1. Preparing your data for analysis using SAS Landon Sego 24 April 2003 Department of Statistics UW-Madison

2. Assumptions That you have used SAS at least a few times. It doesn’t matter whether you run SAS in interactive mode (Windows) or in batch mode (Unix/Linux).

3. Interactive SAS for Windows

4. Editing SAS code in EMACS for Batch mode execution

5. Executing SAS in batch mode at the Linux prompt

6. Where we’re going… Rarely does data come to you in a form that is analyzable. As a best case, all you need to do is clean your data and check it for consistency. As a worst case, extensive manipulation of the data is needed in order to analyze. We want to get familiar with some tools in SAS used to check, clean, and manipulate data.

7. The dark toolbox SAS is like a toolbox as big as a garage—with thousands of tools. For many SAS users, this toolbox is dark and there is a monster lurking inside. Let’s turn on the light and meet a few of the tools available in SAS. No guarantees about the monster…

8. Published resources Lots available I’ve learned on my own and with the SAS documentation Google searches

9. SAS Online Documentation http://shelf.doit.wisc.edu/SASOnlineDocs/onldoc.htm or http://shelf.doit.wisc.edu/SASOnlineDocs/main.htm

10. What I use most often in the SAS Online Documentation Base SAS Software –SAS Language Reference: Concepts –SAS Language Reference: Dictionary –SAS Macro Language Reference –SAS Procedures Guide SAS/STAT –SAS/STAT User’s Guide

11. SAS Language Reference: Dictionary

12. SAS Procedures Guide

13. SAS/STAT User’s Guide

14. Conventions, terminology, and options

15. Conventions SAS terminology in red SAS code in blue

16. Basic terminology Data in SAS exists as a data set, with variables and observations. variables are the columns. observations are the rows. Two types of variables: Character and numeric. Character variables can range in length from 1 to 32,767 = 2 15 characters. Numeric variables can be virtually any size (within the limitations of the computer)

17. My favorite options On the first line of almost every SAS program I write, I include the following: options nodate nocenter nonumber ps=3000 ls=200 mprint mlogic symbolgen; These options control the format of the output and make macro code easier to debug

18. Importing data into SAS

19. Data can exist in many forms (text file, Excel spreadsheet, permanent SAS data set, etc.) Excel spreadsheets are probably the most common form. Can use DDE (dynamic data exchange) (Windows version of SAS only) But for Excel files, I like to use CSV file format (comma separated value). Works on any platform.

20. Excel  CSV text file  SAS data set Column of ‘j’s provide a buffer at the end of each line of text in the CSV file. If you are running SAS on a Linux or UNIX machine, you need to add the j’s (or use the dos2unix command to convert the CSV file to the text formatting used by UNIX).

21. Save Excel spreadsheet in the CSV format

22. How the CSV file looks (when viewed with a text editor) Location,Type,Length,j Albuquerque,1,1.414,j Albuquerque,1,2.000,j Albuquerque,1,1.414,j Albuquerque,1,2.236,j Albuquerque,2,2.000,j Albuquerque,2,2.236,j Lexington,1,2.000,j

23. SAS code to import the CSV file data fake; infile ‘c:\mydata\fake.csv’ dsd firstobs=2; input location :$14. type length; proc print; run; Note, if I were to use: input location $ type length; it would truncate the “location” variable to 8 characters in length.

24. Results from the proc print Obs location type length 1 Albuquerque 1 1.414 2 Albuquerque 1 2.000 3 Albuquerque 1 2.236 4 Albuquerque 2 2.000 5 Albuquerque 2 2.236 6 Albuquerque 2 2.236 7 Lexington 1 2.000 8 Lexington 1 2.000 9 Lexington 1 1.414 10 Lexington 1 2.000 11 Lexington 2 1.732 12 Lexington 2 1.732 13 Lexington 2 2.236 14 Johannesburg 1 2.000 15 Johannesburg 1 2.236 16 Johannesburg 1 2.236 17 Johannesburg 2 2.000 18 Johannesburg 2 1.414 19 Johannesburg 2 2.000

25. Checking and summarizing data

26. proc contents data=fake; proc freq data=fake; run; This is IMPORTANT! Don’t take for granted that there aren’t mistakes in your data.

