Advantages of Using R Notebooks for Data Analysis Instead of Jupyter Notebooks

Yes, ESS is fantastic. The only thing I envy about Rstudio is the GUI to save plots.

My only complaint about org mode is that nobody seems to want to make caching work with noweb references even though it's an old issue.

IMHO R notebooks, jupyter and their ilk are essentially emacs for the masses.. which is neither good nor bad, but is.

The disadvantage is collaboration; I don't know of anyone that uses a non-Emacs Org implementation, and adopting an editor/religion/lifestyle just to collaborate on a project is a huge ask. I find that in Emacs, ESS + polymode works great for rmarkdown files, providing many of the benefits, in a file that will also be interpretable in Rstudio.

Reproducible research must be reproducible by the unenlightened, after all!

Out of curiosity, what was the motivation for switching from Python to R for analysis, was there a particular R package that you were looking to use?

The thing that makes me sad about R is textmining. TM makes me sad, strings-as-factors makes me very sad. But maybe I'll try tidytext...

why do many say 'serious' work gets done in Python? R is great for linear models, but I find it tedious for many other things such as machine learning. However, I wouldn't classify that as 'serious', just that I find one performs better for different tasks.

its already been said, but I do NLP a lot. R handles text poorly. humans use a lot of text.

tensorflow, neural networks, etc is better in Python

between pandas, list comprehensions, python collections library, sklearn, spyder, I feel I have a lot of power at my finger tips and its easy to do most of the machine learning I want.

importing a package takes a meaningful amount of time in R. Several seconds, that is just unacceptable.

its a personal matter, but R has syntaxes that get on my nerves. python list: a = [1,2,3] a = c(1,2,3). perhaps its because i used other languages before, but my fingers are more adept at hitting [ which requires no shift compared to (. some people love curly braces and lots of parentheses in if/for statements, I appreciate them not being there.

I have to fight with R on scientific notation, always copy - pasting into my code: options(scipen=999)

that said, spyder is buggy, and R studio is fantastic. I still haven't come across a good python IDE that is par with R studio.

edit: I forgot to say, I feel pyspark is far superior to sparkr. last i seen, sparkr only works with a VERY old version of spark. I dont even think that version is supported anymore. this is a bit of a big deal to me

I'm not a python or R dev (Scala dev actually whose day job consists substantially of re-factoring python and R code written by data scientists and data analysts to run on the JVM on prod servers). Sure python is easier to grok for up-stream ETL/data processing, but that's commodity work (or it should be anyway) and not a solid basis to compare R vs python. R has far more packages than the "scientific" python portion of pypi and for certain domains the quality (and quantity) of the packages in R makes the better choice; examples: signal processing (or any more-than-routine time-series analysis; seismic interpretation, finance, experimental design, chemo-metrics, etc. And with strict use of the datatables package--coercing dataframes as datatables and using that package's syntax to manipulate your data, R is very fast. Ignore the smack and leave those folks to their "serious" work

Yeah that sounds like something that should at least be possible to fix pretty easily using css max height and an overflow=scroll setting.

Jupyter now has output rate limitations (I pushed for them to add them), though I think they may be off by default. I also implemented something better (I think), which is an output buffer, for CoCalc's version of collaborative Jupyter notebooks. Instead of rate limiting, CoCalc saves only the last part of the output, discarding earlier output, and provides a link to get it.

Which I find leads you to Python, then Julia, then bracket. /s

One of the best things for R is the mailing list. At leasr when I was learning stats and R rhe knowledge both of how to do stuff in R and what math to use when was phenomenal. If gentle people didnt answer in time Prof Brian Ripley from Oxford would answer early morning British time and explain why your question was wrong and what the math meant and what you really meant to do and why and then 3 lines of R to do it.

Re: matplotlib

You have other options like bokeh and plotly

I am currently going through this free book : https://www.openintro.org/stat/textbook.php?stat_book=os

What's your background and what exactly do you mean by modern methods? An Introduction to Statistical Learning is good and you can download the pdf: http://www-bcf.usc.edu/~gareth/ISL/ it assumes you have a pretty decent background in mathematics though.

Each field has their own "good practical statistics book". I work in finance and so recommend Fabozzi. It's good, but so are many other foundational texts. Your requirement for practicality necessarily negates a one true answer.

It will depend a lot on your field, but a solid grasp of fundamental probability theory should be applicable everywhere.

I think this is an excellent overview [1]. Learning probability from a measure theory angle is more difficult to grok compared to the frequentist approach everyone is more familiar with, but I found it much more enjoyable. (I learnt the usual way from doing computer science undergrad, but now re-doing it more rigorously for masters in financial engineering)

[1]: http://www.math.uah.edu/stat/index.html

For statistical programming, since we're talking about R, I strongly recommend R for Data Science (http://r4ds.had.co.nz) by Hadley Wickham (who created a large amount of the R packages that are very commonly used [tidyverse] and incidentally also now works for RStudio)

A good book on statistical theory is harder to come by, though.

Introduction to Statistical Learning is free and quite good: http://www-bcf.usc.edu/~gareth/ISL/

Follow it up with Elements of Statistical Learning by three of the same authors for more advanced stuff.

Perhaps not quite on topic, but introductions to statistics which take a Bayesian approach are starting to exist. Like http://xcelab.net/rm/statistical-rethinking/ or perhaps https://github.com/equinn1/MTH225_Spring2016 .

Writing a bunch of scripts can quickly become a mess. I was working on some twitter analysis for a project, and not really worrying about the code because I didn't intend for it to be used again, and it quickly became a mess of "run this script, then run that script on the generated file, then use this shell command to process the file, then run the final analysis step on that file, then clean up all the intermediates". Not to mention, say, "one-time" data cleanup through the shell / REPL that runs into problems months down the line when you want to update the data set. And, of course, invariably none of this is documented. Notebooks don't force you to organize your code and write documentation, but they strongly encourage it.

Closest alternative to RStudio (on principle, if not in practice): http://rodeo.yhat.com