IBM in-Database Analytics for R
Convert an R object to an IDA data frame
Create or drop a view
Available methods for ida.col.def
Available methods for ida.data.frame
Create an IDA data frame
Available methods for ida.list
Store and retrieve R objects in the database
Association Rule Mining
Open or closes a IDA database connection
Query, store and update data in the database.
Hierarchical (divisive) clustering
Drop a predictive model from the database
Get the name of a model
Generalized Linear Models (GLM)
k-means clustering
Show and set accelerator settings
List all predictive models in the database
Linear regression
Merge IDA data frames
Naive Bayes Classifier
Run an SQL query on the database
Retrieve a predictive model from the database
Taking a random sample from a IDA data frame
In-Database Cross Tabulation and Table Creation
Apply R-function to subsets of IDA data frame
Miscellaneous tools
Decision and Regression tree
two step clustering
Initialize the In-Database Analytics functions
IBM In-Database Analytics
Return a list of tables
Functionality required to efficiently use R with IBM(R) Db2(R) Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in conjunction with IBM Db2 Analytics Accelerator for z/OS. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of parallel processing in the underlying database. For executing R-functions in a multi-node environment in parallel the idaTApply() function requires the 'SparkR' package (<https://spark.apache.org/docs/latest/sparkr.html>). The optional 'ggplot2' package is needed for the plot.idaLm() function only.