#chirunconf(Sharla Gelfand): sharla.party/posts/chirunconf/
footrulrpackage repo: github.com/kanishkamisra/footrulr
drakeworkflow (Ben Listyg): github.com/wlandau/drake-examples/tree/master/fcd
drakev7.0.0 package release notes: ropensci.org/technotes/2019/03/18/drake-700/
electricShinepackage repo: github.com/chasemc/electricShine
rlangtippackage repo: github.com/revodavid/rlangtip
broompackage repo: github.com/tidymodels/broom
workflowrpackage repo: github.com/jdblischak/workflowr
brickr3D LEGO models and mosaics from images using R and
Kanishka Misra is a PhD student at Purdue University studying Natural Language Understanding. He enjoys learning new ways of applying his knowledge of natrual language processing and machine learnign to solve social science problems in a data-driven way. At the 2019 Chicago R Unconference, Kanishka created a brand new R package called footrulr which compare sentences using Machine Translation and Text Summarization evaluation metrics.
Benjamin Listyg is a member of the data team at Wyzant, an online educational/tutoring marketplace based in Chicago, Illinois. His current work revolves around combining network analysis and natural language processing. At the 2019 Chicago R Unconference, Benjamin created an excellent example with the
drake R package to simulate a workflow for fixed-choice design in large social networks.
Will Landau is a research scientist in the life sciences industry. Will likes to solve scientific problems from a statistician’s perspective, and likes to build tools for data analysis. He develops and maintains some R packages on CRAN, including drake and downsize. Will is also a minimalist, a sailor (wind-powered, not military), a climber, a CrossFitter, and a martial artist.
Chase Clark is a PhD candidate in Med Chem and Pharmacognosy at the University of Illinois at Chicago. He has been using R for three years and does a lot of work with Shiny. Recently Chase gave a talk at the 2018 R/Pharma conference about the IDBac project, a Shiny application aimed to make finding new medicines from bacteria a little less serendipitous.