Just gave an #rstats brain dump talk of pkgs/functions I've ♥️ over the last couple months. Aimed for the less obvious choices and called them "secret tRacks" after the cassette tape surprises (yes, I'm old) 🧵
`pak` rocks my world as package manger of choice over install.packages/devtools/remotes pak.r-lib.org
How is this hot mess of an Excel spreadsheet different than the last one? `waldo` knows waldo.r-lib.org
Improve the rendering of your graphics by replacing png()/jpeg()/tiff() with agg_png()/agg_jpeg()/agg_tiff() ragg.r-lib.org
Don't let your gif joke get ruined when you land on a Xaringan slide mid-clip!!! xaringanExtra::freezeframe() guarantees play from the beginning and gives manual gif controls if you want them github.com/gadenbuie/xari…
Full talk here tgerke.github.io/secret-tracks/ with all the credit to these geniuses (who you should follow but I didn't want to tag and potentially wreck their mentions 😅)
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After 6 years, I have left the tenure track. I’m beyond excited to begin a new #postac role (Director of Health Informatics) next week.
I do have some parting thoughts about the unique situation data scientists face in academia currently. 1/
There is increasing recognition that everyone *needs* a data scientist, and this is often distinct from a statistician (which everyone also needs!!). Data are messy, projects need reports/dashboards, figures should be elegant, reproducible pipelines/data sharing should happen.
Data scientists treat these problems as top priority. Statisticians need all that infrastructure to *just work* because they need to operate at top-of-license conducting inference.
I took notes in tired/wired format from the *incredible* #rstudioconf workshop "What they forgot to teach you about R" by @JennyBryan and @jimhester_. Here are some highlights, in case useful for others.
Tired: Use absolute paths or 💀 setwd() 💀
Wired: Use here::here()
Tired: Search and browse for an .Rproj to open with Mac Spotlight.
Wired: Prioritize .Rproj files for immediate open/actions with alfredapp.com