We are excited to present two interesting and timely talks: Sandhya Kambhampati's "The Property Tax Divide analysis with R" and Dirk Eddelbuettel on "Extending R" for performance.
Nearly a year after Jak's Tap closed (R.I.P.), we have found a suitable replacement for our next meetup: Jefferson Tap! Pizza, salads, and drinks will be generously sponsored by IBM, who has been supportive of open source and R. Thanks IBM!
Talks will begin at 6, with time to get to know your local R community before and after.
# Tax Property Divide analysis with R
In 2017, ProPublica Illinois and the Chicago Tribune published The Tax Divide, a four-part series examining deep inequities in the Cook County’s property tax assessments system. The system, which touches every family in the county, was known to be highly regressive, but reporters found that the assessor’s office ignored the problem and misled the public. ProPublica Illinois data reporter Sandhya Kambhampati will talk us through the methodology they used to conduct the analysis.
Sandhya Kambhampati is a data reporter at ProPublica Illinois, where she uses statistics and databases to uncover structural issues. Previously, she worked at Correctiv and The Chronicle of Higher Education.
The story has upended the conversation surrounding the Democratic primary for Cook County assessor, which will occur on March 20th.
# Extending R
From the beginning, the R and S languages were designed to provide an environment for data analysis with quick and simple access to the best
numerical and statistical libraries of the time. Today, that concept has
blossomed and R has continued to evolve its methods for extending the
language. Dirk is going to dive into these concepts with us and show us how we all can extend R.
Dirk is an active contributor to the global R community. He is an R Foundation board member and an Associate Editor for the Journal of Statistical Software. For over 20 years, he has contributed to open source software and worked in Quantitative Finance. Dirk is one of the authors of the Rcpp package, which remains the most downloaded package on the Comprehensive R Archive Network and is used to extend the performance of roughly 10% of R packages published on CRAN.