- SAS Tutorial
The joins in SAS are one-to-one joins or one-to-many joins. The advantage of theses types of joins is that SAS carries out the joins faster. However, the only condition to create these joins is that the input tables are sorted on the common variable(s), i.e. The variable(s) you want to use to join on. MERGE Example The Input Data Sets. As a SAS® Programmer, one of our common tasks is to merge data from two or more datasets. Most merges are 1-to-1 or 1- to-many, i.e. There is at least one dataset with a sequence of variables that create a unique record identifier.
- SAS Data Set Operations
- SAS Data Representation
- SAS Basic Statistical Procedure
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Multiple SAS data sets can be merged based on a specific common variable to give a single data set. This is done using the MERGE statement and BY statement. 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. 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.
There are two Prerequisites for merging data sets given below −
- input data sets must have at least one common variable to merge on.
- input data sets must be sorted by the common variable(s) that will be used to merge on.
The basic syntax for MERGE and BY statement in SAS is −
Following is the description of the parameters used −
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Data-set1,Data-set2 are data set names written one after another.
Common Variable is the variable based on whose matching values the data sets will be merged.
Let us understand data merging with the help of an example.
Consider two SAS data sets one containing the employee ID with name and salary and another containing employee ID with employee ID and department. In this case to get the complete information for each employee we can merge these two data sets. The final data set will still have one observation per employee but it will contain both the salary and department variables.
The above result is achieved by using the following code in which the common variable (ID) is used in the BY statement. Please note that the observations in both the datasets are already sorted in ID column.
Missing Values in the Matching Column
There may be cases when some values of the common variable will not match between the data sets. In such cases the data sets still get merged but give missing values in the result.
ExampleConsider the case of employee ID 3 missing from the dataset salary and employee ID 6 missing form data set DEPT. When the above code is applied, we get the below result.
Merging only the Matches
To avoid the missing values in the result we can consider keeping only the observations with matched values for the common variable. That is achieved by using the IN statement. The merge statement of the SAS program needs to be changed.
In the below example, the IN= value keeps only the observations where the values from both the data sets SALARY and DEPT match.
Upon execution of the above SAS program with the above changed part, we get the following output.
1. One-to-one merge
Below we have a file containing family id, father’s name and income. We also have a file containing income information for multiple years. We would like to match merge the files together so we have the dads observation on the same line with the faminc observation based on the key variable famid. In proc sql we use where statement to do the matching as shown below.
2. One-to-many merge
Imagine that we had a file with dads like we saw in the previous example, and we had a file with kids where a dad could have more than one kid. Matching up the 'dads' with the 'kids' is called a 'one-to-many' merge since you are matching one dad observation to possibly many kids records. The dads and kids records are shown below. Notice here we have variable fid in the first data set and famid in the second. These are the variables that we want to match. When we merge the two using proc sql, we don’t have to rename them, since we can use data set name identifier.
3. Renaming variables with the same name in merging
Below we have the files with the information about the dads and family, but look more closely at the names of the variables. In the dads file, there is a variable called inc98, and in the family file there are variables inc96, inc97 and inc98.
Let’s merge them using the same strategy used in our previous example on merging. We see below that we lost variable inc98 from the second dataset faminc. Proc sql uses the column from the first data set in case of same variable names from both datasets. This may not be what we want.
Many To Many Merge Proc Sql
In proc sql we can rename the variables using the as statement shown below.
4. Using full join to handle mismatching records in a one-to-one merge
Sas Merge One To Many Functions
The two datasets may have records that do not match. Below we illustrate this by including an extra dad (Karl in famid 4) that does not have a corresponding family, and there are two extra families (5 and 6) in the family file that do not have a corresponding dad.
Let’s apply the previous example to these two datasets. We see that the unmatched records have been dropped out in the merged data set, since the where statement eliminated them.
What if we want to keep all the records from both datasets even they do not match? The following proc sql does it in a more complex way. Here we create two new variables. One is indic, an indicator variable that indicates whether an observation is from both datasets, 1 being from both datasets and 0 otherwise. Another variable is fid, a coalesce of famid from both datasets. This gives us more control over our datasets. We can decide if we have a mismatch and where the mismatch happens.
5. Producing all the possible distinct pairs of the values in a column
Let’s say that we have a data set containing a variable called city. We want to create allpossible distinct pairs of cities appeared in the variable. This would be reallytricky to do if we only use a data step. But it can be accomplished fairly straightforwardly with SAS proc sql asshown below. Proc sql is first used to select distinctcities and to save them to a new dataset. It is used again to create alldistinct pairs of cities. As shown below, there are seven different places. Therefore there will be 7*6/2 =21 pairs of cities.