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R Programming

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R Programming

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R Programming

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Contents

  • 1. Section 1: Welcome
    • Free Welcome!
      03m 04s
    • Free Course Overview
      04m 57s
  • 2. Section 2: Introduction to R
    • Free Why R?
      05m 03s
    • Free R for Data Science
      06m 24s
    • Free Preparing workspace
      04m 17s
    • Guide to RStudio
      09m 20s
    • Exercise 1 - Introduction to R
      03m 01s
  • 3. Section 3: Hello World! - Basics of R programming
    • Operations-and-variables
      08m 44s
    • Data Types in R
      04m 28s
    • Coding Style
      04m 54s
    • Comments
      02m 29s
    • Exercise 2 - Basics of R programming
      04m 17s
  • 4. Section 4: Vectors
    • Vector Creation
      06m 15s
    • Selecting Components from a Vector
      04m 10s
    • Labeling Vector Elements
      05m 52s
    • Calculations with Vectors
      05m 17s
    • Base R functions to use with vectors
      03m 03s
    • Comparing two Vectors
      01m 38s
    • Modifying Vector Components
      02m 00s
    • Exercise 3 - Vectors
      06m 05s
  • 5. Section 5: Matrices
    • Matrix Introduction and creation
      08m 16s
    • Matrix Metrics and Naming
      05m 58s
    • Selecting Elements
      06m 35s
    • Matrix Arithmetic
      03m 54s
    • Matrices Operations
      03m 53s
    • Matrix modification
      04m 49s
    • Exercise 4 - matrices
      05m 36s
  • 6. Section 6: Arrays
    • Array Introduction and Creation
      04m 39s
    • Array Similarities to Matrices
      03m 16s
    • Other Array Operations
      01m 06s
    • Exercise 5 - Arrays
      04m 49s
  • 7. Section 7: Lists
    • List Introduction and Creation
      05m 45s
    • List Naming
      02m 37s
    • Selecting List Elements
      02m 21s
    • List Manipulation
      02m 41s
    • List Operations
      01m 30s
    • Exercise 6 - Lists
      04m 07s
  • 8. Section 8: Factors
    • Factor Introduction and Creation
      04m 46s
    • Setting Factor Levels
      06m 07s
    • Ordering Factors
      05m 20s
    • Converting Factors
      02m 28s
    • Other Considerations
      03m 03s
    • Exercise 7 - Factors
      03m 11s
  • 9. Section 9: Loops
    • Loop Introduction and Creation
      05m 38s
    • If-else Statements
      04m 03s
    • For Loops
      06m 06s
    • While loops
      13m 23s
    • Repeat loops
      02m 45s
    • Loop Comparison
      03m 42s
    • Exercise 8 - Loops
      05m 32s
  • 10. Section 10: Functions
    • Function Introduction and Creation
      06m 58s
    • Function Arguments
      09m 38s
    • Nested Functions
      10m 13s
    • Global vs. Local Variables
      03m 30s
    • Exercise 9 - Function
      06m 44s
  • 11. Section 11: Data Frames
    • Dataframe Introduction and Creation
      08m 00s
    • Tidyverse
      02m 54s
    • Tibbles
      09m 56s
    • Tidy Data
      10m 13s
    • dplyr and data transformation
      09m 12s
    • Summarizing Dataframes
      07m 24s
    • Exercise 10 - Dataframe
      10m 20s
  • 12. Section 12: Mini-project
    • Introduction to Mini-Project
      09m 42s
    • Importing Data
      08m 23s
    • Comprehending the Dataset
      12m 29s
    • Tidying Data
      06m 52s
    • Grouping Time Series Analysis Data
      03m 20s
    • Data Visualization
      07m 35s
    • Statistical Analysis
      07m 17s
    • Exercise 11 Mini-project
      05m 15s
  • 13. Section 13: Course Wrap-up
    • Great Job and Farewell!
      01m 24s
  • Description
  • Course Resources
  • Shortcode Not working

R Programming

  • DURATION: 6.30 hours
  • VIDEOS: 71
  • LEVEL: Beginner

In this Introduction to R Programming course, we assume you are brand new to R Programming and teach you the basics of this impressive programming language.

We’ll start by introducing you to R, why it’s used by data scientists, and what it’s capable of. After that, we get you set up in R Studio and show you how to prepare the R Workspace. We then launch into Data Types in R, Coding Style in R.

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.

We end the course with a mini-project where we pull everything you have learned together and set you an R Programming challenge.

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

R Programming

The course support files consist of two .ZIP files. You will need to download the .zip files to your PC or Mac (the file are not compatible with a mobile device) and unzip the file. Once unzipped, all of the exercise/example files will reside in one folder.

  • R Programming Exercise Files
  • R Programming Instructor Files

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