Adding comments in R

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Fundamentals of R
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DifficultyIntermediate
Duration21m
Students159
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Description

Course Description 

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 

Learning Objectives 

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

  • How to download and install the R programming language  
  • How to download and install the RStudio IDE  
  • The different panes in RStudio 
  • How plots are formed in RStudio 
  • How to add comments in RStudio  
  • Useful keyboard shortcuts in RStudio  

 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 

Having an understanding of 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

- [Instructor] How to add comments in R. Anything to the right of a hashtag symbol is ignored by R. The benefit of this would be to add comments to your code, add notes to your code, add instructions to the user of your code. The hashtag symbol is also known as a comment, a pound symbol, a number symbol. For example, in our console window, if I was to type in, print one through to 10, then it would return something to the screen text output. I might want to add a helpful hint to my user of such code to inform them that what is gonna happen at the end of running this code. In the same way, I might want to have a plot from one through to 10, against one through to 10, this produces a plot in the output window. Now I might want to add in a comment to that to tell me, or to tell my user of this code, that I have a graphical output.

About the Author
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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. 

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