27. Result from proc contents Data Set Name: WORK.FAKE Observations: 19 Member Type: DATA Variables: 3 Engine: V8 Indexes: 0 Created: 11:46 Wednesday, April 16, 2003 Observation Length: 32 Last Modified: 11:46 Wednesday, April 16, 2003 Deleted Observations: 0. File Name: /tmp/sastmp_sego/SAS_workB6B700000836_ gstat201.stat.wisc.edu/fake.sas7bdat. -----Alphabetic List of Variables and Attributes----- # Variable Type Len Pos ----------------------------------- 3 length Num 8 8 1 location Char 14 16 2 type Num 8 0

28. Results from proc freq The FREQ Procedure Cumulative Cumulative location Frequency Percent Frequency Percent ----------------------------------------------------------------- Albuquerque 6 31.58 6 31.58 Johannesburg 6 31.58 12 63.16 Lexington 7 36.84 19 100.00 Cumulative Cumulative type Frequency Percent Frequency Percent --------------------------------------------------------- 1 10 52.63 10 52.63 2 9 47.37 19 100.00 Cumulative Cumulative length Frequency Percent Frequency Percent ----------------------------------------------------------- 1.414 3 15.79 3 15.79 1.732 2 10.53 5 26.32 2 8 42.11 13 68.42 2.236 6 31.58 19 100.00

29. Selecting subsets of the data

30. Selecting observations (rows) A large contiguous group of observations Specific observation numbers Using selection criteria e.g. when the location is “Lexington” or when the length is between 1 and 2.

31. Selecting a group of contiguous observations data smallfake; set fake (firstobs=10 obs=15); proc print; Obs location type length 1 Lexington 1 2.000 2 Lexington 2 1.732 3 Lexington 2 1.732 4 Lexington 2 2.236 5 Johannesburg 1 2.000 6 Johannesburg 1 2.236 Selects observations 10 through 15 Data set options

32. Selecting specific observation numbers data smallfake; set fake; if _n_ in (7,11,16); proc print; Obs location type length 1 Lexington 1 2.000 2 Lexington 2 1.732 3 Johannesburg 1 2.236 Selects observation numbers 7, 11, and 16.

33. Selection criteria: where statement data smallfake; set fake; where location = ‘Lexington’; or where location ne ‘Lexington’; or where location in (‘Lexington’, ’Albuquerque’); or where (1 le length le 2); or where (length > 2.3);

34. Selection criteria: if statement data smallfake; set fake; if location in (‘Lexington’, ’Albuquerque’); or if location = ‘Lexington’ | location = ‘Albuquerque’; or if location = ‘Johannesburg’ then delete; These three if statements produce identical results.

35. Some comparison operators neor^= not equals to eqor=equals to geor>=greater than or equal to gtor> greater than leor<= less than or equal to ltor<less than inif contained in a group not inif not contained in a group andor&and logical operator oror|or logical operator

36. Selecting and managing variables

37. Selecting variables using “keep” data smallfake (keep = location length); set fake; where type = 1; (type is available for processing, but not written to smallfake data set) data smallfake; set fake (keep = location length); (type is not available for processing) Data set options

38. Selecting variables using “drop” data smallfake (drop = type); set fake; where type = 1; (type is available for processing, but not written to smallfake data set) data smallfake; set fake (drop = type); (type is not available for processing) Data set options

39. Renaming variables data fake1; set fake (rename=(location=place type=trt)); where trt = 1; data fake2 (rename=(location=place type=trt)); set fake; where type =1; data fake3 (drop = location type); set fake; where type = 1; place = location; trt = type; These three pieces of code achieve the same result. Look closely at the where statements. location place type trt

40. Concatenation

41. Concatenation (stacking) SAS can stack multiple data sets on top of one another. Pay attention whether or not the variables and their attributes (length and variable type) match among the different data sets. Can use the set statement or proc append to concatenate data sets.

42. Using the set statement to concatenate data sets Suppose you wanted to stack the three data sets fake, faux, and fraud on top of one other: data fantastic; set fake faux fraud;

43. Using proc append to concatenate data sets proc append concatenates only two data sets at a time—and typically these data sets must have the same variable names with the same attributes. proc append base=fake data=faux; Here the observations in faux are tacked onto the end of the fake data set. The combined data set is called fake.

