice working with dplyr packages and functions in R

Practice working with ggplot2 in R

Understand the utility of various plots

Learn how to read plots, convey results in R

Practice working with palmer archipelago data in R

Eligibility:

Anyone without prior coding or programming knowledge or experience

Anyone without Technical background

Anyone who wants to understand R from scratch, basic fundamentals and concepts

Anyone who is at a Beginner stage and wants to reach an intermediate level

Anyone who wants to learn R

Benefits of the course:

Starts with very basic

Concept building and clarity

Learning with help of practical exercises

Bonus Lectures to further deepen the understanding

Quizzes after practical sessions to deepen the concepts

Resources at the end of the section to aid learning and quick revision

Course Curriculum

Chapter 1: Getting Started : Installation and Set Up

Lecture 1: Course Introduction

Lecture 2: Downloading Anaconda Distribution

Lecture 3: Complete Installation and Set up

Lecture 4: Installing R

Chapter 2: Handling Files in R

Lecture 1: Section Introduction | Learnings from this section

Lecture 2: Understanding Data

Lecture 3: Different Types of Data

Lecture 4: Create your own Data frame in R

Lecture 5: Import and Export a CSV File

Lecture 6: Import and Export an Excel File

Lecture 7: Import and Export JSON FIle

Chapter 3: R Basics : Variables and Data Types and Data Structure in R

Lecture 1: Section Introduction| | What you will learn

Lecture 2: What are variables and assigning data

Lecture 3: Understanding Data Types in R

Lecture 4: Data Types in R | Practical Exercise

Lecture 5: Understanding Data Structures in R

Lecture 6: What are Vectors, Understanding Vectors and Operations on Vectors

Lecture 7: What are Lists in R

Lecture 8: List | Create List and other functions

Lecture 9: What are Matrices in R

Lecture 10: Matrices Part 1: Create Matrices and operations in Matrix

Lecture 11: Matrices Part 2 | Matrix Calculations

Lecture 12: Understanding Data Frames in R

Lecture 13: DataFrames Part 1: Create a data frame in R , data exploration in R

Lecture 14: Data Frames Part 2: Adding Rows, Columns, Renaming Columns, Combining Data Frame

Lecture 15: Understanding Factors in R

Lecture 16: Factors | Create Factors and operations on Factors in R

Lecture 17: Understanding Arrays in R

Lecture 18: Arrays | Create Arrays and various Operations on Arrays

Lecture 19: Data Structures in R | Complete Wrap Up

Chapter 4: Data Manipulation in R | Introduction to dplyr , work with real data set

Lecture 1: Section Introduction | What you will learn

Lecture 2: Tidy Verse Brief Introduction

Lecture 3: Understanding dplyr

Lecture 4: Understanding The Pipe %>% Operator

Lecture 5: Loading Libraries, Loading Data Set and Data Exploration in R

Lecture 6: Understanding the Data Set : Data set Description

Lecture 7: Understanding arrange() function

Lecture 8: arrange() function Practice

Lecture 9: Exploring the Data in R | More Functions

Lecture 10: Understanding filter() function

Lecture 11: Filter() Function Practice

Lecture 12: Understanding select() function

Lecture 13: Select() Function Practice

Lecture 14: Dealing with NAs in the Data Frame

Lecture 15: Different Types of Missing Data

Lecture 16: Understanding summarize() function

Lecture 17: Summarize() function Practice

Lecture 18: Group_by() function Practice

Lecture 19: Understanding mutate() function

Lecture 20: Mutate() function Practice

Chapter 5: Understanding Different Charts in ggplot2

Lecture 1: Introduction to Data Visualization in R

Lecture 2: An introduction to ggplot2

Lecture 3: More on ggplot2 library

Lecture 4: Distribution Plots in R

Lecture 5: Composition Charts in R

Lecture 6: Correlation Plots in R

Lecture 7: Ranking Charts in R

Lecture 8: Grouped Plots in R

Lecture 9: Deviation Plots in R

Chapter 6: Data Visualization in R with ggplot2 | Working with different layers in ggplot2

Lecture 1: First Basic Plot With Basic layers | Adding Data, Aesthetics and Geometries

Lecture 2: Adding Labels to the Plot

Lecture 3: Adding Facets Layer

Lecture 4: Improving Facets | Dealing with NAs in the Plot

Lecture 5: Adding Coordinates Layer

Lecture 6: Adding Theme Layer

Lecture 7: Working with Scatter Plots

Lecture 8: Improved Scatter Plot with ggplot2 layers

Lecture 9: More Data For Practice

Chapter 7: Bonus Section

Lecture 1: Bonus

Instructors

Priyanka Sharma
Data Analyst, Content Writer, Instructor

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5 stars: 1 votes

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