![]() SAS programming tips: A guide to efficient SAS processing (1990). The example would fit with the need to identify a small number of medical claims that fall within a range of hospital days. Please note that it does not exactly deal with many-to-many merges but it can be modified. The code has not been provided in this document since it is in the SAS Tips manual. (Tip 4.13) provides an example of using point to identify matched cases. : This code is simplified version of the original. This method does not provide all possible combinations but only those with overlapping dates. In the set statement the duration (hospital) data must be first. ![]() The data sets must be sorted by order of ID and chronological sequence. medical) and M records having duration (e.g. There are examples of data step code that require a match between multiple one date records (e.g. When using large databases you might want to consider one of the data step processes outlined instead. See note above regarding inefficiency of SQL. The same or similar code would work for determining medical claims that fall within a Hospital stay. The example here looks for hospital stays that fall within a longer PCH stay. Setting the PROC DS2 BYPARTITIONNO option does not affect a MERGE statement when you are using in-database processing. However, if you are using the SAS In-Database Code Accelerator for Hadoop, Hive. "Many of the problems with PROC SQL arise because of its insistence on forming a Cartesian product, and then sub setting based on that". The MERGE statement is allowed when you are using the SAS In-Database Code Accelerator. "It was really only designed as a user interface tool, not an efficient way to do things in SAS". The following was posted to the SAS-L list serv a number of years ago by Phil Mason. a complex match between 3, 4 or more databases.īe careful when using SQL: it is not a very efficient procedure in SAS and can increase the use of CPU time dramatically. SQL can also deal with multiple databases at once - e.g. SQL does not require any pre-processing and the request is often easier to follow. It has the same requirements as above, but SQL is used instead. It seems to work on views, but there are potential problems and it is not recommended.Īlso see notes below on using set with a by statement. : POINT= will not work on compressed data sets. Sets the file with the pointers and point into the other dataset using the pointer variable. Merges intermediate data to other dataset by the identifier - this works because the intermediate data is only one record/individual. The code in Example 1 does the following:Ĭreates an intermediate dataset with only one record per individual but the start and end points in the data are identified for pointing back to this data. This method uses the POINT = key word on the SET statement to select each observation the correct number of times. This method is possibly more efficient than SQL. Further processing must be done to limit the data to the records of interest. A single database with all of the combinations between the two based on ID is created. The examples provided in this concept represent the combination of medical and hospital databases based on individual. When there are multiple records for a key on both files SAS writes a warning to the log but continues to process anyway. Using a simple merge is not recommended since SAS expects only one record per key value on at least one of the datasets. Variable on each data set can lead to unexpected problems. In this method we will use PROC SQL with UNION ALL to row bind two tables.Merging files where there are multiple values of the Row Bind in SAS : PROC SQL using UNION ALL : Method 2 So the resultant row binded table in SAS will be SET statement takes two tables namely EMP_DET1 and EMP_DET2 and binds them together and named as EMP_DET_FIN as shown below In this method we will use datastep procedures to row bind two tables. We will be using EMP_DET1 and EMP_DET2 table Row bind in SAS – using UNION ALL in PROC SQL.Row bind in SAS is accomplished in two ways, one way by specifying the tables together in SET statement and other way by using UNION ALL in SAS PROC SQL.
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