44. Splitting data into several data sets Supposed we want all Albuquerque observations to go into a data set called albuq, the Lexington observations to go into the data set lexing, and observations that have lengths larger than 3.0 into the data set large. data albuq lexing large; set fake; if location = ‘Albuquerque’ then output albuq; else if location = ‘Lexington’ then output lexing; if length gt 3 then output large;

45. Merging data

46. Merging (combining) data Merging data sets places two or more data sets side by side into a single data set. If you simply want place two data sets side by side (1 to 1 merging): data faux; set faux (rename=(location=location1 type=type1)); data fantastic; merge fake faux; proc print data = fantastic;

47. Results of 1 to 1 merge Obs location type length location1 type1 weight 1 Albuquerque 1 1.414 Lexington 2 4.456 2 Albuquerque 1 2.000 Lexington 2 2.546 3 Albuquerque 1 2.236 Lexington 2 3.789 4 Albuquerque 2 2.000 Lexington 1 2.457 5 Albuquerque 2 2.236 Lexington 1 4.456 6 Albuquerque 2 2.236 Lexington 1 4.551 7 Lexington 1 2.000 Lexington 1 3.246 8 Lexington 1 2.000 Johannesburg 1 2.998 9 Lexington 1 1.414 Johannesburg 1 2.721 10 Lexington 1 2.000 Johannesburg 1 2.115 11 Lexington 2 1.732 Johannesburg 2 3.489 12 Lexington 2 1.732 Johannesburg 2 3.447 13 Lexington 2 2.236 Johannesburg 2 4.122 14 Johannesburg 1 2.000 Albuquerque 1 2.118 15 Johannesburg 1 2.236 Albuquerque 1 2.871 16 Johannesburg 1 2.236 Albuquerque 1 2.516 17 Johannesburg 2 2.000 Albuquerque 2 3.445 18 Johannesburg 2 1.414 Albuquerque 2 2.998 19 Johannesburg 2 2.000 Albuquerque 2 3.549

48. Merging data with a “by” variable All data sets that will be merged must first be sorted by the linking variables: proc sort data = fake; by location type; proc sort data = faux; by location type; data fantastic; merge fake faux; by location type; proc print data = fantastic;

49. Results of merging with “by” variables Obs location type length weight 1 Albuquerque 1 1.414 2.118 2 Albuquerque 1 2.000 2.871 3 Albuquerque 1 2.236 2.516 4 Albuquerque 2 2.000 3.445 5 Albuquerque 2 2.236 2.998 6 Albuquerque 2 2.236 3.549 7 Johannesburg 1 2.000 2.998 8 Johannesburg 1 2.236 2.721 9 Johannesburg 1 2.236 2.115 10 Johannesburg 2 2.000 3.489 11 Johannesburg 2 1.414 3.447 12 Johannesburg 2 2.000 4.122 13 Lexington 1 2.000 2.457 14 Lexington 1 2.000 4.456 15 Lexington 1 1.414 4.551 16 Lexington 1 2.000 3.246 17 Lexington 2 1.732 4.456 18 Lexington 2 1.732 2.546 19 Lexington 2 2.236 3.789

50. More about merging When you merge with a by statement, you may only want observations that have by-variable “matches” in both data sets. fakefraud

51. Assume both fake and fraud are sorted by location and type. data fantastic; merge fake (in = tmp1) fraud (in = tmp2); by location type; from_fake = tmp1; from_fraud = tmp2; proc print; Using (in =) data set option

52. from_ from_ Obs location type length thickness fake fraud 1 Albuquerque 1 1.414 1.120 1 1 2 Albuquerque 1 2.000 1.120 1 1 3 Albuquerque 1 2.236 1.120 1 1 4 Albuquerque 2 2.000. 1 0 5 Albuquerque 2 2.236. 1 0 6 Albuquerque 2 2.236. 1 0 7 Albuquerque 3. 1.400 0 1 8 Albuquerque 3. 1.540 0 1 9 Albuquerque 3. 1.968 0 1 10 Johannesburg 1 2.000 1.320 1 1 11 Johannesburg 1 2.236 1.945 1 1 12 Johannesburg 1 2.236 1.945 1 1 13 Johannesburg 2 2.000 1.440 1 1 14 Johannesburg 2 1.414 1.440 1 1 15 Johannesburg 2 2.000 1.440 1 1 16 Johannesburg 3. 1.521 0 1 17 Johannesburg 3. 1.500 0 1 18 Lexington 1 2.000. 1 0 19 Lexington 1 2.000. 1 0 20 Lexington 1 1.414. 1 0 21 Lexington 1 2.000. 1 0 22 Lexington 2 1.732 1.160 1 1 23 Lexington 2 1.732 0.783 1 1 24 Lexington 2 2.236 0.783 1 1 25 Lexington 4. 1.372 0 1 26 Lexington 4. 1.980 0 1 Identifying obs from both data sets

