Note : Run the FIRST PROGRAM each time you begin to play with a different method so as to retain the data sets CLASS1, CLASS2 and CLASS3 in their original form(i.e. The observations from other data set contain missing values for the uncommon variables. The result is that SAS retains all original variables in the BASE data set and appends. To overcome this error, we use FORCE option. Appending CLASS3 to CLASS2Īs CLASS1 and CLASS2 contain some different variables, the program stops and shows an error message in the log. As this method adds observations to the BASE data set, the program executes only if all variables in BASE data set and other data set are common. After PROC APPEND, the variable description in the BASE data set remains as it is. To add more data sets more proc steps need to be executed. One PROC step can append only one data set to the base data set. It has a BASE data set and one another data set which it adds to the BASE data set. To show how this method works, we create a new input data set CLASS3.ĬLASS1 SORTED IN DESCENDING ORDER BY AGE CLASS3 SORTED IN DESCENDING ORDER BY AGE MATCHMERGE2 data setĪppending method of combining uses PROC APPEND. This method uses MERGE and SET statements. Other methods of combining combine observations in data sets on the basis of their relative positions. Match merging method helps in combining data sets on the basis of values of variables in a sorted order. In our example, we sort the input data sets by AGE. To use this method, each input data set must be sorted in ascending order based on the BY variable(s). Interleaving creates a new data set out by combining input data sets in sorted order(BY some variable). – if there is a common variable and its length, label, format or informat atttribute is different, SAS takes it from the first data set that contains that variable – if there is a common variable, it must have same type attribute – the new data set contains all variables and observations from all input data sets – number of observations in new data set = number of observations in smallest input data set.Ĭoncatenating method is used to append observations from one data set to another data set. When two or more data sets are combined by using one to one merging method – – if there are common variables in input data sets, the observations in the last data set overwrite observations in earlier data sets – the observations are combined on the basis of their relative positions in input data set – the new data set contains all variables from all input data sets When two or more data sets are combined by using one to one merging method – Through these data sets, we will be able to clearly distinct one method of combining from the other. Below, I have created two data sets CLASS1 and CLASS2 out of CLASS data set in SASHELP library. I’ve spilt CLASS data set on the basis of age and have also dropped height variable from one data set and weight variable from other data set. But before that we create two sample data sets. We shall discuss each of these methods of combining. There are different ways of combining data sets : Very often a SAS programmer is required to combine observations from two or more data sets.
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