![]() Input data-sets must have at least one common variable to merge with same name (In our case we have CUSTOMER_ID).Lets go for SAS Merge (Inner Join) using IN= Options. See below output. Indicator Sales= 1 When observation coming from the input data-set Sales else indicator is 0, same applied for customer data set. * Creating new variables using indicator variables */ CUSTOMER = IN_CUSTOMER SALES = IN_SALES MERGE CUSTOMER ( IN=IN_CUSTOMER ) SALES( IN=IN_SALES) ![]() Lets have a example – Here is the Data-sets Customer & Sales. If the observation does not come from the input data set, then the indicator variable value will be 0.If the observation does come from the input data-set, then the indicator will be 1.IN= option tells SAS to create an “ indicator variable” having the value 0 or 1 depending on whether or not the current observation comes from the input data set. To achieve the inner join using SAS merge, first we have to understand the concept of IN= option, which is especially useful when merging and concatenating data sets. Equivalent to a merge in a DATA step in SAS. the inner part of a Venn diagram intersection. So an inner join of Customer and Sales gives the result of Customer intersect Sales, i.e. Suppose we have two data-sets/tables Customer & Sales. That sequential comparison process continues until all rows are read from each table listed on the merge statement.In this blog we will explore the basic concept of Inner Join using SAS Merge & Proc SQL.Īn inner join retrieve only the matched rows from the data-sets/tables. 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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