53. Now select observations that are common to both data sets: data fantastic; merge fake (in = tmp1) fraud (in = tmp2); by location type; if tmp1=1 and tmp2=1; proc print; Using (in =) data set option

54. Obs location type length thickness 1 Albuquerque 1 1.414 1.120 2 Albuquerque 1 2.000 1.120 3 Albuquerque 1 2.236 1.120 4 Johannesburg 1 2.000 1.320 5 Johannesburg 1 2.236 1.945 6 Johannesburg 1 2.236 1.945 7 Johannesburg 2 2.000 1.440 8 Johannesburg 2 1.414 1.440 9 Johannesburg 2 2.000 1.440 10 Lexington 2 1.732 1.160 11 Lexington 2 1.732 0.783 12 Lexington 2 2.236 0.783 After selecting for observations in common:

55. Merge mania data fantastic; merge fake fraud; by location type; data fantastic; merge fake (in=tmp1) fraud; by location type; if tmp1 = 1; data fantastic; merge fake (in=tmp1) fraud (in=tmp2); by location type; if tmp1 =1 and tmp2 = 1;

56. Creating new variables

57. data fantastic; merge fake faux; by location type; newcode = substr(location,1,1) || '-' || trim(left(type)); growth_index = length + weight**2; if (growth_index gt 15) then large = '*'; else large = ' ';

58. Results of new variables growth_ Obs location type length weight newcode index large 1 Albuquerque 1 1.414 2.118 A-1 5.8999 2 Albuquerque 1 2.000 2.871 A-1 10.2426 3 Albuquerque 1 2.236 2.516 A-1 8.5663 4 Albuquerque 2 2.000 3.445 A-2 13.8680 5 Albuquerque 2 2.236 2.998 A-2 11.2240 6 Albuquerque 2 2.236 3.549 A-2 14.8314 7 Johannesburg 1 2.000 2.998 J-1 10.9880 8 Johannesburg 1 2.236 2.721 J-1 9.6398 9 Johannesburg 1 2.236 2.115 J-1 6.7092 10 Johannesburg 2 2.000 3.489 J-2 14.1731 11 Johannesburg 2 1.414 3.447 J-2 13.2958 12 Johannesburg 2 2.000 4.122 J-2 18.9909 * 13 Lexington 1 2.000 2.457 L-1 8.0368 14 Lexington 1 2.000 4.456 L-1 21.8559 * 15 Lexington 1 1.414 4.551 L-1 22.1256 * 16 Lexington 1 2.000 3.246 L-1 12.5365 17 Lexington 2 1.732 4.456 L-2 21.5879 * 18 Lexington 2 1.732 2.546 L-2 8.2141 19 Lexington 2 2.236 3.789 L-2 16.5925 *

59. Common functions used to manipulate text strings compress index left scan substr trim Refer to SAS Online Docs : Base SAS Software SAS Language Reference: Dictionary Dictionary of Language Elements Functions and Call Routines

60. by-group processing

61. Suppose you wanted a subset of the data that contained the observation with the smallest length from each location. proc sort data = fake; by location length; data shortest; set fake; by location length; first = first.location; last = last.location;

62. output from by-group processing Obs location type length first last 1 Albuquerque 1 1.414 1 0 2 Albuquerque 1 2.000 0 0 3 Albuquerque 2 2.000 0 0 4 Albuquerque 1 2.236 0 0 5 Albuquerque 2 2.236 0 0 6 Albuquerque 2 2.236 0 1 7 Johannesburg 2 1.414 1 0 8 Johannesburg 1 2.000 0 0 9 Johannesburg 2 2.000 0 0 10 Johannesburg 2 2.000 0 0 11 Johannesburg 1 2.236 0 0 12 Johannesburg 1 2.236 0 1 13 Lexington 1 1.414 1 0 14 Lexington 2 1.732 0 0 15 Lexington 2 1.732 0 0 16 Lexington 1 2.000 0 0 17 Lexington 1 2.000 0 0 18 Lexington 1 2.000 0 0 19 Lexington 2 2.236 0 1

63. by-group processing proc sort data = fake; by location length; data shortest; set fake; by location length; if first.location = 1; Obs location type length 1 Albuquerque 1 1.414 2 Johannesburg 2 1.414 3 Lexington 1 1.414

64. Basic macros

65. SAS macros allow you to easily program repetitive tasks. On the surface, creating a SAS macro is very similar to creating a function in R or S-Plus. SAS Macro is actually a text generation tool.

