Generic Automated Data Dictionary for Any SAS DATA Set or Format Library Dale Harrington, Kaiser Permanente, Oakland, CA
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1 Generic Automated Data Dictionary for Any SAS DATA Set or Format Library Dale Harrington, Kaiser Permanente, Oakland, CA ABSTRACT This paper explains how to create an automated data dictionary which will work for any SAS data set or format library. The program produces data field descriptions, notes, data attributes, value descriptions, and value frequencies. Program can automatically produce results for both continuous and discrete data fields without specification by the programmer. The program can handle all types of data input: disk data sets, views, and tape files. Presentation includes how to automatically update the dictionary as data input changes, how to incorporate already existing formats into the dictionary, and how to leverage information from an existing automated data dictionary to produce other data dictionaries. The program requires only simple parameter inputs and will notify the user (via ) when data descriptions are missing. INTRODUCTION: WHY WE BUILT AN AUTOMATED DATA DICTIONARY My department maintains over 30 different analytical SAS data libraries. Each of these libraries has on-line documentation to which our user base (about 2,000 analysts) can refer when working with the data files. The data dictionaries of this documentation have required constant maintenance to keep them current. This maintenance has taken the unit s skilled programming staff away from their primary duty (programming). Despite this maintenance, the data dictionaries have not always been up to date or consistent in style and information across the different data files. The data dictionaries have often been in need of update because the staff has not been aware that some piece of information was no longer complete or accurate. In addition to the data files, the department maintains a series of format libraries which contain approximately 400 formats for use with the data files. The format libraries have been subject to the same documentation problems as the data files. Therefore, our department has needed a method to do the following: avoid tedious changes to the documentation of the data dictionaries, ensure that the data dictionaries stay up to date, ensure that all data fields have descriptions, standardize the look of each data dictionary, improve the quality of the information in the data dictionaries, standardize descriptions of data fields used in multiple dictionaries, take advantage of maintained format libraries to create and standardize data field value descriptions, enhance the data dictionaries by including value frequencies. ISSUES TO SOLVE: To reach the department s objectives, the automated dictionary program needed to address a whole series of requirements as follows: make the dictionaries automatically update as the data files change overtime, create a single data dictionary program that would apply to all 30 different data files, link the data dictionaries to documentation on the department s web site without having to do a major rewrite of the documentation, keep the data dictionary code simple to allow for development and maintenance by more junior staff, produce frequencies for both discrete and continuous data fields without needing to specify if a data field is discrete or continuous, handle different types of data input: data sets, data views, tape files without specification by the programmer, produce dictionaries for data libraries with multiple members, restrict which library members should have dictionaries, produce dictionaries in a reasonable amount of production time even with large files of 3 million or more records, avoid having to spend significant staff resources to convert data dictionaries to automated form, warn the programmer when the dictionary is missing information, 1
2 the dictionary program must work with either character, numeric or date/time fields or even character data fields which look like numeric data fields without the programmer specifying which is which, provide a method to sort character values which are digits into numeric order, not require any modification to the program for use with a given data file, allow the programmer to customize a given data dictionary without having to change the program code. GENERAL FEATURES OF AUTOMATED DICTIONARY PROGRAM The dictionary program is split into two linked programs: a) first program contains parameters specific to a given data file b) second program is standard and applies same business rules and produces same standard type of data dictionary regardless of data file used. This division allows each user to customize the program for their data file without impacting the core program features. Dictionary program will work for any SAS Data File regardless of whether the data file is a view, a data disk file, or a tape file. Input can be