Data Science package plus SparkR library and PySpark package.Īfter creating my workspace, I launched it in R-Brain IDE (RIDE). Intrinsic package plus pandas, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, cloudpickle, dill, numba and bokeh for Python, and forecast, caret and randomforest for R. SQL, R, Python 3 and Julia with Gadfly, RDatasets and HDF5. Essential packages to have all you need for running R, Python, Shiny, ggplot and RMarkdown. For my hands-on evaluation, I created a new Data Science workspace. R-Brain currently offers four base workspace types: Intrinsic, Data Science, TensorFlow, and Spark. Customized workspace distribution is quite useful for teachers or enterprises that want to standardize analytics environments. If someone had shared an analytics workspace with me, it would have been listed as an available workspace in my Inventory. I simply registered to create an account and then selected options for my workspace. It took me three minutes to set up my environment. Getting started with R-Brain is quick and easy.
Additional data science platform and kernel comparison matrices are also available. Here is an IDE comparison provided by R-Brain. Although a few data science providers have similar features, R-Brain’s IDE provides quite a bit more functionality. With R-Brain, analytics projects across different languages can be easily organized and managed together in one common workspace. For example, you might use RStudio for R, Jupyter(iPython notebooks), Anaconda, P圜harm, Spyder, or Apache Zeppelin for Python, a SQL IDE like TOAD, SQL Database Studio, MySQL Workbench, SQL Server Management Studio (SMSS) or Visual Studio for SQL, and a text editor IDE such as Notepad++ or Emacs. Today most data analysts and data scientists have a toolbox filled with a mix of utilities for Python, R, SQL, and other script languages.
R-Brain also makes it easy to interactively navigate database schemas, view table content and export data. Data gurus can develop, package, share and publish analytics workspaces, data sets and applications that use R, Python, Structured Query Language (SQL) scripts. Since it uses popular Docker container technology, this solution can be deployed on-premises or on your preferred cloud platform. It offers all the familiar building blocks of the classic Jupyter Notebook (interactive notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible, powerful user interface. R-Brain improves multi-language data analysis productivity.