4.9 Use the R Interpreter in a Notebook Paragraph

An Oracle Machine Learning notebook supports multiple languages. Each paragraph is associated with a specific interpreter. To run R functions in an Oracle Machine Learning notebook, you must first connect to the R interpreter.

In an Oracle Machine Learning notebook, you can add multiple paragraphs, and each paragraph can be connected to different interpreters such as R or SQL or Python. You identify which interpreter to use by specifying % followed by the interpreter to use: sql, script, r, python, conda, markdown.

This example shows how to:
  • Connect to the R interpreter to run R commands in a notebook.
  • Verify the connection to Oracle Autonomous AI Database, and
  • Load the ORE libraries
  1. To connect to the R interpreter, type the following directive at the beginning of the notebook paragraph, and press Enter:
    %r
  2. To verify the database connection, type the following command and press Enter:
    ore.is.connected()
    Once your notebook is connected, the command returns TRUE, as shown in the screenshot here. The notebook is now connected to the R interpreter, and you are ready to run R commands in your notebook.

    Figure 4-79 Command to Test Database Connection


    Description of Figure 4-79 follows
    Description of "Figure 4-79 Command to Test Database Connection"

  3. To import R Libraries, run the following commands: 
    library(ORE)
library(OREdplyr)
    Once the packages are loaded successfully, the list of ORE packages are displayed as shown in the screenshot here. Scroll down to view the entire list.

    Figure 4-80 Command to Import R packages


    Description of Figure 4-80 follows
    Description of "Figure 4-80 Command to Import R packages"

  • About Oracle Machine Learning for R
    Oracle Machine Learning for R (OML4R) is a component of the Oracle Machine Learning family of products, which integrates R with Oracle Autonomous AI Database.

Parent topic: OML Notebooks

4.9.1 About Oracle Machine Learning for R

Oracle Machine Learning for R (OML4R) is a component of the Oracle Machine Learning family of products, which integrates R with Oracle Autonomous AI Database.

Oracle Machine Learning for R makes the open source R scripting language and environment ready for enterprise and big data. It is designed for problems involving for both large and small data volumes. OML4R allows users to run R commands and scripts for statistical, machine learning, and perform visualization analyses on database tables and views using R syntax.

Oracle Machine Learning for R is available in Oracle Machine Learning UI, currently available through Oracle Autonomous AI Database, including Autonomous AI Lakehouse, Autonomous AI Transaction Processing, and Autonomous AI JSON Database. Oracle Machine Learning for R Embedded R Execution functionality can be deployed through SQL and REST APIs on Autonomous AI Database.

Use Oracle Machine Learning for R to:

  • Perform data exploration and data preparation while seamlessly leveraging as a high-performance computing environment.
  • Run user-defined R functions on database spawned and controlled R engines, with system-supported data-parallel and task-parallel capabilities.
  • Access and use powerful in-database machine learning algorithms from R language.

To use the R interpreter, specify the %r directive at the beginning of the paragraph. The following R packages are installed to support Oracle Machine Learning for R.

Supported Oracle Machine Learning for R Proprietary R Packages

The supported Oracle Machine Learning for R proprietary R packages are:

  • ORE_1.5.1
  • OREbase_1.5.1
  • OREcommon_1.5.1
  • OREdm_1.5.1
  • OREdplyr_1.5.1
  • OREeda_1.5.1
  • OREembed_1.5.1
  • OREgraphics_1.5.1
  • OREmodels_1.5.1
  • OREpredict_1.5.1
  • OREstats_1.5.1
  • ORExml_1.5.1

Supported Open Source R Modules

The following open source R packages are supported by Oracle Machine Learning for R:

  • R-4.0.5
  • Cairo_1.5-15
  • ROracle_1.4-1: DBI_1.1-2
  • arules_1.7-3
  • png_0.1-7
  • randomForest_4.6-14
  • statmod_1.4-36
  • dplyr_1.0-9:
  • R6_2.5.1
  • assertthat_0.2.1
  • cli_3.3.0
  • crayon_1.5.1
  • ellipsis_0.3.2
  • fansi_1.0.3
  • generics_0.1.2
  • glue_1.6.2
  • lazyeval_0.2.2
  • lifecycle_1.0.1
  • magrittr_2.0.3
  • pillar_1.7.0
  • pkgconfig_2.0.3
  • purrr_0.3.4
  • rlang_1.0.2
  • tibble_3.1.7
  • tidyselect_1.1.2
  • utf8_1.2.2
  • vctrs_0.4.1

Oracle Machine Learning for R Interpreter Requirements

The R interpreter requires the following open source R packages:
  • Rkernel 1.3:
    • base64enc 0.1-3
    • cli 3.3.0
    • crayon 1.5.1
    • digest 0.6.29
    • ellipsis 0.3.2
    • evaluate 0.15
    • fansi 1.0.3
    • fastmap 1.1.0
    • glue 1.6.2
    • htmltools 0.5.2
    • IRdisplay 1.1
    • jsonlite 1.8.0
    • lifecycle 1.0.1
    • pbdZMQ 0.3-7
    • pillar 1.7.0
    • repr 1.1.4
    • rlang 1.0.2
    • utf8 1.2.2
    • uuid 1.1-0
    • vctrs 0.4.1
  • knitr 1.39:
    • evaluate_0.15
    • glue_1.6.2
    • highr_0.9
    • magrittr_2.0.3
    • stringi_1.7.6
    • stringr_1.4.0
    • xfun_0.31
    • yaml_2.3.5

Parent topic: Use the R Interpreter in a Notebook Paragraph