What's New in This Edition
by Michele M. Burlew
Combining and Modifying SAS® Data Sets: Examples Second Edition
- Copyright
- Acknowledgments
- About This Book
- Introducing Data Relationships, Techniques for Data Manipulation, and Access Methods
- Overview
- Determining Data Relationships
- Understanding the Methods for Combining SAS Data Sets
- Understanding Access Methods: Sequential versus Direct
- Understanding the Tools for Combining SAS Data Sets
- Understanding the Tools for Processing Information in Groups
- Choosing between the DATA Step and PROC SQL
- Choosing between MODIFY and UPDATE
- Combining Data Sets Vertically: Concatenating, Interleaving, and Appending Data Sets
- Example 2.1 Concatenating Data Sets
- Example 2.2 Interleaving Observations from Two or More Data Sets Based on a Common Variable
- Example 2.3 Appending One Data Set to the End of Another Data Set
- Example 2.4 Selecting Unique Rows When Concatenating Tables
- Example 2.5 Selecting Rows in Common When Concatenating Tables
- Example 2.6 Selecting Observations Unique to Each Data Set When Concatenating Data Sets
- Combining Data Sets Horizontally: Match-Merging Data Sets by Value
- Example 3.1 Merging Data Sets by a Common Variable
- Example 3.2 Merging Observations from Multiple Data Sets by a Common Variable
- Example 3.3 Combining Observations When Variable Values Do Not Match Exactly
- Example 3.4 Combining Observations by the Formatted Value of a Variable
- Example 3.5 Combining Multiple Tables When the Matching Column Has Different Attributes
- Example 3.6 Combining Rows When There Is No Common Column
- Example 3.7 Matching Observations Randomly
- Example 3.8 Combining Multiple Data Sets without a Variable Common to All the Data Sets
- Example 3.9 Generating Every Combination of Rows (Cartesian Product) between Tables
- Example 3.10 Generating Every Combination of Rows between Tables Based on a Common Column
- Example 3.11 Generating Every Combination of Observations between Data Sets Based on a Common Variable When an Index Is Available
- Example 3.12 Combining and Collapsing Observations Based on a Common Variable
- Example 3.13 Combining and Collapsing Observations Based on a Common Variable When the Transaction Data Set Is Indexed
- Example 4.1 Performing a Simple Table Lookup
- Example 4.2 Performing a Table Lookup in a Small Lookup Data Set
- Example 4.3 Performing a Table Lookup in a Large, Non-Indexed Lookup Data Set
- Example 4.4 Performing Multiple Lookups for Each Observation in a Data Set
- Example 4.5 Performing a Table Lookup When the Lookup Data Set Is Indexed
- Example 4.6 Performing a "Chained" Lookup
- Combining Summary and Detail Data
- Example 5.1 Adding Values to All Observations in a Data Set
- Example 5.2 Adding Values from the Last Observation in a Data Set to All Observations in a Data Set
- Example 5.3 Computing Summary Data and Combining Them with Detail Data
- Example 5.4 Subsetting a Table Based on the Calculated Average of a Group
- Example 5.5 Calculating Totals across a BY Group to Produce Cumulative and Grand Totals
- Example 5.6 Calculating Percentages and Statistics That One Row Contributes to a BY Group
- Updating Data Sets by Match-Merging by Value
- Example 6.1 Updating a Data Set and Controlling Whether Common Variables Are Overwritten with Missing Values
- Example 6.2 Updating a Data Set and Allowing Some Values to Be Updated with Missing Values
- Example 6.3 Merging Data Sets and Conditionally Overwriting Common Variables
- Example 6.4 Adding Observations and Variables to the Master Data Set When Duplicate Matching Variable Values Exist in the Transaction Data Set
- Example 6.5 Saving Observations from Only the Master Data Set When the Transaction Data Set Contains Duplicates
- Modifying Data Sets in Place
- Manipulating Data from a Single Source
- Example 8.1 Performing a Simple Subset
- Example 8.2 Separating Unique Observations from Duplicate Observations Based on BY Values
- Example 8.3 Separating Completely Duplicate Observations from Unique Observations
- Example 8.4 Separating the First Observation in a BY Group from the Other Observations in the BY Group
- Example 8.5 Accessing a Specific Number of Observations from the Beginning and End of a Data Set
- Example 8.6 Creating a Customized Sort Order without Adding a New Column to the Table
- Example 8.7 Adding New Observations to the End of a Data Set
- Example 8.8 Adding Observations to a Data Set Based on the Value of a Variable
- Example 8.9 Adding Observations to a SAS Data Set So the Values of a Variable Are Consecutive throughout the BY Group
- Example 8.10 Adding Rows to a Table So That All Possible Values of Specific Columns Are Present in Each BY Group
- Example 8.11 Expanding Single Observations into Multiple Observations
- Example 8.12 Collapsing Observations within a BY Group into a Single Observation
- Example 8.13 Obtaining the Previous Value of a Variable within a BY Group
- Example 8.14 Comparing the Value of a Variable to Its Value in the Next Observation
- Example 8.15 Applying the Same Operation to a Group of Variables
- Example 8.16 Obtaining Hierarchical Data from a Table and Matching Them to the Rows in the Same Table
- Example 8.17 Combining Generation Data Sets
- Manipulating Data with Utilities and Functions
- Example 9.1 Converting Variable Types from Character to Numeric and Vice Versa
- Example 9.2 Determining the Type of a Variable's Content
- Example 9.3 Determining Whether a Variable Is Character or Numeric
- Example 9.4 Specifying a Numeric or Character Format at Run Time
- Example 9.5 Creating Columns That Contain the Attributes of Other Columns
- Example 9.6 Sorting Variable Values within an Observation
- Example 9.7 Shifting Nonmissing Values Left in an Observation
- Example 9.8 Generating Random Numbers within a Range of Values
- Example 9.9 Selecting Observations at Random from a Data Set without Replacement
- Example 9.10 Selecting Equal-Sized Samples from Different Groups
- Example 9.11 Creating SAS Datetime Values and Computing the Difference between Two Datetime Values
- Example 9.12 Creating a SAS Time Value from a Character Value
- Example 9.13 Calculating a Person's Age
- Example 9.14 Incrementing a Date by an Interval
- Example 9.15 Determining the Number of U.S. Business Days between Two Dates
- Example 9.16 Counting the Occurrences of a String
- Example 9.17 Extracting a Character String without Breaking the Text in the Middle of a Word
- Example 9.18 Cleaning Character Data Values
- Example 9.19 Validating and Standardizing Complex Character Data
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.