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