By Michelle M. Burlew

Construction at the acclaim for the 1st variation, Michele Burlew has revised this well known examples ebook to incorporate elevated content material and new good points of SAS software program. thoroughly up-to-date for SAS 9.2, Combining and enhancing SAS facts units: Examples, moment version, offers examples that express recommendations to universal programming projects that contain combining, editing, and reshaping information units. multiplied examples show the best way to mix info units vertically and horizontally; retrieve information from search for tables; alter and replace facts units; mix precis and element information units; reshape and transpose observations in a knowledge set; and manage information in an information set with utilities and features. The instruments used to mix and regulate information units contain the SET, MERGE, regulate, and replace statements within the info step; joins and set operators in PROC SQL; BY-group processing; indexes; hash gadgets within the info step; using PROC structure and hash tables as desk lookups; and iteration facts units. specific positive aspects of this ebook contain the next: Examples are grouped by means of activity, no longer via code, so that you can simply discover a way to a specific activity; substitute suggestions are awarded as well as the most examples; such a lot examples that mix and regulate info units comprise either a knowledge step and a PROC SQL answer; many examples contain a "Closer glance" part that describes in-depth how the instance is helping you entire the duty; and every instance stands by itself so that you do not have to learn the ebook from commencing to finish. Designed for SAS programmers in any respect degrees, this examples publication can assist simplify the demanding activity of mixing and enhancing info units.

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**Extra resources for Combining and Modifying SAS Data Sets: Examples, Second Edition**

**Example text**

Output table SPRINGRUNNERS should contain the names of all the runners with no duplicate rows for those running two or three times. There were 14 runners in March, 9 in April, and 17 in May. Output table SPRINGRUNNERS contains 28 rows. PROC SQL determines uniqueness of rows by the columns in the SELECT clauses. For example, if you expanded the list of columns selected by coding SELECT * instead of SELECT RUNNER, PROC SQL would select all rows from each of the three input tables because none of the repeat runners had identical race times.

Sum the IN= variables. Execute a subsetting IF statement to save observations found in only one of the three data sets. Related Technique merge marchrunners(in=inmar) aprilrunners(in=inapr) mayrunners(in=inmay); by runner; keep runner; if inmar+inapr+inmay=1; run; The following PROC SQL step creates a table equivalent to the data set that was created in the main example. The step combines three tables vertically and selects the rows found in only one table based on the value of one column common to the tables.

For example, if you expanded the list of columns selected by coding SELECT * instead of SELECT RUNNER for all of the queries, PROC SQL would select all rows from all three tables because none of the repeat runners ran identical times. Create table RANONCE. Select one column from the inline view. Select the same column and concatenate all rows from the three tables. Use the CORR keyword to align the RUNNER column from the three tables. Group the rows returned by the inline view by the values of RUNNER.