PROC APPEND is most useful when you use it in a macro. the dataset identified by the syntax ‘DATA=’). PROC APPEND is faster than SET statement or PROC SQL UNION because it only reads in the data set being appended (i.e. If the dataset ALLDATA does not already exist, it would be automatically created by SAS.
Sas data merge code#
In the first PROC APPEND, it would create a base table ALLDATA (as specfied in the code below). If you want to append data and store it to another dataset, you can run PROC APPEND twice to do it. After running this code, the dataset1 contains 8 records ( 4 from the original 'dataset1' file and 4 from the dataset2)
In log, it writes 'Appending Dataset2 to Dataset1' for our example. In PROC APPEND, the data set specified in BASE= option refers to a data set in which other data set would be added or appended. QUIT The output of PROC SQL is same as the output of previous example.
The CORR tells SAS to append data sets by name (not by column position). OUTER UNION CORR keyword is used in PROC SQL to concatenate two data sets. Make sure data sets are sorted before appending datasets. You interleave data sets using a SET statement and a BY statement in a DATA step. Interleaving combines individual sorted SAS data sets into one sorted data set. Interleaving SAS Data Sets (Sorted Stacked Data Set) Upon execution of the above SAS program with the above changed part, we get the following output.Note : The stacked data set is not sorted because we have not used BY statement.Ģ. In the below example, the IN= value keeps only the observations where the values from both the data sets SALARY and DEPT match. The merge statement of the SAS program needs to be changed. To avoid the missing values in the result we can consider keeping only the observations with matched values for the common variable. When the above code is applied, we get the below result. ExampleĬonsider the case of employee ID 3 missing from the dataset salary and employee ID 6 missing form data set DEPT. In such cases the data sets still get merged but give missing values in the result. There may be cases when some values of the common variable will not match between the data sets. Please note that the observations in both the datasets are already sorted in ID column. The above result is achieved by using the following code in which the common variable (ID) is used in the BY statement. The final data set will still have one observation per employee but it will contain both the salary and department variables. In this case to get the complete information for each employee we can merge these two data sets. ExampleĬonsider two SAS data sets one containing the employee ID with name and salary and another containing employee ID with employee ID and department. Let us understand data merging with the help of an example. The basic syntax for MERGE and BY statement in SAS is −įollowing is the description of the parameters used −ĭata-set1,Data-set2 are data set names written one after another.Ĭommon Variable is the variable based on whose matching values the data sets will be merged. input data sets must be sorted by the common variable(s) that will be used to merge on.input data sets must have at least one common variable to merge on.There are two Prerequisites for merging data sets given below − It is because the variables form both data sets get merged as one record based when there is a match in the value of the common variable.
The total number of observations in the merged data set is often less than the sum of the number of observations in the original data sets. This is done using the MERGE statement and BY statement.
Multiple SAS data sets can be merged based on a specific common variable to give a single data set.
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