Combining Data Sets with SQL. Advanced SAS Programming

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Alvin Young
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1 Combining Data Sets with SQL Advanced SAS Programming

2 Joins SQL joins refer, in general, to a horizontal combination of tables: i.e.: some form of record matching. This makes SQL joins similar to merging in the data step. A very simple join

3 Simple Join proc sql create table join1 as select goals.*, performance.* from advsas.goals, advsas.performance quit The * operator selects all columns Lists tables for selection

4 Log What is the nature of the warning that we see? Merge expects (requires) variables that we join on to have the same name, SQL has no such requirement.

5 Consider proc sql create table join2 as select goals.*, performance.month as month1, performance.sales from advsas.goals, advsas.performance quit Produces no warning, and 4 variables in the resulting data set. Should also give some additional notion as to what process SQL undertook in joining these tables.

6 Cartesian Product The default joining of multiple data sets listed in the from statement is a full Cartesian product every record in one table is paired with every other. Most of the time we want a subset of that product

7 More Specific Joins One way to subset the full product is with a where statement. Select no longer chooses month from performance proc sql create table join3 as select goals.*, performance.sales from advsas.goals, advsas.performance where goals.month eq performance.month quit Subset to those that match on month

8 Final Product Result is similar to a data step merge on month

9 Inner Join This is also known as an inner join, which takes rows that match on the specified condition. proc sql create table join4 as select goals.*, performance.sales from advsas.goals join advsas.performance on goals.month eq performance.month quit On replaces where as the conditioning clause Join is used as an operator (inner is default)

10 Inner Joins vs. Data Step Merge Inner joins and data step merges are not identical. Consider proc sql create table join as select crewlist.*, sched.flightnum from advsas.crewlist, advsas.sched where crewlist.empid eq sched.empid quit versus data merge merge advsas.crewlist advsas.sched by empid run

11 Results Inner join takes matches present across all tables, merge preserves all records. We can get this behavior with merge by using the in= variable

12 Outer Joins Outer joins permit records lacking matches into the final table. proc sql create table joinfull as select crewlist.*, sched.flightnum from advsas.crewlist full join advsas.sched on crewlist.empid eq sched.empid quit Creates the same result as the previous merge.

13 Left and Right Join The left join preserves all rows from the first table listed. Hopefully the right join is selfexplanatory Consider the following joins and their results.

Left & Right Joins proc sql create table joinleft as select crewlist.*, sched.flightnum from advsas.crewlist left join advsas.sched on crewlist.empid eq sched.

14 Left & Right Joins proc sql create table joinleft as select crewlist.*, sched.flightnum from advsas.crewlist left join advsas.sched on crewlist.empid eq sched.empid quit Left table had row with no match proc sql create table joinright as select crewlist.*, sched.flightnum from advsas.crewlist right join advsas.sched on crewlist.empid eq sched.empid quit Right table did not

15 One-to-Many SQL behavior is not affected by oneto-many matches. Consider a merge of the accounts and transaction tables against the possible SQL joins.

16 One to Many Merge proc sort data=advsas.accounts out=acct by account_no run proc sort data=advsas.transactions out=trans by account_no run data one_many_merge merge trans acct by account_no run

17 Inner Join proc sql create table one_many_inner as select transactions.*, fname, lname from advsas.transactions, advsas.accounts where transactions.account_no eq accounts.account_no quit

18 Full Join proc sql create table one_many_full as select transactions.*, fname, lname from advsas.transactions full join advsas.accounts on transactions.account_no eq accounts.account_no quit

19 Left Join (transactions on left) proc sql create table one_many_left as select transactions.*, fname, lname from advsas.transactions left join advsas.accounts on transactions.account_no eq accounts.account_no quit

20 Right Join (transactions on left) proc sql create table one_many_right as select transactions.*, fname, lname from advsas.transactions right join advsas.accounts on transactions.account_no eq accounts.account_no quit

21 Many-to-Many The data step does not handle many-tomany merges in what would generally be considered a useful manner. Consider the following merge: data many_to_many merge advsas.salesstaff advsas.clients by district_no run

22 Result of many-to-many data step merge (check notes in log). When multiple records have same by value in both data sets, matches one-to-one then one-to-many (see PowerPoint on merge for processing details)

23 SQL The SQL join: proc sql create table many2many as select salesstaff.*, company from advsas.salesstaff, advsas.clients where salesstaff.district_no eq clients.district_no quit

24 Result Cartesian product is formed for all records with a matching key value Is the result different for any of the outer joins?

C H A P T E R 1 Introducing Data Relationships, Techniques for Data Manipulation, and Access Methods

C H A P T E R 1 Introducing Data Relationships, Techniques for Data Manipulation, and Access Methods Overview 1 Determining Data Relationships 1 Understanding the Methods for Combining SAS Data Sets 3