adapted to handle either one or multiple data files. With manipulation of the input file by the user, one could convert a non-sas data file to produce a data dictionary. Program will produce data dictionaries for multiple members of a data library without rerunning the program. The user can exclude certain members from having data dictionaries from being produced. The program can be run simultaneously by multiple programmers without contention. Dictionary program lists the last update date/time for the data file. STEPS TO CREATING THE GENERIC AUTOMATED DATA DICTIONARY: 1. USE PROC CONTENTS The key tool in creating the data dictionary program is PROC CONTENTS. PROC CONTENTS is an extremely useful procedure for creating a data dictionary because the CONTENTS procedure lists the contents of a SAS data set and information on the directory of the SAS data library. This information can be output to a data file which the programmer can use to direct the development of a data dictionary. A key feature of the SAS data file is that there is a description of the file which is stored within the data file. The programmer can take advantage of this feature (via CONTENTS) to find out many features of the data file without having to read the entire data file. This is a major advantage to deal with large data files which take a long time for the computer to process. One aspect of PROC CONTENTS is that it produces an abbreviated data dictionary because it lists all the data fields in the data file as well various attributes of each data field. This automated data dictionary program leverages off the abbreviated PROC CONTENTS dictionary by creating links to use with other sources of information as follows: a. Produce a basic data dictionary by running PROC CONTENTS on the data file and output the results as a data file. A similar process was followed for the format dictionaries but instead of PROC CONTENTS, the key tool was PROC CATALOG to build the basic format dictionary. b. Using the data fields of the PROC CONTENTS data file, the program creates MACRO variables to determine the number of data library members, determine the datatypes of the library members (disk files, tapes, views), derive the name of the data file member derive the last date the data file member was modified find the number of observations in the data member, determine the number of data fields in the data member, create a list of the data fields in the data member, determine the data type (numeric, character, date) of each data field, determine the length of each data field, determine if a format is associated with the data field. 2
3 Here is a sample of the code for this step: DATA MEM; SET MEM; *GET NAMES OF MEMBER FILES; CALL SYMPUT('MEMB' LEFT(_N_),MEMNAME); *GET NUMBER OF MEMBER FILES; CALL SYMPUT('MNUM',_N_); RUN; Note that the code is generic, i.e., the program code requires no unique information about a particular data file. 2. USE PROC FREQ To get the value frequency percents of the data fields in the data file, I used PROC FREQ in conjunction with the information gleaned from the PROC CONTENTS step above. If so desired, user determined parameters can direct the program to select the following: a) the maximum number of discrete values to display, b) the maximum length for a discrete value, c) automatically set a data field as continuous. However, even without user specifications, the program will automatically determine if a data field has too many discrete values to list in the data dictionary. As a default, the program will produce frequencies of missing/nonmissing for data fields defined by the parameters and business rules as a continuous data field. The program will also determine if the number of unique values is too large to use PROC FREQ and will instead use an alternate method which does not require holding the data vector in memory. Here is a sample of the code for this step: *CALCULATE FREQUENCIES FOR EACH DISCRETE DATA FIELD; PROC FREQ DATA= &DFILE ORDER=FREQ; TABLES &&DVAR&I / MISSING NOPRINT OUT=DICT1; RUN; You may wonder why I did not use PROC SQL for this step. Most other papers related to this topic use PROC SQL to produce frequencies. However, my testing found that PROC FREQ is a more efficient choice for producing frequencies. Although PROC SQL used less CPU than PROC FREQ for the same observations (about 25% faster than PROC FREQ in my test), PROC FREQ is overall much faster due to much less time to read thru the data (about 800% faster than PROC SQL in my test). For large data files this difference can be critical when run time is important. 