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Programming and data science

R Programming

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.

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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
To complete your first R programming assignment

Who should take this training

Prerequisites

  • Computer with at least 4GB Ram

Target audience

  • Data Analysts, Business Analysts Financial Managers, Statisticians Researchers, R learners Software Engineering Undergraduates Data Scientists, Data Engineers Entry-level Data Scientists

About this training


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.

Course Language : EN
On-demand video
Full lifetime access to videos
Downloadable resources
Assignments
Certificate of Completion
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Training options

Only Videos

$ 30

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • Hours of Individual Coaching

Blended

$ 630

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 10 Hours of Individual Coaching
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Blended

$ 1200 $ 1107

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 20 Hours of Individual Coaching
Save 20%

Blended

$ 1830 $ 1464

  • Full lifetime access to videos
  • Downloadable resources
  • Certificate of Completion
  • 30 Hours of Individual Coaching

Course Content

1. Section 1: Welcome
1. Welcome! 03:04 mins
2. Course Overview 04:57 mins
2. Section 2: Introduction to R
1. Why R? 05:03 mins
2. R for Data Science 06:24 mins
3. Preparing workspace 04:17 mins
4. Guide to RStudio 09:20 mins
5. Exercise 1 - Introduction to R 03:01 mins
3. Section 3: Hello World! - Basics of R programming
1. Operations-and-variables 08:44 mins
2. Data Types in R 04:28 mins
3. Coding Style 04:54 mins
4. Comments 02:29 mins
5. Exercise 2 - Basics of R programming 04:17 mins
4. Section 4: Vectors
1. Vector Creation 06:15 mins
2. Selecting Components from a Vector 04:10 mins
3. Labeling Vector Elements 05:52 mins
4. Calculations with Vectors 05:17 mins
5. Base R functions to use with vectors 03:03 mins
6. Comparing two Vectors 01:38 mins
7. Modifying Vector Components 02:00 mins
8. Exercise 3 - Vectors 06:05 mins
5. Section 5: Matrices
1. Matrix Introduction and creation 08:16 mins
2. Matrix Metrics and Naming 05:58 mins
3. Selecting Elements 06:35 mins
4. Matrix Arithmetic 03:34 mins
5. Matrices Operations 03:53 mins
6. Matrix modification 04:49 mins
Exercise 4 - matrices 05:36 mins
6. Section 6: Arrays
1. Array Introduction and Creation 04:40 mins
2. Array Similarities to Matrices 03:16 mins
3. Other Array Operations 01:06 mins
Exercise 5 - Arrays 04:49 mins
7. Section 7: Lists
1. List Introduction and Creation 05:45 mins
2. List Naming 02:37 mins
3. Selecting List Elements 02:21 mins
4. List Manipulation 02:41 mins
5. List Operations 01:30 mins
Exercise 6 - Lists 04:07 mins
8. Section 8: Factors
1. Factor Introduction and Creation 04:46 mins
2. Setting Factor Levels 06:07 mins
3. Ordering Factors 05:20 mins
4. Converting Factors 02:28 mins
5. Other Considerations 03:03 mins
Exercise 7 - Factors 03:11 mins
9. Section 9: Loops
1. Loop Introduction and Creation 05:38 mins
2. If-else Statements 04:03 mins
3. For Loops 06:06 mins
4. While loops 12:23 mins
5. Repeat loops 02:45 mins
6. Loop Comparison 03:42 mins
Exercise 8 - Loops 05:32 mins
10. Section 10: Functions
1. Function Introduction and Creation 06:58 mins
2. Function Arguments 09:38 mins
3. Nested Functions 10:13 mins
4. Global vs. Local Variables 03:30 mins
Exercise 9 - Function 06:44 mins
11. Section 11: Data Frames
1. Dataframe Introduction and Creation 08:00 mins
2. Tidyverse 02:54 mins
3. Tibbles 09:55 mins
4. Tidy Data 10:13 mins
5. dplyr and data transformation 09:12 mins
6. Summarizing Dataframes 07:24 mins
Exercise 10 - Dataframe 10:20 mins
12. Section 12: Mini-project
1. Introduction to Mini-Project 09:42 mins
2. Importing Data 08:23 mins
3. Comprehending the Dataset 12:29 mins
4. Tidying Data 06:52 mins
5. Grouping Time Series Analysis Data 03:20 mins
6. Data Visualization 07:35 mins
7. Statistical Analysis 07:17 mins
Exercise 11 Mini-project 05:15 mins
13. Section 13: Course Wrap-up
1. Great Job and Farewell! 01:24 mins

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