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4. Intermediate Data Structures in R

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Contents

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Fundamentals of R
1
Objects in R
PREVIEW2m 34s
2
Integers in R
PREVIEW2m 1s
8

The course is part of this learning path

Fundamentals of R
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11
certification
3
lab-steps
1
description
1
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Objects in R
Overview
DifficultyIntermediate
Duration40m
Students36
Ratings
5/5
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Description

Course Description 

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

Learning Objectives 

The objectives of this module are to provide you with an understanding of: 

  • Different data types  
  • Integers  
  • How to coerce elements, and force coercion  
  • How to construct a matrix  
  • How to construct an array 
  • How to construct a list 

Intended Audience 

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

Pre-requisites 

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 

Understanding mathematical concepts will be beneficial 

Feedback 

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

Transcript

- [Narrator] It is said that everything in R is an object. There are some basic data types that form the advanced data structures that we can have in R, so let's start by explaining what they are. The first would be characters such as strings. Complex numbers, which are made up of real and imaginary numbers. The numeric data type forms either real numbers or decimal numbers. Specifically we can have integers if we add capital L to an element. And finally logical data types, which include Booleans such as true, false. Let's examine some of the features of these objects. If I was to enter in the number two, and ask for the class of this, I can find out the class of the object by using the class function, informing me that this is a numeric. I may be inclined to know how it is being stored or what the type of this data is, I could ask for the type of this. I can also ask for a feature such as the length. Now in this case we have a vector of one in number of entries, i.e. the dimensions or how long this item is, so we'll see a return of one. I can repeat this for say for example a vector ranging from one to three, and we can say what is the class of this vector. It's the same as if I was using A the class of the number two, or the class of the vector of integers. Again, with the type we have two being a type of two double, and the type of the vector is also double, 'cause we're asking for the type of the data. If I was to ask now for the length, this may be showing us a different answer, because this will ask for the length of the entire vector, which would return three.

About the Author
Students400
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.