Publication on the performance of the section R meetup @ Moscow Data Science Major (Spring 2019) .
The entire presentation in pdf format .

Why is this question relevant?

Business cases are different, the technical essence is the same.

• Call Center Analytics
• Sales Analytics, including forecasts
• Antifraud system
• Business process mining
• Various audits (technical, financial)
• Warehouse 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

• very many such tasks are reduced to mathematical manipulation of data (CRUD systems beyond, we consider various processing and transformation);
• 80% of data manipulation tasks can be quickly and efficiently solved on a turnkey basis by using the R toolkit;
• in business, as a rule, tasks and requirements are quickly adjusted, incl. due to external factors or intermediate results obtained;
• “modular” technologies are well adopted in IT; building a "monolith" can take 2-3 years, which is comparable to the life of a small solution. It is much more effective 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 the past changes in IT and business.

Typical “urban legends” about R

• R slow
• R hard to read
• R is for stat. calculations using complex algorithms
• R is designed for interactive work.

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

Urban Legends - Fallacies from the "90s"

• R is a full-fledged programming language, not a console calculator.
• R acts well as a universal “glue” between different platforms and C components - it counts quickly!
• Readability of the code depends on the developer’s experience. Modern style R - metaprogramming. The code is compact and fast.
• R is an ecosystem that allows you to implement a full data processing cycle from data import to the provision of AWP and the preparation of presentations.

Previous publication - "Using computational capabilities of R to test the hypothesis of equality of averages . "