Fundamentals of R
The course is part of this learning path
This module introduces you to some of the basics of how to interpret data with R.
The objectives of this module are to provide you with and understanding of:
- How to use calculator operations in R
- How to store results with labels
- The difference between print and cat in R
- How libraries can be installed in R
Aimed at all who wish to learn the R programming language.
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
We welcome all feedback and suggestions - please contact us at firstname.lastname@example.org to let us know what you think.
- [Instructor] Transmitting to an output device involves transmitting, usually to a screen, and displaying items. We can use two functions, one being print, and I can print pretty much anything that I'd like. I can create and assign a variable, X, and I can choose to print this to the output device, being the screen. I could also, if I wanted to, I could view X, but that would bring up my date of viewer. So this is a different part of the interface available with our studio. Another function I might use instead of print is, if I had multiple pieces of strings that I wanted to put together, I could use CAT function, short for concatenate. I can do, any thing testing one two three, to mix up strings and integers. I could also have many strings connected via a comma. And as a final concluding thought, after having assigned a variable, instead of having assigned a variable, four, five, six, seven, eight, nine, to X, I could use, brackets, round brackets, outside of this expression to print this directly onto the screen at the same time as creating that variable.
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.