Using OML4R, you can prepare data for analysis in the database.

Data preparation is described in the following topics:

• About Preparing Data in the Database
  Oracle Machine Learning for R provides functions that enable you to use R to prepare database data for analysis.
• Select Data
  A typical step in preparing data for analysis is selecting or filtering values of interest from a larger data set.
• Index Data
  You can use integer or character vectors to index an ordered ore.frame object.
• Combine Data
  You can join data from ore.frame objects that represent database tables by using the merge function.
• Summarize Data with ore.summary
  The ore.summary function calculates descriptive statistics and supports extensive analysis of columns in an ore.frame, along with flexible row aggregations.
• Transform Data
  In preparing data for analysis, a typical step is to transform data by reformatting it or deriving new columns and adding them to the data set.
• Sample Data
  Sampling is an important capability for statistical analytics.
• Partition Data
  In analyzing large data sets, a typical operation is to randomly partition the data set into subsets.
• Prepare Time Series Data
  OML4R provides you with the ability to perform many data preparation operations on time series data, such as filtering, ordering, and transforming the data.