Combining and Modifying SAS Data Sets: Examples

Credits
Recognition
An Introduction to Data Relationships, Access Methods,and Techniques for Data Manipulation
Overview
Data Relationships
Access Methods: Sequential versus Direct
An Overview of Methods for Combining SAS Data Sets
An Overview of Tools for Combining SAS Data Sets
Where to Go from Here
Combining Single Observations with Single Observations

Example 2.1 Merging Data Sets by a Common Variable, Specifying Their Origin, and Replacing Missing Values
Example 2.1 Merging Data Sets by a Common Variable, Specifying Their Origin, and Replacing Missing Values
Example 2.2 Combining Observations When Variable Values Do Not Match Exactly
Example 2.3 Combining Observations When There Is No Common Variable
Example 2.4 Performing a Table Lookup When the Lookup Data Set is Indexed
Example 2.5 Performing a Table Lookup When the Lookup Data Set is Not Indexed
Example 2.6 Matching Observations Randomly
Example 2.7 Combining Observations Based on a Calculation on Variables Contributed by Two Data Sets
Combining a Single Observation with Multiple Observations
Example 3.1 Adding Values to All Observations in a Data Set
Example 3.2 Adding Values from the Last Observation in a Data Set to All Observations in Another Data Set
Example 3.3 Merging Observations from Multiple Data Sets Based on a Common Variable
Example 3.4 Applying Transactions to a Master Data Set Based on a Common Variable
Example 3.5 Combining and Collapsing Observations Based on a Common Variable
Example 3.6 Applying Transactions to a Master Data Set Using an Index
Example 3.7 Removing Observations from a Master Data Set Based on Values in a Transaction Data Set
Example 3.8 Performing a Table Lookup with a Small Lookup Data Set
Example 3.9 Performing a Table Lookup with Large Nonindexed Data Sets
Example 3.10 Performing a Table Lookup Using a Composite Index When the Transaction Data Set Contains Duplicate Values
Example 3.11 Performing a Table Lookup with a Large Lookup Data Set That Is Indexed
Combining Multiple Observations with Multiple Observations
Example 4.1 Adding Variables from a Transaction Data Set to a Master Data Set
Example 4.2 Updating a Master Data Set with Only Nonmissing Values from a Transaction Data Set
Example 4.3 Generating Every Combination of Observations (Cartesian Product) between Data Sets
Example 4.4 Generating Every Combination of Observations between Data Sets Based on a Common Variable
Example 4.5 Delaying Final Disposition of Observations Until All Processing Is Complete
Example 4.6 Generating Every Combination between Data Sets, Based on a Common Variable When an Index Is Available
Example 4.7 Combining Multiple Data Sets without a Variable Common to All the Data Sets
Example 4.8 Interleaving Nonsorted Data Sets
Example 4.9 Interleaving Data Sets Based on a Common Variable
Example 4.10 Comparing All Observations with the Same BY Values
Manipulating Data From a Single Source
Example 5.1 Performing a Simple Subset
Example 5.2 Separating Unique Observations from Duplicate Observations
Example 5.3 Accessing a Specific Number of Observations from the Beginning and End of a Data Set
Example 5.4 Adding New Observations to the End of a Data Set
Example 5.5 Adding Observations to a Data Set Based on the Value of a Variable
Example 5.6 Simulating the LEAD Function by Comparing the Value of a Variable to Its Value in the Next Observation
Example 5.7 Obtaining the Lag (Previous Value) of a Variable within a BY Group
Example 5.8 Applying Common Operations to a Group of Variables
Example 5.9 Calculating Totals across a BY Group to Produce Either a Grand or Cumulative Total
Example 5.10 Calculating the Percentage That One Observation Contributes to the Total of a BY Group
Example 5.11 Adding a New Variable that Contains the Frequency of a BY-Group Value
Example 5.12 Subsetting a Data Set Based on the Calculated Average of a BY Group
Example 5.13 Really Rounding Numbers
Example 5.14 Collapsing Observations within a BY Group into a Single Observation
Example 5.15 Expanding Single Observations into Multiple Observations
Example 5.16 Reshaping Observations into Multiple Variables
Utilities and Functions
Example 6.1 Converting Variable Types from Character to Numeric and Vice Versa
Example 6.2 Determining the Type of a Variable's Content
Example 6.3 Determining Whether a Variable is Character or Numeric
Example 6.4 Creating a SAS Data Set Whose Variables Contain the Attributes of Variables from Another SAS Data Set
Example 6.5 Sorting Variable Values within an Observation (Bubble Sort)
Example 6.6 Creating Equal-Sized Random Samples and Producing Equal-Sized Subsets or Exact-Sized Subsets
Example 6.7 Counting the Occurrences of a String within the Values of a Variable
Example 6.8 Extracting a Character String without Breaking the Text in the Middle of a Word
Example 6.9 Creating SAS Datetime Values
Example 6.10 Creating a SAS Time Value from a Character Value
Example 6.11 Calculating a Person's Age

Appendix Error Checking When Using MODIFY or SET with KEY=
Why Error Checking?
New Error-Checking Tools

Example 1: Routing Execution When an Unexpected Condition Occurs
Example 2: Using Error Checking on All Statements That Use KEY=

Index
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