6: Conclusions
ISMB 2022 Madison
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:
- MixedModels.jl: 100% julia package
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
- quarto.org. These notes are rendered with quarto!
- Jupyter
- Pluto.jl
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