I was exploring Jupyter notebooks , that combines live code, markdown and data, through Microsoft's implementation, known as MS Azure Notebooks , putting together a small library of R and F# notebooks . As Microsoft's FAQ for the service describes it as : ...a multi-lingual REPL on steroids. This is a free service that provides Jupyter notebooks along with supporting packages for R, Python and F# as a service. This means you can just login and get going since no installation/setup is necessary. Typical usage includes schools/instruction, giving webinars, learning languages, sharing ideas, etc. Feel free to clone and comment... In R Azure Workbook for R - Memoisation and Vectorization Charting Correlation Matrices in R In F# Charnownes Constant in FSharp.ipynb Project Euler - Problems 18 and 67 - FSharp using Dynamic Programming
Project Euler (PE) provides some statistics, but I wanted to see the effectiveness of the languages in solving problems. Although PE lists Mathematica as number 1, and on balance of popularity and effectiveness it might be, it is not the language used to solve the highest proportion of problems. For that, the language and programmers of the following are most effective.
For comparison, the results of a few other oher languages
The entire list (Zipped Excel files): ProjectEulerStats.zip
- Frink (43%)
- PARI/GP (28%)
- Magma (21%)
- MUMPS (18%)
- Mathematica (17%)
For comparison, the results of a few other oher languages
- Haskell (11%)
- Python (10%)
- Perl (10%)
- Ruby (9%)
- F# (9%)
- Scala (8%)
- C/C++ (8%)
- Java (8%)
- C# (8%)
The entire list (Zipped Excel files): ProjectEulerStats.zip
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