Utilizing the VNAME SAS function in restructuring data files

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1 AD13 Utilizing the VNAME SAS function in restructuring data files Mirjana Stojanovic, Duke University Medical Center, Durham, NC Donna Niedzwiecki, Duke University Medical Center, Durham, NC ABSTRACT Format conversion of data files is a day to day activity in almost all organizations. Files of different types are received and often variable names are descriptive or even include data values, for example, dosing cycle number or day of treatment. Frequently these files are Excel files. SAS provides XMLMap and SAS code specific to Excel to load the Excel XML directly into SAS tables. After conversion of Excel files to SAS data sets using the %XLXP2SAS macro the VNAME function was applied to extract relevant data from variable names. Final data sets were in a format ready for statistical analysis. This paper will illustrate the combined use of a new feature in SAS v9 (the %XLXP2SAS macro), and an older SAS feature (the VNAME function/call routine) to create the final data sets. For this operation you need to have Microsoft Excel 2002 or INTRODUCTION Data from clinical trials, surveys, research and other activities can be collected in various ways and using different input devices. Data files can be text files, MS Excel spread sheets, MS Access data bases, fixed length text files, text files with comma or tab delimited values, or files with other structures. Analysis variables may sometimes be included in these files as column names. In this paper we describe how data can be extracted from the column names of an Excel file and converted into a SAS data file. The paper is composed of four parts. First, we describe the Excel file and the desired SAS data structure after transformation of the Excel files to SAS data sets. We also explain the advantages of using the %XLXP2SAS macro in the conversion process. Second, we illustrate the benefits of using the VNAME function. Third, we present the restructuring of the data files using the combination of the %XLXP2SAS macro and the VNAME function. An example of the code is given in part four. DATA FILE STRUCTURE BIomarker measurements on patients for several treatment cycles and days within treatment cycles were received in an Excel file. The file contained one row and n columns per patient with the name of the biomarker and the associated cycle and day of the measurement as part of each of n-1 column headings. One column contained a patient ID. For data analysis using PROC MIXED the file needed to be restructured with 4 columns (ID, cycle, day, biomarker) and n-1 rows (observations) per patient, one for each cycle, day pair. Example of input Excel file (all values are hypothetical).

2 Example of output SAS data set after restructuring and resorting. In this example, three SAS data variables were defined from each Excel file column name, cycle, day and biomarker. However, there is no limitation to using this approach in converting larger numbers of SAS variables, for example, adding, data collection site, treatment regimen, etc). SAS V9 MACRO %XLXP2SAS %XLXP2SAS is a SAS V9 macro designed, together with SAS XMLMap, specifically for Excel XML data by SAS Institute. XML is an acronym for Extensible Markup Language, and represents a way to define and format data for easy exchange. An XML file is similar to an HTML file. It provides an automatic way to import multiple Excel worksheets with SAS supported by the SAS XML LIBNAME Engine (SXLE) and the SAS XMLMap. This operation is available for Microsoft Excel 2002 or later versions since the Excel file must be saved as an XML file before conversion into a SAS data set with the macro, and only Excel 2002 and later versions support saving Excel spreadsheets in XML format. In order to use this macro for Excel file conversion to a SAS data set you must save the SAS XMLMap in a file named ExcelXP.map and the SAS macro named XLXP2SAS for loading the XML data in a file named LoadXL.sas. Both files should be in the same folder. A detailed explanation is given in (1. To download the SAS macro and SAS XMLMap, go to the SAS Presents Web Site at support.sas.com/saspresents and find the entry Creating AND Importing Multi-Sheet Excel Workbooks the Easy Way with SAS. The entire process is very simple. You only need to define two separate locations for program/macro and data. The next step is to convert the Excel file to the XML version. The code described below requires an XML file as input. To import the workbook into SAS, we submitted the following code with the appropriate directory paths: libname mylib "C:\StudyXXX\sasprogs\";

3 libname db " C:\StudyXXX \sasfiles\"; %include " C:\StudyXXX \LoadXL.sas"; %xlxp2sas(excelfile =PHARMA Raw data-biom1.xml, mapfile = ExcelXP.map, library=mylib); The %include statement makes the XLXP2SAS macro available to SAS. The %XLXP2SAS statement imports the data from all the worksheets into separate SAS tables. By default, the SAS tables are created in the WORK library. You can control which library is used to store the SAS tables by specifying the LIBRARY argument in the XLXP2SAS macro. For example, to store the tables in the SASUSER library, submit this code: %xlxp2sas(excelfile=mydata.xml, mapfile=excelxp.map, library=sasuser); In the conversion of Excel Column Labels to SAS Column Names, the name of the worksheet (for example, "PHARMA_BIOM1_Flow_Data") is used as the SAS table name. The SAS XML Libname Engine, SAS XMLMaps, and the XLXP2SAS macro greatly simplify the task of moving Excel XML-formatted data into SAS. VNAME FUNCTION The VNAME function (2,3,4,5) returns the name attribute of a variable. This function belongs to group of variable information functions, together with VTYPE, VLABEL, and VLENGTH etc. Also, the VNAME function returns the name of a SAS variable when a specific array element is referenced. Syntax: NewVar = VNAME (OldVar); NewVar variable is a string variable and its content is OldVar. By using a LENGTH statement we can allocate length to the new variable. The length of the variable should be equal to the longest variable name or to 32 characters. Otherwise SAS will allocate a length of 200 characters which is unnecessary because the longest variable name allowable in SAS V8 and above is 32 characters. By utilizing the VNAME function we achieved a simple, reliable and straightforward solution. Simple few coding statements. Reliable we get exactly what we expect to get. Straightforward -- the program is easy to understand and maintain. PROGRAM DESIGN Requirements: Content of the final SAS datafile should be PATID, CYCLE, DAY, and Biomarker (BIOM1). Composition of variable names in the Excel file was CaDb_BIOM1 where value of a denotes CYCLE number and value of b denotes DAY number. Steps in program design. Keep necessary variables only. Convert variable names to variable values (separate set of variables) by using the VNAME function. Determine the value of the variable, CYCLE. Determine the value of the variable, DAY. Assign values for the biomarker variable (eg., BIOM1) from the corresponding Excel variable (converted to a SAS variable). Output the observation to newly-created SAS data set.

4 EXAMPLE The following code is an example of a complete program that performs the tasks outlined above. *Converting Excel file to SAS data set by using %XLXP2SAS sas macro; libname mylib "C:\StudyXXX\PHARMA_BIOM1\"; %include ' C:\StudyXXX\LoadXL.sas'; %xlxp2sas(excelfile =PHARMA Raw data-biom1.xml, mapfile = ExcelXP.map, library=mylib); * At this point SAS image of input Excel file was generated ; * For QC purpose only ; proc print data=mylib.pharma_biom1_flow_data ; title "Print mylib.pharma_biom1_flow_data" ; run; * First data step in the program removes complete blank rows from data set and converts variable names ; * to variable values (into separate variables newvar1 to newvar23) by using VNAME Function ; data PHARMA_BIOM1 ; set mylib.pharma_biom1_flow_data(keep=patient C1D1_BIOM1 C1D2_BIOM1_ C1D3_BIOM1 C1D4_BIOM1 C3D1_BIOM1 C3D2_BIOM1 C3D3_BIOM1 C3D4_BIOM1 C6D1_BIOM1 C6D2_BIOM1 C6D3_BIOM1 C6D4_BIOM1 C12D1_BIOM1 C12D2_BIOM1 C12D3_BIOM1 C12D4_BIOM1 C60D1_BIOM1 C60D2_BIOM1 C64D1_BIOM1 C64D2_BIOM1 C69D1_BIOM1 C70D1_BIOM1 C70D2_BIOM1 rename =(patient=patid)) ; array newv(23) $25 newvar1 - newvar23 ; retain newvar1 - newvar23 ; array origv(*) C1D1_BIOM1 C1D2_BIOM1_ C1D3_BIOM1 C1D4_BIOM1 C3D1_BIOM1 C3D2_BIOM1 C3D3_BIOM1 C3D4_BIOM1 C6D1_BIOM1 C6D2_BIOM1 C6D3_BIOM1 C6D4_BIOM1 C12D1_BIOM1 C12D2_BIOM1 C12D3_BIOM1 C12D4_BIOM1 C60D1_BIOM1 C60D2_BIOM1 C64D1_BIOM1 C64D2_BIOM1 C69D1_BIOM1 C70D1_BIOM1 C70D2_BIOM1 ; * By using the RETAIN statement we apply VNAME function once per each variable only ; if _N_ = 1 then do ; do i = 1 to dim(origv) ; newv(i) = vname(origv(i)) ; end ; end ; if patid = ' ' then delete ; * Removing blank lines from conversion ; drop i ; * Second data step in the program determines values for CYCLE, DAY, and BIOM1 from newvar1 to newvar23; data mylib.pharma_biom1(keep=patid CYCLE DAY BIOM1) ; set PHARMA_BIOM1 ; array newvar(23) $25 newvar1 - newvar23 ; array origv(*) C1D1_BIOM1 C1D2_BIOM1_ C1D3_BIOM1 C1D4_BIOM1 C3D1_BIOM1 C3D2_BIOM1 C3D3_BIOM1 C3D4_BIOM1 C6D1_BIOM1 C6D2_BIOM1 C6D3_BIOM1 C6D4_BIOM1 C12D1_BIOM1 C12D2_BIOM1 C12D3_BIOM1 C12D4_BIOM1 C60D1_BIOM1 C60D2_BIOM1 C64D1_BIOM1 C64D2_BIOM1 C69D1_BIOM1 C70D1_BIOM1 C70D2_BIOM1 ; do i = 1 to dim(origv) ; Determining value for CYCLE. ;