3. LEVERAGING FORMAT LIBRARIES To get text descriptions of each value for a given data field, I linked the data values to the department s standard format libraries. A separate file was created which linked the name of each data field to the appropriate format. The entire format file was then brought into the data dictionary program via a %INCLUDE statement. This step is not necessary to produce the data dictionary but definitely adds to the information provided to the users. Our format libraries are regularly updated and validated. By using the format libraries, the department standardized the values descriptions in all 30 dictionaries and significantly reduced the development and maintenance issues of the automated dictionaries. Further, many of the data fields in our data libraries are identical in meaning. This feature of our data files highlights another feature of the dictionary program design. The design allows programmers to leverage off the work of other programmers where the same data field appears in multiple data libraries or the same format can apply to different data fields. Since the format file is not built directly into the dictionary program, as each automated dictionary was developed, the data fields with linked formats increased in the format file so the burden of format development for the next data file s automated dictionary was lighter. 3
4 Here is a sample of the code for this step: IF NAME EQ 'ACTVTY_CD' THEN CODE=PUT( VALUE, $ACTVTYCD. ); IF NAME EQ 'ADT_FAC_ID' THEN CODE=PUT( VALUE, $FACNAME. ); IF NAME EQ 'ADJMT_RSN_CD' THEN CODE=PUT(VALUE,$ADJMRSN.); Note that VALUE in this sample code is the frequency value of the data field output by PROC FREQ in the previous step. The program also has a diagnostic check for missing value descriptions. If a missing value description is detected, the program sends a staff person specified by a program parameter a warning about the issue. 4. BRING IN TEXT DESCRIPTIONS FOR DATA FIELDS To get the text description for each data field, I created a definition file where the name of each data field was associated with a definition. If the user already has another source for text descriptions such as a meta data library or data field LABELS then you may want to incorporate that information into this step. The program could easily be modified to use these alternate sources. Here is a sample of the text description file for this step: ACCT_MRN_PFX!ACCOUNT MRN PREFIX ACCTROLE_DESC!ACCOUNT ROLE DESCRIPTION ACOUNT!COUNT OF APPROVED TRIPS ACPERID!ACCESSION PERSON ID ACQCOST!ACQUISITION COST Note that the descriptions do not have a fixed width and are delimited from the data field name with an exclamation point. The description file was merged to dictionary data file via the name. I also developed two diagnostic checks for the definition file to check for missing text descriptions and duplicate text descriptions. If either issue is detected, the program sends a staff person specified by a program parameter a warning about the issue. Therefore, if in the future, a new data field or data field value should unexpectedly appear, the programmer will be notified and the needed modification can be done. 5. ADD CUSTOMIZED NOTES I also provided the programmer an option to add other information on the data field to the dictionary. I created a note file where the name of each data field was associated with a supplementary note on the data field. In addition, the user has a secondary option which allows for customized notes that only apply to a single data file. Here is a sample of the text description file for this step:!actual_visit_type!same AS THE APPT_TYPE IN EPIC, SEE APPENDIX!ACTVTY_CD!FOR PRINTING, USE THE FORMAT $ACTVTYCD IN HPS.MIS.FORMATS HOV!ACTVTY_CD!FOR PRINTING, USE THE FORMAT $ACTVTY IN HPS.MIS.FORMATS!ADM_REAS!FORMAT $ADMRSN IN HPS.MIS.FORMATS In the sample code, you will notice that one row is listed twice but starts with HOV. The HOV indicates that this note only applies to the HOV data file while the default version of the note applies to all other data files. Like the text description file, the note file was merged to the dictionary file and delimits the data field name from the text with an exclamation point. 6. KEEP DATA DICTIONARY CURRENT BY ADDING TO PROGRAM SCHEDULER My department schedules our production data libraries to be updated automatically by a job scheduling program. The department also uses program chaining techniques where the completion of one program initiates the next program in a production sequence. The auto dictionary program was added 4
5 to the end of the program production process chain of each data library. Since the auto dictionary will be run after the data files are updated, the dictionary will have current information on data fields in the data library including frequencies. 7. MAKE DICTIONARY DOCUMENT WEB FRIENDLY Since the department keeps its data documentation on a document server while the data dictionary program runs on the MVS mainframe, I used the SAS ODS utility to convert the mainframe dictionary file to a Microsoft Word document. The resulting document was FTPed to the server platform. 