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SAS Macros are the Cure for Quality Control Pains Gary McQuown Data and Analytic Solutions.

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Presentation on theme: "SAS Macros are the Cure for Quality Control Pains Gary McQuown Data and Analytic Solutions."— Presentation transcript:

1 SAS Macros are the Cure for Quality Control Pains Gary McQuown Data and Analytic Solutions

2 Rants and Raves of a SAS Programmer

3 Purpose I. Quality Control II. SAS Macros for Quality Control III. Sources of SAS Macros and QC Code

4 I. Quality Control An ongoing effort for validation, improvement and facilitation of the data related process to insure that data meets the business needs.

5 Quality Control “Quality control means you can have what you need, how you need it, when you need it.” E. Demming

6 Why Practice QC? It Saves Time It Saves Money It Makes Money Ignorance is not Bliss

7 How Data Goes Bad “Bad Genes”.. Poor design and collection “Adoption” … Someone Else’s Design “Child Abuse”... Poorly Nurtured “Terrible Teens”... Growing Pains

8 The QC Process 1. Define Requirements 2. Identify Data Issues 3. Analyze Options 4. Improve Data Quality Document every step and repeat

9 Define Requirements What do you need? Requires an understanding of the business process, the data, the operating system and the users. Documentation, business specs and “experts”.

10 Devil’s Advocate What is correct for one task / group may be incorrect for another. What is correct now may be incorrect later. What is correct now... may not be able to be repeated.

11 Identify Data Issues Accuracy Completeness Consistency Timeliness Uniqueness Validity

12 G = Good F = Fair B = Bad

13 Analyze Options What do you need? What do you have? What changes need to be made? Will you break anything along the way?

14 Improve Data Quality Selective Processing Clean Existing Values Correcting Existing Values Delete “bad” data Add additional data Document original and new values.

15 Documentation Design Process... business specs “As You Go”... in the code, log, Input and Output files (Freqs & Means) Modifications.... “as per xxx “, Exceptions (Errors and Issues) User’s Manual Elizabeth Axelrod... Big ‘D’ “Just Shoot Them”

16 General Suggestions “Drive Out Fear” Early Intervention Obtain “Buy In” from all parties Keep it “Simple”... use macros Be consistent … use macros Monitor results Document everything, every time

17 II. SAS Macros Macros allow you to use, re-use and share “object-oriented” code. QC is very redundant.... the same or similar process performed on each data set, each variable and each process.

18 Reality People are: Ignorant Forgetful Busy Lazy Don’t Care

19 Why Macros Minimal Effort Parameters Available (FREE)

20 FREQOUT Produces Frequencies for multiple variables % FREQOUT (data= /* input dataset name */, out= freqout /* output data set name, vars= /* list of variables */, by = /* list of by variables */, fmtassign = /* var fmt var fmt */, debugging = NO /* YES or NO */ Author: Ian Whitlock Location: and sconsig.com

21 %EAP_RPT (DSN=, LIBIN=, LIBOUT=, _VARS=, _FMTS=); DSN = Name of input SAS data set LIBIN= SAS library of input data set LIBOUT= SAS library of output data set _VARS= list of character variables to review.. paired with _FMTS _FMTS= list of formats to apply... paired with _VARS Example: %EAP_RPT(_VARS = AGE INCOME EDUCATION, _FMTS = AGE INC EDU, LIBIN = PROJ_IN, LIBOUT = PROJ_OUT, DSN = STUDY_1); EAP_RPT

22 DATA CLEANING TIP00128a - Cleansing Macro, Data Scrubbing routine (see tip for more) %cleanse(schlib=work, schema=, strlen=50, var=, target=target, replace=replace, case=nocase); Author: Charles Patridge Version: 2.1 (sug. by Ian Whitlock) Location:

23 REMOVE OUTLIERS %outlier ( data = _SAS_dataset_name_, out = _SAS_output_dataset_name var = _variable_to_screen pass = _number_of_passes except = _exception_report_data_set_, mult = _multiplier_of_standard_deviations_) The %OUTLIER macro completes outlier screens based on statistical values of a numeric variable in a SAS data set. It is set up to remove any outlier records that are within a given number of Standard Deviations from the mean, and will run that screen a given number of times. For example, a "3-Pass-2" outlier screen will remove any values outside 3 standard deviations from the mean, and will run that outlier screen twice. The given numbers can be any integer. Author: Unknown Location:

24 CONT_COMPARE Compares two data sets, list all variables and reports potential issues: 1)Fields in Both 2)Type 3)Length %cont_compare (dsn1, dsn2)

25 KEEPDBLS: Documents Duplicates TIP KeepDbls %MACRO KeepDbls (SourceDs =_LAST_, TargetDs =, Overwrit =N, IdList =, Where =); Moves duplicate observations to another file. Author: Jim Groeneveld Location:

26 CK_MISSING Evaluates variables in regards to missing and non missing status. Default= _numeric_ missing. _character_ $missing. Parms: DSN = libname and name of data set. Default is the last read/created. PATH= path to directory where QC info is stored. VAR = list of variables to b evaluated. FMT = format statment. %ck_missing( dsn=mylib.recentfile, var=UPB FICO1 FICO2 FICO3 CHANNEL, fmt=UPB upb. FICO1 FICO2 FICO3 fico. CHANNEL $chnl. );

27 LOG FILTER: Examines and Reports on SAS Log Log Filter checks your log for errors, warnings, and other "interesting" messages. It then displays what it finds in its summary window. Double-click on a row and it'll reposition the log window to display the message in context (if it's an external log file, it'll open it in a viewer window and position it for you). Author: Ratcliffe Location:

28 MK_FORMATS Create a format from a SAS data set. Parms: DSN = SAS data set START =Unique key value ie. SSN LABEL =Value to be associated with start ie. Full Name with SSN FMTNAME =Name of Format (sans ".") TYPE= C or N for Character or Numeric LIBRARY = Libname of Format Library (default =work) OTHER = Value to supply for missing (default =OTHER)

29 III. Sources of SAS Macros and QC Code (examples) (proceeding)

30 More Sources SAS-L Books By Users: Ron Cody’s Data Cleaning Numerous books on Macros.... “By Example”

31 Questions ? Gary McQuown


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