Fundamentals of R

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QA
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DifficultyIntermediate
AVG Duration10h
Students124
Content
Course Created with Sketch. 11 Resources Created with Sketch. 1 Exams Created with Sketch. 3 Labs Created with Sketch. 1

Description

Learning Path overview

Have you ever wondered how you can turn your massive datasets in to useful information for your team, organisation or business? Then R is the language you need to learn.  

This learning path introduces the fundamental concepts and knowledge you need to use the programming language R, a mathematical and statistical modelling language used extensively in data analysis and Big Data. 

By attending this course you will learn how to write programmes using R to create effective statistical outputs and to visualize data using R’s library. 

Intended Audience

Aimed at all who wish to learn the R programming language. 

Prerequisites of the Course

No prior knowledge of R is assumed 

Delegates should already be familiar with basic programming concepts such as variables, scope and functions 

Experience of another scripting language such as Python or Perl would be an advantage 

Having an understanding of mathematical concepts will be beneficial 

Learning Objectives

At the end of this course you will be able to understand: 

  • The basics of R 
  • How to download and install RStudio 
  • What makes RStudio unique 
  • Datasets in RStudio  
  • Basic operations in R 
  • Variables, Booleans, operations and integers in R  
  • Data Structures in R  
  • Functions in R 
  • Operators in R 
  • Conditional Statements in R  
  • Implicit Control Flow in R  
  • Plotting in R  

Agenda

This Learning Path contains videos, PDFs, and labs for 10 courses.  

Course Introduction

We begin with an introduction to your trainer Kunal, and what you can expect from the videos in this Learning Path.  

Module 1: Interacting in R

  • Introductio to RStudio 
  • Understanding RStudio 
  • What makes RStudio unique?  
  • Adding comments in R 
  • Useful keyboard shortcuts in RStudio 
  • Viewing a dataset in RStudio  

Module 2: Introduction to Data

  • Basic calculator operations in R  
  • How to assign variables in R  
  • How to transmit to output devices in R 
  • How to install a package in R  

Module 3: Beginner Data Structures in R

  • Vectors in R  
  • Generating Vectors in R  
  • Element access in R  
  • Operations in R  
  • Vector Recycling in R  
  • Booleans in R  
  • Characters in R  
  • Missing data in R  
  • Infinity in R  

Module 4: Intermediate Data Structures in R

  • Objects in R  
  • Integers in R 
  • Implicit coersion in R  
  • Explicit coercion in R  
  • Matrices in R  
  • Subsetting matrices in R  
  • Arrays in R  
  • Lists in R  

Module 5: Advanced Data Structures in R

  • Factors in R  
  • Data frames in R  
  • Modifying data frames in R  
  • Subsetting data frames in R  
  • Factors in a data frame in R  

Module 6: Functions in R

Introduction to functions in R  

Using built in functions in R  

Creating functions in R  

What is returned from a function in R  

Functions of functions in R 

Input arguments of functions in R  

Named arguments of functions in R  

Module 7: Operators in R

  • Operators in R  
  • Introduction to Conditional Statements in R  
  • Vectorised comparisons to generate one result in R  
  • Vectorised comparisons to generate many results in R  

Module 8: Conditional Statements in R

  • Explicitly repeating operations in R  
  • For loops can be nested in R  
  • Implicitly repeating operations in R  
  • Repetitively apply actions in R  
  • Repetitively apply user defined functions in R  
  • Repetitively apply anonymous functions in R  
  • Repetitively apply actions on vectors in R  

Module 9: Implicit Control Flow in R

  • Bring in data from a file into R  
  • Saving and loading all objects in R  
  • Interacting with clipboard in R  
  • Connecting to files in R  
  • Reading from a file in R  
  • Writing to a file in R  

Module 10: Plotting in R

  • Basic plots in R  
  • Types of lots in R  
  • Graphical parameters of plots in R  
  • Modifying a plot in R 

Course Summary

The last section offers a brief summary, and provides you with some next steps in your journey in R.  

Feedback 

We welcome all feedback and suggestions - please contact us at qa.elearningadmin@qa.com to let us know what you think.  

Certificate

Your certificate for this learning path
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Learning Path Steps

1courses

This module will introduce you to the R programming language and the RStudio Integrated Development Environment. You’ll also look at some useful tools available in RStudio

2courses

This module introduces you to some of the basics of how to interpret data with R.

3courses

This module introduces you to the some of the basic data structures that can be used in R

4exam-filled

Knowledge Check: Fundamentals of R, Part 1 of 3

5courses

This module looks at more complex data structures, building on what was covered in the beginner data structures module.

6courses

This module looks at more complex data structures, building on what was covered in the intermediate data structures module.

7courses

This module looks at functions, how to create functions, and how they can be used in R.

8exam-filled

Knowledge Check: Fundamentals of R, Part 2 of 3

9courses

This module looks at more operators, and introduces conditional statements in R

10courses

This module looks at conditional statements in R, such as for loops and how to repeat functions.

11courses

This module looks at how to control data in R, through reading, writing and loading objects in R.

12courses

This module looks at how to plot data in R. It builds on the knowledge you have learned in the previous modules to show you how to use R to interpret your data.

13description

Modifying a plot in R

15courses

The last section offers a brief summary, and provides you with some next steps in your journey in R.

16exam-filled

Knowledge Check: Fundamentals of R, Part 3 of 3

About the Author

Students282
Labs1
Courses11
Learning paths1

Kunal has worked with data for most of his career, ranging from diffusion markov chain processes to migrating reporting platforms.  

Kunal has helped clients with early stage engagement and formed multi week training programme curriculum. 

Kunal has a passion for statistics and data; he has delivered training relating to Hypothesis Testing, Exploring Data, Machine Learning Algorithms, and the Theory of Visualisation. 

Data Scientist at a credit management company; applied statistical analysis to distressed portfolios. 

Business Data Analyst at an investment bank; project to overhaul the legacy reporting and analytics platform. 

Statistician within the Government Statistical Service; quantitative analysis and publishing statistical findings of emerging levels of council tax data. 

Structured Credit Product Control at an investment bank; developing, maintaining, and deploying a PnL platform for the CVA Hedging trading desk.