SAS - Merge Data Sets

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.

Syntax

The basic syntax for MERGE and BY statement in SAS is −

MERGE Data-Set 1 Data-Set 2
BY Common Variable

Following is the description of the parameters used −

  • 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.

Data Merging

Let us understand data merging with the help of an example.

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.

# Data set 1	
ID NAME SALARY	
1 Rick 623.3		 
2 Dan 515.2 		
3 Mike 611.5 		
4 Ryan 729.1 
5 Gary 843.25 
6 Tusar 578.6 
7 Pranab 632.8 
8 Rasmi 722.5 

# Data set 2
ID DEPT
1 IT 
2 OPS
3 IT 
4 HR 
5 FIN 
6 IT 
7 OPS
8 FIN 

# Merged data set
ID NAME SALARY DEPT	
1 Rick 623.3	IT 		 
2 Dan 515.2 	OPS	
3 Mike 611.5 	IT 	
4 Ryan 729.1    HR 
5 Gary 843.25   FIN 
6 Tusar 578.6   IT 
7 Pranab 632.8  OPS
8 Rasmi 722.5   FIN

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.

DATA SALARY; 
   INPUT empid name $ salary  ; 
DATALINES; 
1 Rick 623.3		 
2 Dan 515.2 		
3 Mike 611.5 		
4 Ryan 729.1 
5 Gary 843.25 
6 Tusar 578.6 
7 Pranab 632.8 
8 Rasmi 722.5 
;
RUN; 
DATA DEPT; 
   INPUT empid dEPT $ ; 
DATALINES; 
1 IT 
2 OPS
3 IT 
4 HR 
5 FIN 
6 IT 
7 OPS
8 FIN 
;
RUN; 
DATA All_details;
MERGE SALARY DEPT;
BY (empid);
RUN;
PROC PRINT DATA = All_details; 
RUN;

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.

Example

Consider 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.

ID NAME SALARY DEPT	
1 Rick 623.3	IT 		 
2 Dan 515.2 	OPS	
3 .		.		IT
4 Ryan 729.1    HR 
5 Gary 843.25   FIN 
6 Tusar 578.6   .
7 Pranab 632.8  OPS
8 Rasmi 722.5   FIN

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.

Example

In the below example, the IN= value keeps only the observations where the values from both the data sets SALARY and DEPT match.

DATA All_details;
MERGE SALARY(IN = a) DEPT(IN = b);
BY (empid);
IF a = 1 and b = 1;
RUN;
PROC PRINT DATA = All_details; 
RUN;

Upon execution of the above SAS program with the above changed part, we get the following output.

1 Rick 623.3	IT 		 
2 Dan 515.2 	OPS	
4 Ryan 729.1    HR 
5 Gary 843.25   FIN 
7 Pranab 632.8  OPS
8 Rasmi 722.5   FIN
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