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Sublime Text is a powerful text editor. Here I introduce how to add custom REPL config for remote/local R, Python, Scala, Spark, Hive, you name it (this is only tested for OS X).
The example below interprets local Python (top), R (middle) and Hive (bottom) code on remote.

Good IDEs are everywhere. RStudio for R, Pycharm for Python, IntelliJ for Scala. But these are specialized in one language and what I wanted to have is a tool for broad and multi-language data science tasks with autocompletion, FTP, highlighter, formatter, split editing, local/remote evaluation and REPL. I especially wanted REPL functionality that takes single inputs, evaluates them, and returns the result for quick prototyping. Sublime Text is a popular text editor with massive amount of plugins that provides quite a lot of what you need. By adding custom REPL, Sublime Text becomes an all-in-one tool for every data science task from hadoop ETL, Spark machine learning, HTML/CSS editing to markdown reporting.

Good IDEs are everywhere. RStudio for R, Pycharm for Python, IntelliJ for Scala. But these are specialized in one language and what I wanted to have is a tool for broad and multi-language data science tasks with autocompletion, FTP, highlighter, formatter, split editing, local/remote evaluation and REPL. I especially wanted REPL functionality that takes single inputs, evaluates them, and returns the result for quick prototyping. Sublime Text is a popular text editor with massive amount of plugins that provides quite a lot of what you need. By adding custom REPL, Sublime Text becomes an all-in-one tool for every data science task from hadoop ETL, Spark machine learning, HTML/CSS editing to markdown reporting.

gif setup is heavily based on the following two packages

SublimeREPL
enables REPL inside Sublime Text

origami
adds flexible pane layout

When you have them installed, the only thing you need to do is to add a new SublimeREPL config.

Add custom SublimeREPL config

First thing you need to do is to find out SublimeREPL config file location. Most probably you can find it by Sublime Text tab, Preferences, Brouse Packages, SublimeREPL, then config (I use OS X 10.10.5).

As an example, we are going to add remote_R sublime interepreter config. This should be useful when you have local R script and like to evaluate it remotelly.

You’ll find two files in the folder.
Now we are going to give SublimeREPL access to your remote R (I assume you can SSH to your remote).
Open the files and edit some lines as shown below.
Find the lines with #CHANGE , those are the ones I changed. You’ll see caption and id are no longer “R” but “remote_R”. And cmd now accesses to your remote R .

If you copy & paste the below code, don’t forget to delete #CHANGE blah blah comment. JSON doesn’t accept comments

Main.sublime-menu

The above example is only to add remote R but basically the same procedure to add Python, Scala, Spark, Hive, Impala and technically anything on remote.
You might need to do a little trick in some cases so I also added config for Python and Hive to my Github page as an example.

The above example is only to add remote R but basically the same procedure to add Python, Scala, Spark, Hive, Impala and technically anything on remote.
You might need to do a little trick in some cases so I also added config for Python and Hive to my Github page as an example.

Detecting fraudulent transactions is a key applucation of statistical modeling, especially in an age of online transactions. R of course has many functions and packages suited to this purpose, including binary classification techniques such as logistic regression.

If you'd like to implement a fraud-detection application, the Cortana Analytics gallery features an Online Fraud Detection Template. This is a step-by step guide to building a web-service which will score transactions by likelihood of fraud, created in five steps:

Detecting fraudulent transactions is a key applucation of statistical modeling, especially in an age of online transactions. R of course has many functions and packages suited to this purpose, including binary classification techniques such as logistic regression.

If you'd like to implement a fraud-detection application, the Cortana Analytics gallery features an Online Fraud Detection Template. This is a step-by step guide to building a web-service which will score transactions by likelihood of fraud, created in five steps: