1. Notes on SQL

2. SQL Programming Employers increasingly tell us that they look for 3 things on a resume: SAS, R and SQL. In these notes you will learn: 1.What SQL is 2.Why it is used 3.The basics of SQL syntax And, we will go through a few REALLY fun and exciting examples.

3. SQL Programming What is SQL? SQL stands for “Structured Query Language”. It was designed as a language to manage data in relational database management systems (DBMS). The SQL language is sub-divided into several language elements, including: Queries, which retrieve the data based on specific criteria. This is the most important element of SQL. Clauses, which are constituent components of statements and queries. Expressions, which can produce either scalar values or tables consisting of columns and rows of data. Statements, which may have a persistent effect on schemas and data, or which may control transactions, program flow, connections, sessions, or diagnostics. SQL statements also include the semicolon statement terminator. Though not required on every platform, it is defined as a standard part of the SQL grammar.

4. Why is PROC SQL better than Data steps? The syntax is transferable to other SQL software packages You can join up to 250 SAS tables No need to sort any of the input tables When is Proc SQL not better than Data steps? Uses more memory than any regular data/procedure steps Could take longer than other procedures when working with very large contributing tables Logic flow becomes harder to implement SQL Programming

5. Why do we use SQL? SQL is used primarily to: Retrieve data from and manipulate tables/datasets Add or modify data values in a table/datasets Add, modify, or drop columns in a table/datasets Create tables and views Join/Merge multiple tables (whether or not they contain columns with the same name) Generate reports.

6. SQL Programming Why do we use SQL? You probably noticed that the previous list includes a lot of things that we do with DATA statements in SAS. In many cases, SQL is a better alternative to DATA statements in SAS – it is more efficient. Clarification regarding SQL in SAS… We use SQL like Data Statements in SAS…NOT like (most) Proc Statements. SQL is used to extract data, merge data and create variables…not to analyze data. Lets take a look…

7. SQL Programming Consider the Pennstate1 dataset. Lets say that you needed to: Only retain sex, earpierces, tattoos, height, height choice, looks and friends variables. Sort by sex. Delete observations with more than 4 earpierces. Create a new variable called HeightDifference which is the difference between their current height and their Height Choice. Create a new dataset called “Modeling” from the above requirements.

8. SQL Programming My guess is that at this point, you would use a DATA step and your code would look something like this: Data Modeling (keep = sex earprces tattoo height htchoice looks friends); set jlp.pennstate2; where earprces < 4; Heightdiff = Htchoice-Height; run; Proc sort data=modeling; by Sex; run; This code would run and produce what you need.

9. SQL Programming Here is what this same requirement would look like using Proc SQL: proc sql; create table work.modeling as Select sex,earprces,tattoo,height,htchoice,looks,friends, Htchoice-Height as HeightDiff from jlp.pennstate2 where earprces<4 order by sex; quit; What do you notice about this code that is unexpected in SAS?

10. SQL Programming Lets pull this apart: proc sql; create table work.modeling as

11. SQL Programming Select sex,earprces,tattoo,height,htchoice,looks,friends, Htchoice-Height as HeightDiff from jlp.pennstate2.

12. where earprces<4 order by sex; quit; SQL Programming

13. Lets look at another example…lets focus on categorizing a variable. Consider the UCDAVIS1 dataset. Create a new dataset called UCTEST. Only retain GPA, SEAT, SEX and ALCOHOL. Create a new variable “GPACAT” which is a categorization of the GPA variable…where <2 is low, <3 is medium and <4 is high. How would we do this without using SQL and using SQL…

14. Using a Data step, your code probably looks like this: Data UCTEST (keep = GPA GPACAT SEX ALCOHOL); set jlp.ucdavis1; Format GPACAT $CHAR7.; If GPA =. then GPACAT =" "; else if GPA <= 2 then GPACAT = "LOW"; else if GPA <= 3 then GPACAT = "MEDIUM"; else GPACAT = "HIGH"; Run; Proc print data=UCTEST; Run; SQL Programming  Why do we need this format statement?

15. Using SQL, your code probably looks like this: PROC SQL; CREATE TABLE work.UCTEST AS SELECT GPA,Sex,alcohol, CASE WHEN GPA =. THEN ' ‘ WHEN GPA<= 2.0 THEN 'LOW‘ WHEN GPA<= 3.0 THEN 'MEDIUM‘ ELSE 'HIGH‘ END AS GPACAT FROM jlp.ucdavis1; QUIT; SQL Programming What do you notice about this code that is different from the Data step?

16. SQL Programming Lets look at another example...lets focus on creating a new quantitative variable using a mathematical operator. Consider the UCDAVIS1 dataset again. Create a new dataset called UCTEST1. Create a new variable that is called “Leisure” which is the amount of TV time plus the amount of Computer time. Create a new variable that is 2x the sleep variable. Only retain those sitting in the front and the back. Sort the data by seat. How would we do this without using SQL and using SQL…

17. SQL Programming Using a Data step, your code probably looks like this: Data UCTEST1 (keep = TV Computer Sleepx2 Seat Leisure); set jlp.ucdavis1; Leisure = (TV + Computer); Sleepx2 = Sleep* 2; If seat = "Middle" then delete; Run; Proc sort data = UCTEST1; by seat; Run;

18. SQL Programming Using SQL, your code probably looks like this: PROC SQL; CREATE TABLE work.TEST AS SELECT TV, Computer, Sleep, Seat,(TV + Computer) AS Leisure FROM jlp.ucdavis1 WHERE SEAT IN ('Front', 'Back') ORDER BY SEAT; QUIT;

19. SQL Programming *The general form of PROC SQL includes the following: PROC SQL; SELECT CREATE TABLE...AS FROM WHERE 3.0> ORDER BY ; CASE WHEN END AS ; QUIT;

20. SQL Programming – Summary Statistics: http://www.tau.ac.il/cc/pages/docs/sas8/proc/zsumfunc.htm The above table can found here Proc SQL syntaxDescription AVG, MEAN means or average of values COUNT, FREQ, N number of nonmissing values CSS corrected sum of squares CV coefficient of variation (percent) MAX largest value MIN smallest value NMISS number of missing values PRT probability of a greater absolute value of Student's t RANGE range of values STD standard deviation STDERR standard error of the mean SUM sum of values SUMWGT sum of the WEIGHT variable values T Student's t value for testing the hypothesis that the population mean is zero USS uncorrected sum of squares VAR variance

21. Any Questions?