R that originated as a purely statistical tool has meanwhile blossomed into a comprehensive workbench for different tasks.

I am familiar with Mathematica and don't like how it forces a license on you. Also the handling of big data and outputting of tables is quite cumbersome in my opinion.

Meanwhile R offers via different packages (on top of the classic statistical tools)

• Some support for object oriented coding approach
• Monte Carlo generators galore
• Optimization packages
• Numerical Integration
• …

Are there still areas where R can’t compete with the big players ?

R that originated as a purely statistical tool has meanwhile blossomed into a comprehensive workbench for different tasks. I am familiar with Mathematica and don't like how it forces a license on you. Also the handling of big data and outputting of tables ins quite cumbersome in my opinion.

Meanwhile R offers (beside the statistical tools) via different packages

• Some support for object oriented coding approach
• Monte Carlo generators galore
• Optimization packages
• Numerical Integration
• …

What functionalities is R still lacking that one needs for everyday quant work? Here "functionalities" also encompass interfaceablity with C++, C#, speed and ease of use.