A new building block of our observatories went through code peer review and was released yesterday. The statcodelists R package aim to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user.
Many people ask if we can really add value to free data that can be downloaded from the Internet by anybody. We do not only work with easy-to-download data, but we know that free, public data usually requires a lot of work to become really valuable. To start with, it is not always easy to find.
Public data sources are often plagued with missng values. Naively you may think that you can ignore them, but think twice: in most cases, missing data in a table is not missing information, but rather malformatted information which will destroy your beautiful visualization or stop your application from working. In this example we show how we increase the usable subset of a public dataset by 66.7%, rendering useful what would otherwise have been a deal-breaker in panel regressions or machine learning applications.
Sisyphus was punished by being forced to roll an immense boulder up a hill only for it to roll down every time it neared the top, repeating this action for eternity. When was a file downloaded from the internet? What happened with it sense? Are their updates? Did the bibliographical reference was made for quotations? Missing values imputed? Currency translated? Who knows about it – who created a dataset, who contributed to it? Which is the final, checked, approved by a senior manager?
Uncut diamonds need to be cut, polished, and you have to make sure that they come from a legal source.