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.
What makes RStudio so unique? Comparing with other IDEs, what might be surprising is that you see the console below the code. You have the ability to have graphical outputs such as contained inside the plot window on the bottom right. Whilst most IDEs would probably have text outputs. The visualisations you might see tend to fall within the RStudio environment whilst most IDEs would have this as a popup or an independent file. The Environment Explorer is live, so it's showing you what you have in memory as of this moment in time.
If I wanted to have text output I can in terms of if I click on the console and I type in print and I ask for the numbers one, three till 10 I can see texts output on the screen. I could also have within my same coding environment, a plot of the numbers one to 10 against the numbers one to 10. And as you can see, RStudio manages to produce the same graphical outputs within the same environment as the text outputs.
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.