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 email@example.com to let us know what you think.
- [Instructor] At it's core, R is a very fancy calculator. We can place spaces around an operator, such as the addition operator, there's no need for an equal sign. By pressing enter, we can see the output of three on the screen. The spaces are not mandatory, however, they are best practice to help readability. One divided by four returns 0.25 The trailing zeros are suppressed as you would expect in a calculator, one divided by three, or one, space, divided by three, for readability. The default for this is, seven digits are displayed. The carrot symbol, is the exponential operator, in terms of the order of precedents, or the order of operations, in this formula here, we have the exponential first, followed by division, then addition. Raising 16 to the power, a half, and I will use brackets here to enforce parenthesis to be run first, returns four, that's the same as taking the square root of 16, large numbers are notated by scientific notation. If I write down 1e6 I'm asking for 10 to the power six, but if I was to take that number, divide it by four, raise it to the power, a half, without any parenthesis, the order of precedents in R, differs from that, the order of precedence in mathematics.
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.