In the mfg and cars dataset, an inner join gives the result of manufacturer intersect cars, i.e. Inner MergeĪn inner join retrieves only the matched rows from the data sets/tables. Right join returns all rows from the right table, and the matched rows from the left table. The SAS Merge code shown below displays a right merge that picks “matched” manufacturers and models from the mfg and car databases depending on mfg variable(BY Variable), as well as all “unmatched” manufacturers and model from the cars database. The shaded regions (Manufacturers and Cars) represented by the Venn diagram below represent a Right Outer merge. Right MergeĪ Right Outer merge provides matched information from two or more datasets while retaining all mismatched information from the next (right) set of data. Left Merge returns all rows from the left table and the matched rows from the right table. Using the IN= dataset option and the variables created in the PDV give you much control over determining what to do with matching or non-matching rows. You’ll notice we have the additional column mileage_mps that was read from the cars table. Our final table below has 4 rows, the 4 rows included from the original mfg table. If the subsetting if statement is true SAS will continue processing the rest of the data step, including the implicit output. The value will be one for a row that is read from the mfg table. It will take on a value of either zero or one. This inMfg variable will be included in the PDV during execution. So after the mfg table, we will add IN=, and we’ll create a variable called inMfg. We can use the IN= data set option to control which rows are output. Remember, we would only like to include in our output table the 4 manufacturers and models that were in the cars table in our output table. For example, in the Venn diagram below, the coloured regions (Car Manufacturer and Car Mileage) represent a Left merge. Left MergeĪ Left Merge provides matched observations from two or more datasets while retaining all mismatched observations from the first (left) data set. So, we’ll sort the data by both mfg and model. How to merge data in Stata | Combining datasets in Stata Zero means that the table does not include the by column value for that row, and one means it does have the by column value. PDV during the merge processĭuring execution, the in= variables are assigned a value of zero or one. Each IN= variable is associated with a particular table that the option follows. The IN= variables are included in the PDV during execution but are not written to the output table. The IN= dataset option follows one or more tables on the merge statement and names a temporary variable that is added to the PDV. You can use the IN= dataset option to create temporary variables in the PDV that you can use to flag matching or non-matching values. Or suppose you want to identify missing cars in one of the tables. For example, suppose you only want to include cars that are on both tables. The output dataset you create when you merge tables with non-matching rows might not be what you want. The rows for Honda and isuz have missing values for the columns in the tables where they were not included. What happens to those non-matching rows in the data step merge? data Mergedata Īlthough both input tables have 4 rows, the output table Mergedata has 6 rows because it includes both Honda and isuz.
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