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Comment by baldfat | Hacker News Reader

This is an unnecessary fight.

They both are great. I personally strongly prefer R over Python due to libraries and the flexibility. Others hate the flexibility of R and like Python's only one way to correctly do something.

It comes down to preferences. I like the R is for statistical specifically. There are people who call R an aweful horrible language and it isn't. It has grown incredibly over the last 5 years and has major funding from many corporations for a reason.

If it seems passionate for or against either language it is more an emotional response to the question. R works best for me and Python might be someone else's prefer solution.

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I agree, I like R and python for certain things. (python for most things and R for statistics and visualization).

However, I really don't agree with the "X works best for me but Y might work better for someone else so let's not fight" argument. Doesn't that imply that the question "Which is better, X or Y"? shouldn't be asked? On the contrary, I think that's often an important question when you need to practically get stuff done, choose between libraries, etc, and I want to know people's opinions.

> Doesn't that imply that the question "Which is better, X or Y"? shouldn't be asked?

No, it just implies that there is, in the domain in which it is applied, no objectively correct answer.

Just be warned I named my son Soren for the founder of Existential Philosophy Soren Kierkegaard. There is no spoon.

Here is my opinion on Python and R. I used Pandas and switched to R.

1) Python 0 based as opposed to 1 based is big to me. I don't want to always have to minus on on columns to call them.

2) I much prefer Hadley Wickham's syntax to Pandas which closely follows it, but R piping with %>% is amazing with library magritr. Using dplyr and stringr, tidyr, rvest, httr, luberdate are awesome and consistant.

3) In R I can use dplyr whcih is fast or even use data.table for even more speed that uses a sql like syntax. In Python your choices are limited in comparison to R for tools.

4) HUGE and the reason I switched to R. I can output my graphics into a editable PowerPoint slide. My managers and company doesn't know that there are anything besides MS Office and I can give them slides that they can fiddle with for their own presentations.

5) RMarkDown and knitr for outputing reports in Word (UGH but ti works great), PDF or , LaTex or HTML is fantastic. Reproducable output is great. If I want I can also use Project Jupyter (IPython's new name in next version) with a R kernel.

6) Tools - I got RStudio installed on my work computers, and my personaly server has RStudio Server installed. This is what I have settled into using because it is the best statistic programing tool out there. Works better than Jupyter/IPython for 90% of the time, though I love Love LOVE Jupyter/IPython.

7) The community. R's community is fantastic [http://www.r-bloggers.com/] just look at all the daily information, new libraries and tutorials. The books are great in R, (I really enjoyed the Python Pandas Book by Wesly) Hadley Wickham's Newest book is avilable in Physical form for purchase and free on the internet [http://adv-r.had.co.nz/]

8) Tutorials - Datacamp, Swirl in R tutorial, Coursera Courses, etc. are great and have just about anything you would like to learn.

9) The libraries and install system with Cran installs [http://cran.r-project.org/] and devtools to install GitHub libraries install and updated on your system [https://github.com/hadley/devtools]

I could go further but those are my opinions to why I like R so much.

Ultimately Julia combines the strengths of both and much more. It is the future IMO, not R or python.

way to miss the point. Part of a language selection for a real -world project is based on the libraries available for it. If I'm doing systems administration programming, there is a much higher probability of there being a python library to interact with BGP or namespaces than there is for Julia.

Selecting Julia blindly is just as stupid as selecting R or python blindly. Language features are only a small part of the reason you choose a language for a large project.

The Julia FFI for Python is absolutely excellent -- calling a particular Python library from Julia takes very, very little effort. As a language for scientific programming, Julia is way ahead of Python. So I'm not sure if this particular argument holds much water.

https://github.com/stevengj/PyCall.jl

I think it is a chicken or the egg problem with Julia right now. Julia is very much a positively viewed language that isn't being used much.

There are not enough libraries (I looked 3 months ago) for me to do my work in Julia.

There isn't enough users to make the libraries for my use case.

What is missing? If its something like shiny, Julia is well on way to obviating that problem: https://shashi.github.io/Escher.jl/

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