Joining / Merging in SAS – alternate approaches (including really efficient ones!)
One of the most common operation for any analyst is merging datasets. As per my estimate, an analyst spends at least 10 – 20% of his productive time joining and merging datasets. If you spend so much time doing joins / merges, it is extremely critical that you join datasets in most efficient manner. This is the thought behind this post.
Traditionally, databases have been designed in a manner where tables capture details of individual functional area.
Example below shows two tables, one capturing patient details in a clinic (from one time registrations) and second table showing their appointment details.
In order to analyze things like:
- Which customer has walked in how many times in last month?
- Which kind of customers have walked in more last month?
- What are the common reasons for people walking in?
we need to join the two tables.
I’ll cover various ways in which you can do this in SAS:[stextbox id=”section”]1. Sort / sort / Merge:[/stextbox]
This is the most common approach used in SAS. In order to use data step command, we need to sort the datasets first and then merge using the common key:[stextbox id=”grey”]
proc sort data=patient_details; by pat_id;
proc sort data=appointment_details; by pat_id;
merge patient_details (in=a) appointment_details (in=b);
/* note by variables are in the same order as sort by */
if a and b; /* Control statement, other options: if a; if b; if not a; if not b;*/[/stextbox]The control statement defines the kind of merge. By specifying “if a and b”, values present in both the tables will be picked. [stextbox id=”section”]2. PROC SQL:[/stextbox]
If you are used to writing SQL, PROC SQL might be the easiest way to learn joins in SAS[stextbox id=”grey”]
PROC SQL; CREATE TABLE analysis_set0 AS SELECT a.*, b.* FROM patient_details a INNER JOIN /* control statement*/ /*other options LEFT JOIN, RIGHT JOIN, OUTER JOIN*/ appointment_details b ON a.pat_id=b.pat_id; QUIT; RUN;
[/stextbox][stextbox id=”section”]3. PROC FORMAT:[/stextbox]
This is one of the latest ways I have learnt, but the most efficient one. Using this method, we convert the smaller file into a format.[stextbox id=”grey”]
DATA format1; SET patient_details (keep = PAT_ID); fmtname = '$pat_format'; label = '*'; RENAME pat_id=start; RUN;
PROC SORT data=format1 nodupkey; by pat_id; RUN;
PROC FORMAT CNTLIN=format1; RUN;[/stextbox]
The first step creates a dataset format1 from patient_details. PROC FORMAT then converts it into a format. Finally we use[stextbox id=”grey”]
DATA analysis_set; SET appointment_details; if PUT(pat_id,$pat_format.) = '*'; RUN;[/stextbox]
This way to join datasets typically takes 30 – 40% lower computation time compared to the two approaches mentioned above.
Since this might look advanced SAS, I will devote one more post explaining formats in more details.
In the meanwhile, if you know of any other way to join tables, please let me know.
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