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Jim Groeneveld, OCS Consulting, ´s Hertogenbosch, Netherlands. PhUSE 2011

PhUSE 2011. Comparing dataset metadata. Jim Groeneveld, OCS Consulting, ´s Hertogenbosch, Netherlands. PhUSE 2011. Comparing dataset metadata. AGENDA / CONTENTS Comparing dataset data and metadata PROC COMPARE macro %CrossRef Dataset and variable attributes Example results (in dataset)

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Jim Groeneveld, OCS Consulting, ´s Hertogenbosch, Netherlands. PhUSE 2011

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  1. PhUSE 2011 Comparing dataset metadata Jim Groeneveld, OCS Consulting, ´s Hertogenbosch, Netherlands. PhUSE 2011

  2. Comparing dataset metadata • AGENDA / CONTENTS • Comparing dataset data and metadata • PROC COMPARE • macro %CrossRef • Dataset and variable attributes • Example results (in dataset) • Dataset attributes • Variable attributes • Application of macro %CrossRef • Some technical information • Future features

  3. Comparing dataset metadata • A. Comparing dataset data and metadata • PROC COMPARE • data oriented (attributes: NOVALUES option) • only 2 datasets (or variables in one) at a time • cumbersome output (summary: OUT= dataset) • may be tuned as desired, yet limited to pairs • SAS macro %CrossRef • structure oriented: dataset & variable attributes • any number of specified datasets (from 1) • tabular summarisation (in result dataset only) • columns: dataset names; rows: attributes • user specification of desired attributes

  4. Comparing dataset metadata • B. Dataset and variable attributes • Dataset attributes • MemName, MemLabel and LibName • Creation and Modification date and time • Number of variables and physical observations • Variable attributes • Name (common name in first attribute column) • Label: as value in above Name attribute record if no label then text: "-no label-" if no corresponding variable: empty • optional variable’s Type and Length (combined) • optional variable’s Informat and Format

  5. Comparing dataset metadata • C.Example results (in dataset) 1/2 • Dataset attributes • attributedatasetdatasetdataset • column123

  6. Comparing dataset metadata C.Example results (in dataset) 2/2 Variable attributes attributedatasetdatasetdataset column123 6

  7. Comparing dataset metadata D. Application of macro %CrossRef not with entirely different datasetsbut with a (limited) number of rather similar datasets to view differences master datasets and subsets of them different versions of datasets same datasets with different names similar datasets with different data Goal: to see whether more datasets could be combined into one dataset (or ignored if the data are identical) 7

  8. Comparing dataset metadata • E. Some technical information • all fields are type character of length $256, first, attribute field has $36 • internally SAS name literal variable names are applied • OPTIONS VALIDVARNAME=ANY is set, and reset to the original state at the end of the macro • variable names starting with an asterisk (*) or ending with an exclamation mark (!) and one digit. Avoid such names in your datasets and limit your variable name length to maximally 30 • WORK dataset names start with __

  9. Comparing dataset metadata • F. Future features 1/2 • comparing all datasets in one or more libraries using a wildcard (LibName.*) • optional aggregated data for both numerical and character variables • (non-deleted) logical number of observations • number of non-missing values • number of missing values • frequency distribution of a limited number of distinct (formatted) values (categories) • minimum and maximum (formatted) value(first and last non-missing character value)

  10. Comparing dataset metadata F. Future features 2/2 optional aggregated, univariate data for (mainly) numerical variables mean value median value (also approximate middle, non-missing, sorted, character value) (formatted) mode value (also most occurring non-missing character value) standard deviation various percentiles and more, e.g. distribution information and the statistics that PROC COMPARE can generate 10

  11. Comparing dataset metadata • QUESTIONS • & • ANSWERS • SASquestions@ocs-consulting.com • Jim.Groeneveld@OCS-Consulting.com • http://jim.groeneveld.eu.tf

  12. Q&A: Comparing dataset metadata SAS name literal A name expressed as a string within quotes, followed by the letter N. Applicable to variable names, statement labels and imported variable and table names from DBMS tables (e.g. Excel). Advantage: more compatibility. Example: 'This @#$name'n = 'a SAS name literal'; More information in:SAS Language Reference: Concepts. 12

  13. Q&A: Comparing dataset metadata • Straightforward inventory of metadata • save results of PROC CONTENTS (or of the CONTENTS statement of PROC DATASETS for one or more libraries) to datasets, • if desired keep the most important variables LibName, MemName, Name, Label, Type, Length, Format, FormatL, FormatD, Informat, InformL and InformD; • concatenate all metadata datasets (SET); • if desired sort by variable NAME. This generates all dataset and variable information in subsequent records.

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