Today is the release of a beta of a package I am writing for the R statistical package to make it easier for researchers to utilise metadata within R and to make it more worthwhile for statisticians to provide metadata.

Most of the methods for R to import data rely solely on the importing of undocumented data, in fact one of the most common ways to import data is through raw CSVs. However, with the release of DSPL.R it is now possible to browse the metadata of a dataset within a statistical package.

For example, the following output is example output from the US Retail Sales dataset provided by Google:

> print (prep.dspl("~/example/census-retail-sales.zip"))
DSPL Dataset - For more info see: [www.kidstrythisathome.com/dspl.r]
------------                  or: [code.google.com/apis/publicdata/]

Name : Retail Sales in the U.S.
Description : Monthly Retail Trade and Food Services report
            for the United States. This dataset was prepared by Google based
            on data downloaded from the U.S. Census Bureau.
Concepts : 3  -  Type of business, Seasonality, Retail Sales Volume
Slices   : 1  -  retail_sales_business
Tables   : 3  -  businesses, seasonalities, retail_sales_business_tbl
Topics   : 3  -  Industry, Business, Gender

As this example shows, a user is able to load in a new dataset, and get an immediate sense for what the dataset contains. By being able to allow a user to be able to understand the meaning behind a dataset, without having to leave the statistical environment, users are able to seamlessly work with their data and metadata within the same interface.

While DSPL is seen as a newcomer to the statistical world, and the R is perceived(albeit wrongly) to be inferior to more established commercial statistical tools, the agility of R and the brevity of the DSPL standard act as a strong indicator of how, given time statistical metadata could become an integral part of the all statistical processes.