HTTP.jl VS DataFramesMeta.jl

The req.url field contains the URL of the received request, the req.method field contains request method, like GET or POST, the req.body field contains the POST body of the request in binary format. HTTP request object contains much other information. All this you can find in HTTP.jl documentation. Our web application will only check the request method. If the received request is a POST request, it will parse req.body to JSON object and send the data from this object to the isSurvived function to make a prediction and return it to the client browser. For all other request types, it will just return the content of the index.html file, to display the web interface. This is how the whole source of titanic.jl web service looks:

If you want to just get the html of a website whose url you already have you can make requests from the http.jl package. https://juliaweb.github.io/HTTP.jl/stable/

HTTP.jl and Gumbo.jl for web-scraping

I haven't seen that tutorial, but I agree that HTTP.jl, Gumbo.jl, and Cascadia.jl are the way. I used them to export public wishlists from bookdepository, which has no API nor a built in exporting tool.

I find some of the library documentation hard to understand. Compare http.jl with python's requests, for example. Something as core as HTTP requests should have clear docs with tonnes of examples. Part of this is also a personal dislike of documenter.jl styling. Idk why the contrast is so low – would prefer a standard readthedocs theme.

A package: https://github.com/JuliaData/DataFramesMeta.jl

I'd also heartily recommend DataFramesMeta which provides really nice macros for manipulating dataframes.

I do get the feeling though that Python people are considered “more sophisticated” as programmers than R. But I think Julia is gaining traction now and it can handle general programming tasks better than R can, while still remaining pretty similar so its worth learning too. It has DataFramesMeta.jl: https://github.com/JuliaData/DataFramesMeta.jl. Works like dplyr.