Ann Arbor ASA ‘Up and Running’ Series: The SAS System

1. Ann Arbor ASA‘Up and Running’ Series:The SAS System Sponsored by The Department of Statistics and the Ann Arbor Chapter of American Statistical Association,

2. Contents • Starting SAS • User Interface • Libraries • Syntax • Getting Data into SAS • Examining Data • Manipulating Data • Descriptive Statistics • Graphing Data • Statistics in SAS

3. Starting SAS Start=>All Programs=> Math & Statistics=>SAS =>SAS 9.2 (32) English

4. User Interface Log Comments, warnings, etc. Program Editor: Write and submit commands Explorer/Results Output (not seen)

5. Libraries • SAS requires the creation of Library folders to save the data • Libraries are accessed through LIBREF or LIBNAME commands • Four Libraries are defined by default, at the start of SAS • Maps • SASHELP: holds help info and sample datasets • SASUSER: holds settings, etc. • WORK: default temporary Library for each session • All data stored in this folder will be deleted at the end of each SAS session • It is recommended the creation of permanent files/Libraries

6. Libraries • Create a folder called ‘My_Files’ on your desktop. • Run this command in SAS: LIBNAME a "C:\Users\uniquename\Desktop\My_Files"; • Refer to datasets in that folder by with the prefix ‘a.datasetname’. • TIP: Use memorable names for libraries, rather than ‘a’ (e.g., ‘raw’, ‘final’, ‘time1’, etc)

7. Syntax • SAS divides commands into two groups • DATA step • create/alter datasets • PROC (Procedures) • perform statistical analyses or generate reports. • Some exceptions to the rule: • DATA step can be used to generate reports • PROC IMPORT creates a data set • PROC SORT alters data sets (without telling you!)

8. Getting data into SAS • PROC IMPORT • Allows the reading of standard file types • Allows the reading of plain text, with user-specified delimiters (i.e., the characters which separate the data) • DATA step • Allows the reading of non-standard file types, complex file structures, and unusual delimiters.

9. PROC IMPORT • Place pointer in Editor window • In Menu Bar: File  Import data • Follow the wizard • When asked if you want SAS to save the syntax to a file, click “Browse...” and give a filename • SAS will generate and run the syntax • Examine Log for errors

10. PROC IMPORT

11. PROC IMPORT • When asked if you want SAS to save the syntax to a file, • click “Browse...” and give a filename

12. PROC IMPORT

13. PROC IMPORT • Examine Log for errors

14. PROC IMPORT

15. PROC IMPORT • For example, SAS generated the following syntax: PROC IMPORT OUT= WORK.class2 DATAFILE= "C:\Desktop\class2.xls" DBMS=EXCEL5 REPLACE; GETNAMES=YES; RUN; • Examine and understand the syntax - you’ll learn more!

16. DATA step • SAS syntax can be used to read in raw data files (.txt, .csv files), specifying which variables to read in, which ones are text/numeric, combining multiple rows into one case, etc. • However, this is a more advanced topic. • Follow up with an Intro class from CSCAR, or by going through examples from the literature (e.g., ‘The Little SAS Book’).

17. Examining Data • VIEWTABLE Window • Select dataset icon in Explorer • PROC CONTENTS • Produces a listing of data set information, including the variables and their properties • PROC PRINT • Prints a subset of variables or cases to the output window

18. VIEWTABLE Window

19. PROC CONTENTS • In the Editor window, type: proc contents data=a.class2; run; • Highlight the syntax • Submit for processing • Click on icon of ‘running-man’ • Right click on selected syntax  Submit Selection

20. PROC CONTENTS

21. PROC PRINT • In the Editor window, type: proc print data=a.class2; run; • Submit for processing

22. PROC PRINT

23. Manipulating Data • Usually done within a data step • Match data sets using a shared key variable • Create new variables, or drop/rename existing variables • Take one or more subsets of the data • Sort the data by specific variable(s). • Overwrite existing or create new datasets • PROC SORT • Adding/Removing variables • Merging Datasets

24. PROC SORT • In the Editor window, type: proc sort data=a.class2; by age descending weight height; run; • Submit for processing • WARNING: PROC SORT alters data • Store in a new dataset out=‘newdatasetname’;

25. Adding/Removing variables • Create new data set, compute new variables, remove unwanted variables data a.class2metric (drop=weight height sex age); set a.class2; height_cm=height*2.54; weight_kg=weight/2.2; label height_cm=‘Height in CM’ weight_kg=‘Weight in Kilograms’; run; • Submit for processing

26. Merging Datasets • Data sets must be sorted by the same key variable(s) proc sort data=a.class2; by name; proc sort data=a.class2metric; by name; data a.classmerged; merge a.class2 a.class2metric; by name; run; • Submit for processing

27. Merging Datasets

28. Descriptive Statistics • PROC FREQ • Produces a table of counts and percentages • For cross-tabulations, statistical tests can also be performed; e.g., independence testing • PROC MEANS • Produces descriptive statistics such as mean, standard deviation, minimum, maximum

29. PROC FREQ • In the Editor window, type proc freq data=a.class2; tables age*sex; run; • Submit for processing

30. PROC FREQ

31. PROC MEANS • In the Editor window, type proc means data=a.class2; var age weight height; run; • Submit for processing

32. PROC MEANS

33. Graphing DataPROC GPLOT • Simple bivariate scatterplot • Separate lines • Multiple variables scatterplot • Options

34. PROC GPLOT • Simple bivariate scatterplot: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height; run; • Submit for processing

35. PROC GPLOT

36. PROC GPLOT • To graph separate lines for each level of a categorical variable, type: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height = sex; run; • Submit for processing

37. PROC GPLOT

38. PROC GPLOT • Multiple variables on the same graph: proc gplot data=a.class2; symbol1 value=dot interpol=rl color=blue; symbol2 value=dot interpol=rl color=red; plot weight * age; plot2 height * age; run; quit; • Submit for processing

39. PROC GPLOT

40. value=___ Any character enclosed in single quotes Special characters dot plus sign star square ...and many others interpol=___ RL / RQ / RC linear quadratic cubic regression curves JOIN connects consecutive points (line graph) BOX PROC GPLOT

41. Statistics in SAS • PROC CORR • Correlational analyses • PROC REG • Statistical Regression • PROC UNIVARIATE • To assess normality of regression residuals

42. PROC CORR • Compute bivariate correlation coefficients proc corr data = a.class2; var age; with height weight; run;

43. PROC CORR

44. PROC REG Run a regression on merged ‘class’ dataset Save residuals and predicted values in an output dataset Request residual plot proc reg data=a.classmerged; model height_cm=age weight / partial; output out=reg_data p=predict r=resid rstudent=rstudent; plot rstudent. * height_cm; run; quit; Notes – the quit command terminates the regression procedure; otherwise it keeps running; the output data set will be in the work library, since no library was specified.

45. PROC REG

46. PROC REG

47. PROC UNIVARIATE • Assess normality of regression residuals stored in the output dataset from PROC REG: proc univariate data=reg_data; var rstudent; histogram; qqplot / normal (mu=est sigma=est); run; quit;

48. PROC UNIVARIATE

49. PROC UNIVARIATE

50. QUESTIONS