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Hai Qian edited this page Jul 3, 2013 · 25 revisions

This page is for master branch only.

Newest features in master

  • sample with or without replacement

  • generic.cv for cross-validation

  • generic.bagging for bagging

PivotalR is an R package, which you can download from CRAN. However, GitHub has the latest code, which has many more functionalities but is less stable.

Big Data

PivotalR is an R front-end to PostgreSQL and all PostgreSQL-like databases like Pivotal Inc.'s Greenplum Database (GPDB), Pivotal HD / HAWQ.

When running on the products of Pivotal Inc., PivotalR utilizes the full power of parallel computation and distributive storage embedded in Greenplum Database or HAWQ, and thus gives the normal R user access to Big Data.

Machine Learning

PivotalR also provides the R wrapper for MADlib. MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine-learning algorithms for structured and unstructured data. The number of machine learning algorithms that MADlib covers is quickly increasing.

Easily Exploring Data Using the Familiar R Syntax

PivotalR mimics the regular R syntax for manipulating data.frame to operate on the tables stored in the databases. We strive hard to make PivotalR's learning curve as smooth as possible.

Quick Prototype of Data-Parallel Machine Learning Algorithms

PivotalR enables the user to create prototypes of machine learning algorithms quickly using the regular R syntax. These prototypes acquire parallel computation power automatically when running on GPDB or HAWQ.

Thus, first copy your R script and then make proper changes to make the script runnable in PivotalR. The goal of PivotalR is to minimize the amount of changes that are needed to convert a normal R script to parallel R script.

See here for some examples.

Minimizing the data flow between R and databases

PivotalR class hierarchy structure

The demo script of PivotalR

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