3.2.1 Load Data

Access the data set from the SH Schema and explore the data to understand the attributes.

Access Data

You will be using the CUSTOMERS and SUPPLEMENTARY_DEMOGRAPHICS tables available in the SH schema.

See SH.CUSTOMERS for information about the CUSTOMERS table in SH Schema.

The following table displays information about the attributes from SUPPLEMENTARY_DEMOGRAPHICS:

Attribute Name	Data Type	Information
CUST_ID	Numeric	The ID of the customer
EDUCATION	Character	Education level attained
OCCUPATION	Character	Occupation of the customer
HOUSEHOLD_SIZE	Character	Number of people living at residence
YRS_RESIDENCE	Numeric	Number of years customer lived at current residence
AFFINITY_CARD	Character	Indicates whether the customer holds an affinity card. 1 means Yes. 0 means No.
BULK_PACK_DISKETTES	Character	Product. Indicates whether the customer purchased the bulk pack diskettes. 1 means Yes. 0 means No.
FLAT_PANEL_MONITOR	Character	Product. Indicates whether the customer purchased flat panel monitor. 1 means Yes. 0 means No
HOME_THEATER_PACKAGE	Character	Product. Indicates whether the customer purchased home theatre package. 1 means Yes. 0 means No
BOOKKEEPING_APPLICATION	Character	Product. Indicates whether the customer purchased bookkeeping application. 1 means Yes. 0 means No
PRINTER_SUPPLIES	Character	Product. Indicates whether the customer purchased printer supplies. 1 means Yes. 0 means No
Y_BOX_GAMES	Character	Product. Indicates whether the customer purchased YBox Games. 1 means Yes. 0 means No
OS_DOC_SET_KANJI	Character	Product. Indicates whether the customer purchased the Kanji character set for the operating system documentation. 1 means Yes. 0 means No
COMMENTS	Character	Comments from customers

To access database data from R using OML4R, you must first create a proxy object in R that represents a database table, view, or query. In this example, the proxy object is created using a query. Create proxy objects for SUPPLEMENTARY_DEMOGRAPHICS and CUSTOMERS and then merge them by inner join on a key column, in this case, CUST_ID. Assess the data to identify data types and data quality issues. Look for missing values, outlier numeric values, or inconsistently labeled categorical values.

1. Run the following command in an R interpreter paragraph (using %r) in an OML notebook (or similar notebook environment) to import the Oracle Machine Learning for R libraries and suppress warnings regarding row ordering. Alternatively, this code can be run from the R command line or tools like RStudio.

   library(ORE)
   options(ore.warn.order=FALSE)

2. Use the ore.sync function to create the ore.frame object that is a proxy for the CUSTOMERS table in the SH schema database table.
   
   
   
   
3. Use the ore.sync function to create the ore.frame object that is a proxy for the SUPPLEMENTARY DEMOGRAPHICS table in the SH schema database table.

   ore.sync(query = c("SUPPLEMENTARY_DEMOGRAPHICS" = "select CUST_ID, HOUSEHOLD_SIZE, YRS_RESIDENCE, TO_CHAR(Y_BOX_GAMES) Y_BOX_GAMES from SH.SUPPLEMENTARY_DEMOGRAPHICS"))
   # The TO_CHAR function is used to have Y_BOX_GAMES treated as a categorical variable, not a numeric variable.
   z.show(head(SUPPLEMENTARY_DEMOGRAPHICS))