5 positiond = index(newvar(i),'d') ; * Determining position of letter 'D' in variable name ; length CycleCHAR $ 2 ; *Auxilliary variable for cycle ; if positiond = 3 then CycleCHAR = substr(newvar(i), positiond-1, positiond-2); else if positiond = 4 then CycleCHAR = substr(newvar(i), positiond-2, positiond-2); CYCLE = input(cyclechar, 2.) ; * Determining value for variable DAY ; DAY = input(substr(newvar(i), positiond+1, 1), 1.) ; * Assigning value for BIOM1 from corresponding member in array ; BIOM1 = origv(i) ; output ; end ; proc contents data=mylib.pharma_biom1 ; title "Final file" ; proc freq data=mylib.pharma_biom1 ; tables CYCLE DAY BIOM1 / missing list ; proc print data=mylib.pharma_biom1 (obs=92) ; title "Print of Final file" ; Program flow diagram We used complementsoft ASAP software (6) to visualize SAS code presented in this paper for better understanding. The ASAP software parses the submitted SAS source code. It also resolves each procedure step and data step and generates graphical view of SAS code. Checking of each procedure is simple by clicking on the corresponding icon. A Data flow diagram (not presented here) provides an exact graphical presentation of the results of each Proc step. Using this tool the user can instantly see all inputs and outputs from each procedure as well as from data steps.

6 CONCLUSION Excel files can be easily converted to SAS data files using the %XLXP2SAS macro in SAS V9. The VNAME function enables the conversion of Excel column names into SAS data. REFERENCES 1. Delgobbo Vincent, Creating AND Importing Multi-Sheet Excel Workbooks the Easy Way with SAS, PharmaSUG 2006, HW08, Bonita Springs, FL 2. Cody Ronn, SAS Functions by Example, SAS Institute, 2006, Cary, NC 3. DiIorio Frank C, Understanding and Using Functions, 4. Carpenter Art, Carpenter s Guide to SAS Macro Language, Second edition, SAS Institute, 2004, Cary, NC 5.First Steven, Hidden Gems in the SAS System, 6. ComplementSoft ASAP, CONTACT INFORMATION Your comments and questions are valued and encouraged. Contact the authors at: Mirjana Stojanovic Donna Niedzwiecki Duke University Medical Center Duke University Medical Center Durham, NC, Durham, NC, mirjana.stojanovic@duke.edu donna.niedzwiecki@duke.edu TRADEMARK INFORMATION 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.