![]() ![]() ![]() Often what is desired in these cases is an "all pairs" merge, where the product of the two datasets is formed. Strange things can happen in these merges. The first two are to be avoided because of the undesirable side-effects of the imbalance between the two datasets. The letters refer to the number of observations in each BY group. The types of merges that SAS can do are listed below. Th decimal place, so avoid using the results of complex mathematical expressions as merge keys. Keep in mind, however, that numeric variables with decimals must match exactly, even down to the It is useful to create intermediate variables as merge keys before attempting to merge, making sure that the above criteria have been met. Name can also be difficult to use because a person can go by one name on one file, and another name (middle name, nickname, etc.) on the other. Left or right justification (for character variables)ĭate can be unreliable as a merge key because processing delays can result in differences across files. Since SAS only recognizes exact matches on BY variables, they have to be identical on the following characteristics:īY value (the actual value of the variable) A BY group is a unique combination of merge keys, called BY variables.Ĭommonly used BY variables are PHIN, name, sex, age, date, or registration number. That sequential comparison process continues until all rows are read from each table listed on the merge statement.SAS merges two datasets by matching the BY groups on one dataset to the corresponding BY groups on the other. ![]() 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. 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. 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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