8. LINK TO DEPARTMENT WEB SITE CONCLUSION The auto dictionaries were attached to the department s documentation web site via a hyperlink between the web site and document server. Therefore, every time the dictionary is updated the web documentation also updates to the current dictionary. The automatic data dictionary program has been implemented within my department this year. The program was written on a MVS TSO machine because the department s data is stored on MVS due to the size of data files (multi-million records). I used the following SAS features to create the dictionary: PROC PRINT, PROC FREQ, PROC CONTENTS, PROC CATALOGUE, MACROS, ODS, DATA STEP functions, SAS function, AND PROC TEMPLATE. Programming principles that I followed in developing this program were: leverage what you already have in place (formats, web sites, schedulers, program chaining, documentation, etc.), keep your code as simple as possible, use procedures and techniques which are well understood by most SAS programmers, modularize the coding steps, don t require significant changes by programmers to use the new program, design the program so a basic dictionary can be easily enhanced and customized at the user s discretion to produce a very complex data dictionary. have the program identify potential problems with the data and notify the programmer, do not expect the programmer to check for problems ACKNOWLEDGMENTS I would like to acknowledge Ian Harper and Khoa Nguyen for their assistance and thoughtful comments in the development of this application. CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the author at: Dale Harrington Kaiser Permanente 1950 Franklin Oakland, CA Work Phone: (510) dale.harrington@kp.org SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. 5
6 SAMPLE OF WEB PAGE OF DICTIONARY Following is an example page from the web documentation of the link to one of the data file dictionary. 1 Data Dictionary Data Dictionary is automated and current. Depending on your configuration, CLICK or CNTL-CLICK on the following links to see the individual data dictionary files. SUBSET Variables Each record in these views represents one hospital outpatient visit. ICD9DIAG Variables Each row represents one ICD9 code for a patient encounter. ICD9PROC Variables Each row represents one ICD9 procedure code for a patient encounter. CPT4 Variables Records in this file represent the service code line items associated with a case. There may be more than one line item per case. MRNOVLP Variables Records in this file represent the HOVs by patients who have bedded hospital utilization (in the ADT Subset) at the same time as their HOV event. These records have been identified by MRN and time overlap. This table was created because there are a small number of HOVs which occur during bedded hospital utilization but have been given a completely different Hospital accounts (HSP_ACCOUNT_ID) and encounter numbers (PAT_ENC_CSN_ID). HOVs with ADT_FLAG = N will be of most concern as these cases would otherwise be regarded as stand alone event 6
7 SAMPLES OF DATA DICTIONARIES Following are example pages from a data file dictionary and a data format dictionary. I have left out the frequency percents due to confidentiality. AUTOMATED DATA DICTIONARY FOR HOV SUBSET DICTIONARY AS OF 22JUL08:09:08 VALUES AND PERCENTS BASED ON MOST CURRENT TIME PERIOD DATA FILE IF FORMAT LISTED THEN VALUE DESCRIPTION WILL DISPLAY WHEN PRINTED DATA FIELD NAME DATA TYPE RECORD LENGTH FORMAT DEFINITION ACCT_MRN CHAR 10 Subscriber's Medical Record Number ADM_SRCE CHAR 1 THE SOURCE OF THE ADMISSION WHERE PATIENT CAME FROM ADM_TYPE CHAR 1 THE TYPE OF ADMISSION ADT_FLAG CHAR 1 INDICATOR OF TYPE OF HOV ENCOUNTER VALUE NA NA A VALUE DESCRIPTION NOT MISSING MISSING MISSING A: FROM HOME E E: OUTPATIENT CLINIC X C F G H E H N P X: OTHER MISSING C: ELECTIVE F: HOSP AMBULATOR Y G: OUTPAT SURGICNTR H: HOSP OUPT SERVICES EMBEDDED HOV ONLY HOSPITAL ACCOUNT ID IN ADT STAND ALONE HOV PATIENT ENCOUNTER ID IN ADT PERCENT FREQUENCY OF TOTAL NOTE ABOUT FIELD FOR PRINTING, USE THE FORMAT $ADMSRCE IN HPS.MIS.FORMATS FOR PRINTING, USE THE FORMAT $ADMTYPE IN HPS.MIS.FORMATS 7
8 AUTOMATED DATA DICTIONARY FOR ZRAM.S25.LIBRARY DICTIONARY AS OF 23JUN08:10:17 FORMATS ARE FOR ALL OF CALIFORNIA UNLESS OTHERWISE NOTED FORMAT NAME FORMAT TYPE DEFINITION CNTYNAME NUM ASSIGN A NAME TO A COUNTY CODE CSANAME NUM ASSIGN A NAME TO A SERVICE AREA CODE FACNMAJ NUM ASSIGN A MEDICAL CENTER CODE TO A FACILITY AREA CODE FACNMAJI NUM CONVERTS NUMERIC FACILITY CODE TO CHARACTER MAJOR CODE FMNEMONC NUM ASSIGNS A 3 CHAR FAC CODE TO A ZRAM FACILITY AREA CODE NFACNAME NUM ASSIGN A NAME TO A FACILITY AREA CODE NMAJCSA NUM ASSIGN A SERVICE AREA CODE TO A MEDICAL CENTER CODE NMAJNAME NUM ASSIGN A NAME TO A MEDICAL CENTER CODE SUBNAME NUM ASSIGN A NAME TO A SUBMINOR AREA CODE SUBNFAC NUM ASSIGN A FACILITY AREA CODE TO A SUBMINOR AREA CODE SUBNMAJ NUM ASSIGN A MEDICAL CENTER CODE TO A SUBMINOR AREA CODE ZIPCNTY NUM ASSIGN A COUNTY CODE TO A ZIP CODE ZIPNAMES NUM ASSIGNED BY POST OFFICE ZIPSUB NUM ASSIGN A SUBMINOR AREA CODE TO A ZIP CODE NOTE ABOUT FIELD 8
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