Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism

For the many journalists who use data and computation to report news, wrangling is an integral part of their work. Despite abundance literature on in context enterprise analysis, little known about specific operations, processes, pain points encounter while performing this tedious, time-consuming task. To better understand needs user group, we conduct a technical observation study 50 public repositories analysis code authored by 33 professional at 26 news organizations. We develop two detailed cross-cutting taxonomies computational journalism, for actions processes. observe extensive multiple tables, notable gap previous analyses. concise, actionable framework general multi-table that includes operations documented our taxonomy are without clear parallels other This framework, first incorporate tables as first-class objects, will support future interactive tools both journalism general-purpose use. assess generative descriptive power through discussion its relationship set taxonomies.