# What features will I miss in Julia?

**URL:** <https://discourse.julialang.org/t/what-features-will-i-miss-in-julia/3577>\
**Category:** New to Julia\
**Created:** [May 7, 2017, 12:47pm UTC](https://discourse.julialang.org/t/what-features-will-i-miss-in-julia/3577 "2017-05-07T12:47:58Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![ChrisRackauckas](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/chrisrackauckas/32/77_2.png) [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)\
**Post date:** [May 7, 2017, 1:40pm UTC](https://discourse.julialang.org/t/what-features-will-i-miss-in-julia/3577/3 "2017-05-07T13:40:46Z")

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> [@Juan](#):
>
> In R I use quite a lot data.table, ggplot, lattice, lme4, stan, knitr to create Rmd files, regular expressions and Rcpp.

I would say that in the current state of Julia, you won’t miss Stan since Stan.jl does a great job.

> **[GitHub - StanJulia/Stan.jl: Stan.jl illustrates the usage of the 'single...](https://github.com/StanJulia/Stan.jl)**
>
> Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc. - GitHub - StanJulia/Stan.jl: Stan.jl illustrates the usage of the 'single method'...

You won’t miss knitr because Weave.jl is great.

> **[GitHub - JunoLab/Weave.jl: Scientific reports/literate programming for Julia](https://github.com/JunoLab/Weave.jl)**
>
> Scientific reports/literate programming for Julia. Contribute to JunoLab/Weave.jl development by creating an account on GitHub.

You won’t miss regular expressions because Julia’s string macros are more powerful than anything you’d imagine. You definitely won’t miss Rcpp because that entire idea is eliminated: you just write Julia code and and get the C++ speed free. However, if you were using it to wrap a C++ package, you can use Cxx.jl:

> **[GitHub - JuliaInterop/Cxx.jl: The Julia C++ Interface](https://github.com/JuliaInterop/Cxx.jl)**
>
> The Julia C++ Interface. Contribute to JuliaInterop/Cxx.jl development by creating an account on GitHub.

And MixedModels.jl is written by the same guy who wrote the main R statistical packages:

> **[GitHub - JuliaStats/MixedModels.jl: A Julia package for fitting (statistical)...](https://github.com/JuliaStats/MixedModels.jl)**
>
> A Julia package for fitting (statistical) mixed-effects models - GitHub - JuliaStats/MixedModels.jl: A Julia package for fitting (statistical) mixed-effects models

As for the others, some who have come to Julia don’t think there’s quite a replacement to ggplot yet. Gadfly.jl uses a grammar of graphics syntax, but it’s limited. Plots.jl is a great plotting library, but it doesn’t use grammar of graphics except in some experimental addon:

> **[GitHub - JuliaPlots/GGPlots.jl: Grammar of Graphics interface to Plots.jl](https://github.com/JuliaPlots/GGPlots.jl)**
>
> Grammar of Graphics interface to Plots.jl. Contribute to JuliaPlots/GGPlots.jl development by creating an account on GitHub.

Also, DataTables/DataFrames are in flux right now:

> [@DataTables or DataFrames?](https://discourse.julialang.org/t/datatables-or-dataframes/3160):
>
> After a lot of reading, I’m still not certain about the state of data handling in Julia. My understanding is there is a migration over to DataTables due to type-safety considerations, however there are still a number of functions that are not supported? Will DataFrames eventually be deprecated completely?

Hope that’s a good overview of what to expect at this current stage. Julia, the core language itself, is much more developed than you’d probably expect, and the package ecosystem has many packages which are already more expansive than what you’d find anywhere else (especially in scientific computing, like optimization, differential equations (🙂), and linear algebra). However, I think those two points (and maybe a few others) are where we are still lacking some.

However, the point I’d like to end on is that, in Julia, I find it so easy to write efficient code that in many cases packages aren’t necessary in ways they are in other languages.

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