66. %macro analyze(dataset=,response=); proc mixed data = &dataset; class location type; model &response = location type; lsmeans location type; ods output lsmeans=model_means; data model_means; set model_means; variable = "&response"; proc append base=results data=model_means; %mend analyze; %analyze(dataset=fake,response=length) %analyze(dataset=faux,response=weight) proc print data = results; Macro example Name macro, begin macro definition, identify macro variables Code to be generated by macro Note the use of proc append End macro definition Call macro Print results

67. Results from macro code Obs Effect location type Estimate StdErr DF tValue Probt variable 1 location Albuquerque _ 2.0203 0.1243 15 16.25 <.0001 length 2 location Johannesburg _ 1.9810 0.1243 15 15.94 <.0001 length 3 location Lexington _ 1.8728 0.1155 15 16.21 <.0001 length 4 type 1 1.9621 0.09678 15 20.27 <.0001 length 5 type 2 1.9540 0.1015 15 19.25 <.0001 length 6 location Albuquerque _ 2.9162 0.2844 15 10.25 <.0001 weight 7 location Johannesburg _ 3.1487 0.2844 15 11.07 <.0001 weight 8 location Lexington _ 3.6842 0.2643 15 13.94 <.0001 weight 9 type 1 2.9614 0.2214 15 13.37 <.0001 weight 10 type 2 3.5379 0.2322 15 15.23 <.0001 weight

68. proc transpose

69. Rearranging data with proc transpose Obs year cultivar Effect trt Estimate StdErr DF tValue Probt 1 95 1 Intercept _ 1.5930 0.4477 38 3.56 0.0010 2 95 1 trt 1 0.2987 0.7485 35 0.40 0.6923 3 95 1 calevel _ -0.00155 0.002401 35 -0.65 0.5229 4 95 1 calevel*trt 1 -0.00010 0.003992 35 -0.02 0.9804 5 95 2 Intercept _ 0.5331 0.2782 38 1.92 0.0628 6 95 2 trt 1 0.3637 0.3501 36 1.04 0.3058 7 95 2 calevel _ 0.002348 0.001570 36 1.49 0.1437 8 95 2 calevel*trt 1 -0.00170 0.002173 36 -0.78 0.4388 13 96 1 Intercept _ 1.4129 0.3650 10 3.87 0.0031 14 96 1 trt 1 0.4670 0.4387 160 1.06 0.2887 15 96 1 calevel _ 0.003074 0.001146 160 2.68 0.0081 16 96 1 calevel*trt 1 -0.00127 0.001387 160 -0.92 0.3598 Consider this output from proc mixed:

70. Results from proc transpose proc transpose data=solution out=tsolution; by year cultivar; var estimate; id effect; calevel_ Obs year cultivar _NAME_ Intercept trt calevel trt 1 95 1 Estimate 1.5930 0.2987 -0.00155 -0.00010 2 95 2 Estimate 0.5331 0.3637 0.002348 -0.00170 3 95 3 Estimate 1.3996 0.08453 -0.00121 0.000326 4 96 1 Estimate 1.4129 0.4670 0.003074 -0.00127 5 96 2 Estimate 1.9705 0.1312 0.003656 -0.00073 6 96 3 Estimate 3.2758 -0.1568 -0.00131 0.001427 7 96 4 Estimate 2.6396 0.7034 0.001114 -0.00237 8 97 1 Estimate 1.3943 0.2684 -0.00026 -0.00120 9 97 2 Estimate 1.5388 -0.2840 -0.00183 0.002834

71. Parting words of advice

72. Attributes of SAS SAS is read/write intensive. Every time you create a data set, the data set is written to the disk. Where does it get written? To the SAS Work Library, which is assigned to a directory somewhere…..use proc contents to find out. For CALS HP users and PC users, the SAS Work Library resides on the actual machine.

73. Attributes of SAS Users of the AFS system beware! (Stat department, CS department) The SAS Work Library is assigned to your account in AFS—not to the local machine that is running SAS. Your local computer running SAS AFS recording and reading your SAS data sets Network traffic

74. Assigning the SAS Work Library To assign the SAS work library to a local directory (when running in batch mode on a Linux or Unix system): $ sas mysascode.sas -work /scratch

75. Synthesis Most of what we’ve covered today involves the data step. Many of the techniques shown in this presentation can be applied together in a single data step. Now that you know the names of some of the tools, use the Online Documentation!