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Back in , one of the first advantages that made me play around with Emacs again when I went back to graduate school was Emacs-Speaks-Statistics. It allowed me to avoid the pain of using the R-console which was frankly a miserable experience. The deficiencies of that interface stood in stark contrast to the enormous benefits to be had by using a FREE and open source statistical computing environment where each function could be examined and verified.

Data analysis was no longer tied to a machine with a working license server. I could now work from home or even on my lengthy commute from Coney Island to school in Manhattan. Three dead hours of my day now became my most productive time.

Although I do not have the resources to donate to the project I have cited it in my scientific work and encourage others who have used it to do the same. Although R has continued to improve since I started using it.

The Python Data community has truly blossomed, albeit from a much lower base. Tools like numpy, scipy networkx, NLTK, sympy, pandas, rpy2 and particularly IPython have made Python a formidable competitor in the scientific computing space.

In fact I am not going to give links to the source code because best practice is to install them in a virtual environment using pip. The best instructions I have found for Linux are here.

I will write a tutorial for Mac and Windows next week. The reason why IPython has had such a profound impact are human rather than technical. IPython prints detailed error messages where as R prints error messages that are cryptic at best. Python has a vibrant community with numerous initiatives to reach under-served, under-computed and under-represented groups including PyLadies and other-groups.

R help is notoriously caustic. Funny you should ask. That is 63 papers cited at least 63 times. I took a less sophisticated approach. I read their posting-guide and answers and I vowed to never ask a question. If I want to be abused like that I will go find a job on a trading desk.

But searching the archives and other sources, I got by. I discovered the notebook from this post on R-bloggers back in November The browser was a great way to show work across various operating systems. But whoa, did this mean I actually had to edit in the browser. Surely we can do better. He made the IPython Notebook a mode in Emacs. If you have not set it up, please read my tutorial.

I am looking at data that is simply to big for my laptop. I needed to run IPython remotely on an amazon ec2 instance but edit the interactive session locally. These servers have no windowing software x11 and it violates the terms of service to install it.

There were a few choices. Start on your machine. Good directions are here. It is worth stating, the local machine is your laptop and the remote is the server you are using.

The result is that establishing an ssh connection should be as easy as:. See my issue on the ein repo to set your ein: The directions are in this repo.

If you are using a virtual environment, the configuration will be in: Congratulations, you now have an ssh connection to your notebook on a remote server in local emacs. And you know what, that my friend, does not suck at all.