Download E-books Handbook of SAS® DATA Step Programming PDF

By Arthur Li

To write an entire software within the facts step of SAS®, programmers needs to comprehend programming common sense and know the way to enforce or even create their very own programming set of rules. Handbook of SAS® info Step Programming shows readers how top to regulate and control facts by utilizing the knowledge step.

The booklet is helping rookies stay away from universal errors as a result of a scarcity of figuring out basic and specified SAS programming suggestions. It explains that studying syntax doesn't clear up all difficulties; quite, an intensive comprehension of SAS processing is required for profitable programming. the writer additionally courses readers via a programming job. In many of the examples, the writer first offers suggestions and steps for fixing the matter, then bargains an answer, and at last supplies a extra distinctive rationalization of the solution.

Understanding the knowledge steps, quite this system info vector (PDV), is necessary to right facts manipulation and administration in SAS. This booklet is helping SAS programmers completely clutch the idea that of information step processing and write exact courses within the information step. quite a few aiding fabrics, together with info units and courses utilized in the textual content, can be found at the book’s CRC Press net page.

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Identification equals 1. you then might want to calculate the suggest rating (MEAN_SCORE) by way of dividing overall by way of N and output the end result whilst final. identity equals 1. (See Program 4. 2. ) software four. 2: info sas4_mean (drop = score); set sas4_1; through identification; 54 guide of SAS® info Step Programming if first. identity then do; overall = zero; n = zero; end; overall + rating; n + 1; if final. identity then do; mean_score = total/n; output; end; run; identify 'The suggest ranking for every subject'; proc print information = sas4_mean; run; Output from application four. 2: The suggest rating for every topic mean_ Obs identity total n rating 1 A01 8 3 2. 66667 2 A02 6 2 three. 00000 four. 2. 2  growing facts units with replica or Non-Duplicate Observations a standard activity in analyzing an information set is checking whilst the knowledge set ­contains reproduction observations. back, you should use BY-group processing to spot duplicated observations. the 1st observations in SAS4_1 are exact. believe that you just would like to create information units: one with observations with non-­duplicated documents and one containing observations with duplicated documents from the data set SAS4_1. simply because a duplicated checklist could have an identical worth for either the identity and rating variables, either identity and rating ­variables should be used because the through variables. A non-duplicated list is the one where both FIRST. ranking and final. ranking equivalent 1; ­otherwise, it will be a ­duplicated checklist. the answer for this software is in Program 4. three. application four. three: proc style info = sas4_1; through identification ranking; run; information sas4_1_s sas4_1_d; set sas4_1; through identity ranking; if first. rating and final. rating then output sas4_1_s; BY-Group Processing within the facts Step fifty five else output sas4_1_d; run; identify 'Non-duplicated records'; proc print information = sas4_1_s; run; identify 'Duplicated records'; proc print info = sas4_1_d; run; Output from software four. three: Non-duplicated files Obs identity ranking 1 A01 2 2 A02 2 3 A02 four Duplicated files Obs identification ranking 1 A01 three 2 A01 three four. 2. 3 Obtaining the latest Non-Missing facts inside of every one by means of crew Longitudinal facts, reminiscent of sufferers with repeated measurements over the years, are usually encountered within the scientific box. for instance, a sufferer may have multiple measurements of weight, blood strain, overall c­ holesterol, or blood glucose point over a number of clinical visits, yet would possibly not have these kinds of values recorded each time. Researchers will be attracted to a ­database depicting the latest on hand info on their sufferers. for instance, the information set sufferer comprises the triglyceride (TGL) ­measurement and smoking prestige (SMOKE) for sufferers for various time sessions. discover that a few sufferers have just one dimension, while ­others have been measured greater than as soon as in several years. believe that you'd wish to create a knowledge set that includes the latest non-missing info. The ensuing facts set can have 3 variables: PATID (patient ID), TGL_NEW (the latest non-missing TGL), and SMOKE_NEW (the most modern non-missing SMOKE).

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