• Rapidly develop and deploy R scripts that work directly on database data
• Use scalable in-database machine learning algorithms, data preparation, data exploration, and statistical analysis from a natural R API
• Execute user-defined R functions in data- and task-parallel manner using database managed R engines from R and SQL APIs
• Use in-database R script repository and R object Datastore with user access privileges
• Include R-based results through Oracle Analytics Cloud, Oracle Data Visualization Desktop, and Oracle Business Intelligence dashboards

• Use Oracle Database as a high performance compute environment through an R interface
• Draw upon scalable in-database machine learning algorithms and statistical functions
• Operationalize R scripts in production applications and eliminate porting R code or reinventing deployment infrastructure
• Minimize data movement
• Use R packages contributed by the R community
• Automatically leverage existing database backup and recovery mechanisms and procedures
• Reach data from the Data Lake through Oracle Big Data SQL

Transparency layer - Leverage R data.frame proxy objects so data remains as database tables and views. Overloaded R functions translate select R functionality to equivalent SQL for in-database processing, parallelism, scalability and security. Data scientists can use familiar R syntax to manipulate database data that remains in the database. Leverage the package OREdplyr, which provides overloaded functionality from the popular open source R dplyr package.

Machine Learning Algorithms - R users can take advantage of Oracle Machine Learning’s library of in-database, parallel algorithms using the R language. Users can specify machine learning models using the familiar R formula syntax. Algorithms support classification, regression, anomaly detection, clustering, feature extraction, time series, and association rules. 

Embedded R Execution - Manage and invoke user-defined R functions in Oracle Database for data-parallel, task-parallel, and non-parallel execution, which may also use third-party R packages, e.g., from the CRAN repository. When data scientists require techniques from the R ecosystem to satisfy unique requirements, they can leverage the R ecosystem.

Integrated Text Mining - The in-database algorithms accept text columns from tables and views, and then automates term and theme extraction. The extracted data is combined with other predictors in building models and scoring data.

Partitioned Models - With in-database models, users can automatically create ensembles of models, where each component model is built on a user-specified partition of the data. Scoring is enabled and simplified using a single integrated model.