Description
Use Values analysis as the first type of analysis performed on unknown data.
Values analysis determines the nature and quality of the data. For example, whether
the data is categorical or continuously numeric, how many null values it contains,
and so on.
A Values analysis provides a count of rows, rows with non-null values, rows with
null values, rows with value 0, rows with a positive value, rows with a negative
value, and the number of rows containing blanks in the given column. By default,
unique values are counted, but this calculation can be inhibited for performance
reasons if desired.
For a column of non-numeric type, the zero, positive, and negative counts are
always zero (for example, 000 is not counted as 0). A Values analysis can be
performed on columns of any data type, though the measures displayed vary according
to column type.
Usage
td_values_valib(data, columns, ...)
Arguments
data |
Required Argument. |
columns |
Required Argument.
Types: character OR vector of Strings (character) |
... |
Specifies other arguments supported by the function as described in the 'Other Arguments' section. |
Value
Function returns an object of class "td_values_valib"
which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using name: result.
Other Arguments
exclude.columns
Optional Argument.
Specifies the name(s) of the column(s) to exclude from the
analysis, if a column specifier such as 'all', 'allnumeric',
'allcharacter' is used in the "columns" argument.
Types: character OR vector of Strings (character)
group.columns
Optional Argument.
Specifies the name(s) of column(s) to perform separate analysis
for each group.
Types: character OR vector of Strings (character)
distinct
Optional Argument.
Specifies whether to select unique values count for each selected column.
Default Value: FALSE
Types: logical
filter
Optional Argument.
Specifies the clause to filter rows selected for analysis within Values.
For example,
filter = "cust_id > 0"
Types: character
Examples
# Notes:
# 1. To execute Vantage Analytic Library functions, set options 'val.install.location' to
# the database name where Vantage analytic library functions are installed.
# 2. Datasets used in these examples can be loaded using Vantage Analytic Library installer.
# Set the option 'val.install.location'.
options(val.install.location = "SYSLIB")
# Get remote data source connection.
con <- td_get_context()$connection
# Create an object of class "tbl_teradata".
df <- tbl(con, "customer")
print(df)
# Example 1: Perform Values analysis using default values on 'income' and
# 'marital_status' columns.
obj <- td_values_valib(data=df, columns=c("income", "marital_status"))
# Print the results.
print(obj$result)
# Example 2: Perform Values analysis on 'income' column with values grouped by
# 'gender' and only for rows with income greater than 0.
obj <- td_values_valib(data=df, columns="income", group.columns="gender", filter="income > 0")
# Print the results.
print(obj$result)