In early May I attended the New York R Conference. There were 24 speakers, including my coworker at Weill Cornell Medicine, Elizabeth Sweeney! Each person did a 20-minute presentation on some way they use R for their work and/or hobbies. There was a ton of information, and even though not all of it was directly useful for my workflow as a statistical consultant in an academic setting, I really enjoyed being around so many people who love R.
It seems fitting that my first blog post is on a topic that I tried and failed to find via Google search a few years ago.
I’ll back up for a second. A few years ago I was a recent college graduate, and trying hard to “figure out my life.” My major was biochemistry, which is one of those degrees where 99%* of people just keep on going to school.
A Presentation for Weill Cornell Medicine’s Biostatistics Computing Club Image courtesy of Allison Horst’s Twitter: @allison_horst
Introduction Why dplyr? Powerful but efficient
Works well with entire tidyverse suite Efficiency*
Ability to analyze external databases
Works well with other packages in tidyverse suite ggplot2 tidyr stringr forcats purrr *if you start dealing with data sets with > 1 million rows, data.