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List of Examples
1-1 Getting Help on Oracle R Enterprise Classes, Functions, and Methods
1-2 Viewing Oracle R Enterprise Documentation
1-3 Finding the Mean of the Petal Lengths by Species in R
1-4 SQL Equivalent of Example 1-3
1-5 Classes of a data.frame and a Corresponding ore.frame
1-6 Coercing R and Oracle R Enterprise Class Types
1-7 Using demo to List Oracle R Enterprise Examples
1-8 Running the basic.R Example Script
2-1 Using ore.connect and Specifying a SID
2-2 Using ore.connect and Specifying a Service Name
2-3 Using ore.connect and Specifying an Easy Connect String
2-4 Using ore.connect and Specifying a Full Connection String
2-5 Using the conn_string Argument to Specify an Oracle Wallet
2-6 Using the conn_string Argument and Specifying an Empty Connection String
2-7 Using the conn_string Argument in Connecting to a Pluggable Database
2-8 Using the service_name Argument in Connecting to a Pluggable Database
2-9 Disconnecting an Oracle R Enterprise Session
2-10 Using ore.sync to Add ore.frame Proxy Objects to an R Environment
2-11 Using ore.get to Get a Database Table
2-12 Using ore.attach to Add an Environment for a Database Schema
2-13 Ordering Using Keys
2-14 Ordering Using Row Names
2-15 Merging Ordered and Unordered ore.frame Objects
2-16 Using ore.push and ore.pull to Move Data
2-17 Using ore.create and ore.drop to Create and Drop Tables
2-18 Saving Objects and Creating a Datastore
2-19 Using the ore.datastore Function
2-20 Using the ore.datastoreSummary Function
2-21 Using the ore.load Function to Restore Objects from a Datastore
2-22 Using the ore.delete Function
3-1 Selecting Data by Column
3-2 Selecting Data by Row
3-3 Selecting Data by Value
3-4 Indexing an ore.frame Object
3-5 Joining Data from Two Tables
3-6 Aggregating Data
3-7 Formatting Data
3-8 Using the transform Function
3-9 Adding Derived Columns
3-10 Simple Random Sampling
3-11 Split Data Sampling
3-12 Systematic Sampling
3-13 Stratified Sampling
3-14 Cluster Sampling
3-15 Quota Sampling
3-16 Randomly Partitioning Data
3-17 Aggregating Date and Time Data
3-18 Using Date and Time Arithmetic
3-19 Comparing Dates and Times
3-20 Using Date and Time Accessors
3-21 Coercing Date and Time Data Types
3-22 Using a Window Function
3-23 The NARROW Data Set
3-24 Performing Basic Correlation Calculations
3-25 Creating Correlation Matrices
3-26 Creating a Single Column Frequency Table
3-27 Analyzing Two Columns
3-28 Weighting Rows
3-29 Ordering Cross-Tabulated Data
3-30 Analyzing Three or More Columns
3-31 Specifying a Range of Columns
3-32 Producing One Cross-Tabulation Table for Each Value of Another Column
3-33 Producing One Cross-Tabulation Table for Each Set of Value of Two Columns
3-34 Augmenting Cross-Tabulation with Stratification
3-35 Binning Followed by Cross-Tabulation
3-36 Using the ore.freq Function
3-37 Building a Double Exponential Smoothing Model
3-38 Building a Time Series Model with Transactional Data
3-39 Building a Double Exponential Smoothing Model Specifying an Interval
3-40 Ranking Two Columns
3-41 Handling Ties in Ranking
3-42 Ranking by Groups
3-43 Partitioning into Deciles
3-44 Estimating Cumulative Distribution Function
3-45 Scoring Ranks
3-46 Sorting Columns in Descending Order
3-47 Sorting Different Columns in Different Orders
3-48 Sorting and Returning One Row per Unique Value
3-49 Removing Duplicate Columns
3-50 Removing Duplicate Columns and Returning One Row per Unique Value
3-51 Preserving Relative Order in the Output
3-52 Sorting Two Columns in Different Orders
3-53 Sorting Two Columns in Different Orders and Producing Unique Combinations
3-54 Calculating Default Statistics
3-55 Calculating Skew and Probability for t Test
3-56 Calculating the Weighted Sum
3-57 Grouping by Two Columns
3-58 Grouping by All Possible Ways
3-59 Calculating the Default Univariate Statistics
3-60 Calculating the Default Univariate Statistics
3-61 Calculating the Complete Quantile Statistics
3-62 Downloading, Installing, and Loading a Third-Party Package on the Client
3-63 Using a kernlab Package Function
4-1 Displaying Values from the longley Data Set
4-2 Using ore.lm
4-3 Using the ore.stepwise Function
4-4 Using the ore.glm Function
4-5 Building a Neural Network Model
4-6 Using ore.neural and Specifying Activations
4-7 Using the ore.odmAssocRules Function
4-8 Using the ore.odmAI Function
4-9 Using the ore.odmDT Function
4-10 Building a Linear Regression Model
4-11 Using Ridge Estimation for the Coefficients of the ore.odmGLM Model
4-12 Building a Logistic Regression GLM
4-13 Specifying a Reference Value in Building a Logistic Regression GLM
4-14 Using the ore.odmKM Function
4-15 Using the ore.odmNB Function
4-16 Using the ore.odmNMF Function
4-17 Using the ore.odmOC Function
4-18 Using the ore.odmSVM Function and Generating a Confusion Matrix
4-19 Using the ore.odmSVM Function and Building a Regression Model
4-20 Using the ore.odmSVM Function and Building an Anomaly Detection Model
5-1 Using the ore.predict Function on a Linear Regression Model
5-2 Using the ore.predict Function on a Generalized Linear Regression Model
5-3 Using the ore.predict Function on an ore.model Model
6-1 Installing a Package for a Single Database in an Oracle R Enterprise Session
6-2 Installing a Package for a Single Database from the Command Line
6-3 Installing a Package Using DCLI
6-4 Using a C50 Package Function
6-5 Using the ore.scriptCreate and ore.scriptDrop Functions
6-6 Using the ore.doEval Function
6-7 Using the ore.doEval Function with an Optional Argument
6-8 Using the ore.doEval Function with the FUN.NAME Argument
6-9 Using the ore.doEval Function with the FUN.VALUE Argument
6-10 Using the doEval Function with the ore.connect Argument
6-11 Using the ore.tableApply Function
6-12 Using the ore.groupApply Function
6-13 Using ore.groupApply for Partitioning Data on Multiple Columns
6-14 Using the ore.rowApply Function
6-15 Using the ore.rowApply Function with Datastores and Scripts
6-16 Using the ore.indexApply Function
6-17 Using the ore.indexApply Function and Combining Results
6-18 Using the ore.indexApply Function in a Simulation
6-19 Dropping and Creating an R Script with the SQL APIs
6-20 Using rqEval
6-21 Passing Arguments to the R Function invoked by rqEval
6-22 Specifying PNG as the Output Table Definition
6-23 Using an rqGroupEval Function
6-24 Using an rqRowEval Function
6-25 Using the rqTableEval Function
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