![]() So show us a better example of what you want and we can maybe offer some suggestions to get there, but a MERGE/JOIN as shown will not get you there because your logic is more complicated. If you cannot, a merge links each record by the identified variable and repeats. If you want this, you need to be able to join by more than ID, ie ID + Event or another variable to uniquely identify each event. The purpler portion is what you expect, but your expectations are incorrect. Every ID is different: different ages and different amount of entries and diagnoses. It will transform others (that must remain 1 to 0) - it will overwrite my correct data in Z. Every ID is different: different ages and different amount of entries and has height Body Weight variablesīut with all diagnoses = 1 I don't have a way to give a command to transform it to 0. for example.īut with all diagnoses = 1 I don't have a way to give a command to transform it to 0. I thought they would be missing and then I could tell sas to do if dx =. So if I have one entry from Z and 10 from Y for ID 1- I have 11 entries with different ages for ID 1 but then all of the diagnosis that were 1 in 1 ENTRY are repeated as 1 in all other 10 entries even though it should have been 1 only in ONE ENTRY and 0 in all the other 10 (from Y). The variable that they both have in common is AGE and I found a way to prevent the ages from overwriting. It is also longitudinal so one ID may have 5 entries in File Y and 10 in File Z.Īnother ID might have 2 entries in File Y and 5 entries in file Z. X needs to have all variables from Y and Z. That sequential comparison process continues until all rows are read from each table listed on the merge statement.Z has diagnoses (URI ASTHMA etc) and drugs variables Again the values in name match so both rows are read into the PDV, and so on. SAS returns to the top of the data step for the next iteration and advances to row two in both tables. If they match then both rows are read into the PDV, additional statements are executed, and at the end of the data step the row is written to the output table. In the execution phase, SAS begins by examining the by column value for the first row in each table. Finally any other compile-time statements are processed. If there are any other statements in the data step that create new columns, they are also added to the PDV. Any additional columns and their attributes that are not already in the PDV are added. SAS then examines the second table on the merge statement. In the compilation phase, all of the columns from the first table listed on the merge statement and their attributes are added to the PDV. ![]() SAS simply compares rows sequentially as it reads from the multiple tables matching rows based on the value of the common column. The data step merge process is very similar to how you would envision matching two lists by hand if the values are in sorted order. ![]() Let's see how SAS processes the code behind the scenes. Both tables are listed in the merge statement, and the common column "name" is listed in the by statement. So here's the data step merge that will join our two tables. Typically you would use PROC SORT steps to arrange the rows of the input tables by the matching column before the data step merge. When a BY statement is used in a data step, the data must be in sorted order. The data sets ANIMAL2 and PLANT2 do not contain all values of the BY variable Common. When SAS performs a match-merge with nonmatched observations in the input data sets, SAS retains the values of all variables in the program data vector even if the value is missing. You can list multiple tables on the merge statement as long as each table has the common matching column that is listed on the by statement. Example 3: Match-Merge with Nonmatched Observations. To merge tables in the data step, you use a merge statement rather than a set statement. This is a one-to-one merge since each value of name is in both tables. Notice that the name column is in both tables, and both tables are sorted by name. Suppose we want to combine class and class teachers in a single table. Let's look at an example of merging tables.
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