Talks & Workshops

Recent Posts

Over the past month or so, the r4ds online learning community founded by Jesse Maegan has been developing projects intended to help connect mentors and learners. One of the first projects born out of this collaboration is #TidyTuesday, a weekly social data project focused on using tidyverse packages to clean, wrangle, tidy, and plot a new dataset every Tuesday. If you are interested in joining the r4ds online learning community check out Jesse Maegan’s post here!


When you first started in R you likely were writing simple code to generate one outcome. print("Hello world!") ## [1] "Hello world!" 5 * 6 ## [1] 30 x <- c(1, 2, 3, 4, 5) ## [1] 1 2 3 4 5 This is great, you are learning about strings, math, and vectors in R! Then you get started with some basic analyses. You want to see if you can find the mean of some numbers.


While data analysis in R can seem intimidating, we will explore how to use it effectively and clearly! Introduction After a great discussion started by Jesse Maegan (@kiersi) on Twitter, I decided to post a workthrough of some (fake) experimental treatment data. These data correspond to a new (fake) research drug called AD-x37, a theoretical drug that has been shown to have beneficial outcomes on cognitive decline in mouse models of Alzheimer’s disease.



Customer Churn - EDA

Exploratory data analysis of a customer churn dataset