From our blog

You can browse our list of posts that describe previous and future presentations.

Telling Meaningful Stories with Data & Connecting Javascript to Shiny

By Alyssa Columbus & Alan Dipert on November 27, 2018

These talks were given on November 27, 2018. Details == Schedule == 6:30 – 7:00: Networking 7:00 – 7:05: Welcome & general announcement 7:05 – 7:35: Telling Meaningful Stories with Data 7:35 – 8:05: Custom htmlwidgets: connecting Javascript to Shiny 8:05 – 8:30: Networking, clean-up == Talk 1 == Telling Meaningful Stories with Data (Alyssa C.) According to Edward Tufte, an excellent data visualization expresses ‚ complex ideas communicated with clarity, precision and efficiency.

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Alteryx & Application of R

By John Peach and Ryan Benz on October 8, 2018

#This meetup will be held on October 30, 2018. === Talk 1 === Alteryx – TBD Title: Using R to build large-scale models with Alteryx Abstract: Alteryx will demonstrate how you can leverage your R skills in Alteryx to build large-scale models and deploy them. Alteryx makes deploying a predictive model easy with the click of a button. Alteryx exposes an R and Python interface via standard REST API requests, instead of recoding the models from within their native languages, integration of your most advanced analytic models into production systems is simple and painless.

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APIs and JSON with R, and Color Palette Similarity Measures

By Ryan Benz on September 25, 2018

These talks were given on September 25, 2018. == Talk 1 == APIs, JSON and R (Peter S.) Access RESTful APIs using httr, jsonlite and tidyverse packages. Focus will be writing R code. == Talk 2 == Similarity measure in the space of color palettes (Emil H.) Related to my project of creating a catalog of all available color palettes in r r-color-palettes and its associated r package https://CRAN.

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Cancer Mutations Scores & RStudio Tune-up

By John Peach on August 28, 2018

These talks were given on August 28, 2018. Talk 1: Classification and Statistical Analysis of Cancer Mutations Scores By: Yemi Odeyemi (Ph.D. Candidate in Data Science at Chapman University) The talk will describe part of Yemi’s doctoral work on building a statistical and predictive model to classify driver-passenger mutations. A Logit model is used with 10-fold cross-validation. The data was preprocessed to impute missing values using the rule-of-thumb approach, removal of redundant features and feature scaling.

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