6: Conclusions

ISMB 2022 Madison

Author

Claudia Solis-Lemus and Douglas Bates

Published

2022-07-30

Today we learned

  • Julia provides many advantages to data science programmers especially those creating programs that need to be efficient and that will be shared with the scientific community
  • Julia allows programmers to easily write good performant code and avoid the two language problem

Among the main Julia tools, we focused on five:

1. Data tools:

  • Arrow.jl: memory, layout, data frame, binary form. The binary form allows for cross-platform use (julia, R, python). Need to be careful going from Julia to R.
  • Tables.jl: generic idea of data table; row oriented (vector of named tuples) or column oriented (named tuple of vectors).
  • DataFrames.jl: cheatsheet similar to tidyverse.

2. Model fitting:

3. Communications with other systems:

4. Package system

  • With Julia 1.7+, precompilation is done when the package is added
  • Multiple biology-oriented packages in BioJulia

5. Tuning performance

6. Plotting

7. Literate programming

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