Publication of the presentation at the section of R meetup @ Moscow Data Science Major (Spring 2019) .
The whole presentation is in pdf format .

Why is this question relevant?

Business cases are different, the technical essence is the same

• Call Center Performance Analytics
• Sales analytics, including forecasts
• Antifraud system
• Business process mining
• Various audits (technical, financial)
• Warehousing and logistics tasks
• Activity-based costing
• Business process monitoring
• Log-based analytics
• Capacity management
• Text analytics (e-mail, service-desk)
• Flexible dashboards and reports
• "smart tires" between accounting systems (1C, ACS, SAP, ...) and executive
• ...

It is a continuation of previous publications .

Practical observations

• a lot of such tasks come down to mathematical manipulation of data (CRUD systems are beyond the scope, we consider precisely various processing and transformation);
• 80% of data manipulation tasks can be quickly and efficiently solved "turnkey" by using the R tools;
• in business, as a rule, tasks and requirements are quickly adjusted, incl. due to external factors or intermediate results obtained;
• "modular" technologies take root well in IT; the construction of the "monolith" may take 2-3 years, which is comparable to the life of a small solution. It is much more efficient to quickly assemble a “modular” design, accumulate practical experience, and after 2-3 years build a new solution taking into account the knowledge gained and past changes in IT and business.

Typical “urban legends” about R

• R slow
• R hard to read
• R is for stat. calculations by complex algorithms
• R is designed for interactive use.

All this arises from a superficial study of the topic and the tools used.

City legends - misconceptions from the 90s

• R is a complete programming language, not a console calculator.
• R acts well as a universal “glue” between various platforms and C components - it counts quickly!
• The readability of the code depends on the experience of the developer. The modern style of R is metaprogramming. The code is compact and fast.
• R is an ecosystem that allows you to implement a complete data processing cycle from importing data to providing AWPs and preparing presentations.

Previous publication - "Using the computing power of R to test the hypothesis of equality of means . "