Plenty of people use the REPL in terminal and sublime text or vim or whatever. I also dislike browser-based tooling and think Julia has done a good job avoiding Rstudio-style dependencies. But if your point is the inability to do `julia script.jl` , yeah thats a pain point. Fortunately there has been some tooling to make running many jobs in a row easier: https://github.com/dmolina/DaemonMode.jl
What do you mean by “RStudio-style dependencies”?
Just that there seems to be an expectation of using Rstudio when using things in R that seem generic.
One pain point is that Rmarkdown uses a different pandoc installation when executed by Rstudio than from the terminal.
Well, R isn’t the best language when it comes to building systems. Most R code is essentially one file written to produce an output once (for a paper, project, etc.). This means that people want a better language to build systems which Python fit. That explains why people moved to Jupiter. I don’t like RStudio for the same reason I don’t like Matlab. I already have my editor and terminal workflow. I don’t want to use…
> Well, R isn’t the best language when it comes to building systems. Most R code is essentially one file written to produce an output once (for a paper, project, etc.). This means that people want a better language to build systems which Python fit. That explains why people moved to Jupiter. I definitely agree that Python is a better general purpose computing language than R, but R's deployment story (i.e. packages)…
Yeah, I find that the out-of-order execution issue is common with people who have a software development mindset, but for data analysis/science is basically the only sensible way to work. The "load data" command might be one line but takes 3 minutes to run, while a huge chunk of code that plots the data might take 1 second and I might want to tweak it 50 different ways before settling on something that I like/delivers insight. Producing a standalone script that develops the same insight you get from "playing" with the data is an afterthought in some cases.
I really wish the Julia ecosystem would stop assuming that you always interact with your computer through the Julia REPL and started supporting proper command line interfaces. This is one of the big annoyances and mistakes of the R ecosystem, and I think it's unwise to carry that mistake over to Julia. Also, big "ugh" to browser-based tooling. I want to browse webpages in my browser, I don't want to do my data scienc…
> Well, R isn’t the best language when it comes to building systems. Most R code is essentially one file written to produce an output once (for a paper, project, etc.). This means that people want a better language to build systems which Python fit. That explains why people moved to Jupiter. I definitely agree that Python is a better general purpose computing language than R, but R's deployment story (i.e. packages)…
Yeah, I find that the out-of-order execution issue is common with people who have a software development mindset, but for data analysis/science is basically the only sensible way to work. The "load data" command might be one line but takes 3 minutes to run, while a huge chunk of code that plots the data might take 1 second and I might want to tweak it 50 different ways before settling on something that I like/deliver…
As long as you're aware of the dangers, it's fine. Personally I try to model offline from analysis to avoid this issue, and set eval to no in org for those cases where I've built the model inline with the analysis.
Unfortunately, it generally takes a couple of terrible situations before people learn the problems with this.
> Well, R isn’t the best language when it comes to building systems. Most R code is essentially one file written to produce an output once (for a paper, project, etc.). This means that people want a better language to build systems which Python fit. That explains why people moved to Jupiter. I definitely agree that Python is a better general purpose computing language than R, but R's deployment story (i.e. packages)…
Yeah, I find that the out-of-order execution issue is common with people who have a software development mindset, but for data analysis/science is basically the only sensible way to work. The "load data" command might be one line but takes 3 minutes to run, while a huge chunk of code that plots the data might take 1 second and I might want to tweak it 50 different ways before settling on something that I like/deliver…
I agree that data analysis needs a tool to persist data while iterating over certain functions. But in this vein, said tool should aim to try to prevent the user from having to run the load_data() function more than once. Not encourage it by allowing someone to permanently manipulate the output of load_data().
I also do not really get the complaint, but it is along the lines of people wanting to write `julia-pkg install Pluto` instead of `julia -e 'using Pkg; Pkg.add("Pluto")'`. It seems it is a big pet peeve for some people.
Thanks for the example. As someone who has never used Julia... Wow, that looks exceptionally painful compared to most other modern languages.
The package manager that comes with Julia is actually way better than what is available in python, and it has an unmatched "foreign language dependencies" support. It just happens to be mostly used from the REPL, not the command line (hence the execute -e flag above).
Yeah, I find that the out-of-order execution issue is common with people who have a software development mindset, but for data analysis/science is basically the only sensible way to work. The "load data" command might be one line but takes 3 minutes to run, while a huge chunk of code that plots the data might take 1 second and I might want to tweak it 50 different ways before settling on something that I like/deliver…
I agree that data analysis needs a tool to persist data while iterating over certain functions. But in this vein, said tool should aim to try to prevent the user from having to run the load_data() function more than once. Not encourage it by allowing someone to permanently manipulate the output of load_data().
This is an option in many tools, but it doesn't tend to work that well in practice.
I do agree that this is the ideal though (As an example if Pluto is always reactive, then this workflow becomes much more difficult as when you change a downstream datapoint, the model will be re-run).
I have played around with Pluto.jl, and colleagues of mine use it for research, but I keep going back to Jupyter. I tend to have long running cells that are pulling information from external sources or training models, and triggering one of those cells accidentally will waste a lot of time running something that may not be reliably interrupted. There is talk about putting in execution barriers that would help with th…