Oracle Machine Learning for R provides functions that enable you to perform exploratory data analysis.

With these functions, you can perform common statistical operations.

The functions and their uses are described in the following topics:

• About the Exploratory Data Analysis Functions
  The OML4R functions for exploratory data analysis are in the OREeda package.
• About the NARROW Data Set for Examples
  Many of the examples of the exploratory data analysis functions use the NARROW data set.
• Correlate Data
  You can use the ore.corr function to perform correlation analysis.
• Cross-Tabulate Data
  Cross-tabulation is a statistical technique that finds an interdependent relationship between two tables of values.
• Analyze the Frequency of Cross-Tabulations
  The ore.freq function analyses the output of the ore.crosstab function and automatically determines the techniques that are relevant to an ore.crosstab result.
• Build Exponential Smoothing Models on Time Series Data
  The ore.esm function builds a simple or a double exponential smoothing model for in-database time series observations in an ordered ore.vector object.
• Rank Data
  The ore.rank function analyzes distribution of values in numeric columns of an ore.frame.
• Sort Data
  The ore.sort function enables flexible sorting of a data frame along one or more columns specified by the by argument.
• 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.
• Analyze the Distribution of Numeric Variables
  The ore.univariate function provides distribution analysis of numeric variables in an ore.frame.
• Principal Component Analysis
  The overloaded prcomp and princomp functions perform principal component analysis in parallel in the database.
• Singular Value Decomposition
  The overloaded svd function performs singular value decomposition in parallel in the database.