By Michelle M. Burlew
Construction at the acclaim for the 1st version, Michele Burlew has revised this well known examples ebook to incorporate multiplied content material and new positive factors of SAS software program. thoroughly up to date for SAS 9.2, Combining and enhancing SAS facts units: Examples, moment variation, offers examples that exhibit ideas to universal programming initiatives that contain combining, editing, and reshaping facts units. multiplied examples show the best way to mix info units vertically and horizontally; retrieve facts from search for tables; adjust and replace facts units; mix precis and element information units; reshape and transpose observations in an information set; and manage facts in an information set with utilities and capabilities. The instruments used to mix and adjust information units comprise the SET, MERGE, regulate, and replace statements within the facts step; joins and set operators in PROC SQL; BY-group processing; indexes; hash items within the information step; using PROC layout and hash tables as desk lookups; and iteration information units. particular beneficial properties of this publication contain the next: Examples are grouped by means of activity, no longer through code, so that you can simply discover a technique to a specific job; replacement strategies are awarded as well as the most examples; such a lot examples that mix and alter info units contain 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 don't have to learn the publication from starting to finish. Designed for SAS programmers in any respect degrees, this examples ebook may help simplify the tough activity of mixing and editing info units.
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Additional resources for Combining and Modifying SAS Data Sets: Examples
The step performs three queries that each select the same column and it returns one row for each column value found in all three queries. The INTERSECT operator that connects the queries concatenates the rows that the three queries return. The process that PROC SQL follows is to combine the first two queries, and then combine that result with the third query. The INTERSECT operator that is used without the ALL keyword removes duplicate rows from the output table. PROC SQL determines uniqueness of a row by the values of the columns in the SELECT clauses.
Add the observations from data set SPRINGGRADS to ALLGRADS. Create data set ALLGRADS (it does not exist because of execution of the preceding PROC DATASETS step). Add the observations from SUMMERGRADS to data set ALLGRADS. Add the observations from FALLGRADS to ALLGRADS to complete the concatenation of the three data sets. 2 Interleaving Observations from Two or More Data Sets Based on a Common Variable Goal Interleave the observations from three data sets in order by the values of a specific variable.
The values need to be grouped to make matches between the two data sets. A FORMAT statement for COMPLETED formats its values with the MONYY format, which groups the date values by month and year. The BY statement in the DATA step includes the GROUPFORMAT option. This option causes the matching between the two data sets to be done by the formatted values of the BY variable COMPLETED rather than its internal value. If you omit the GROUPFORMAT option, only two observations in PARTICIPANTS match to an observation in PRIZES.