Jack Kennedy 🐀 @_jcken@social.cleverlibre.org

For this week's #TidyTuesday data about haunted places, I looked at the most haunted city in the USA - Los Angeles!

🗺️ Street map data from OpenStreetMap via {osmdata}

💀 The {creepr} package from @_jcken to make a scary noise when the OSM streetmap data was finished downloading...

👻 Background glow added to points with {ggfx}

Code: https://github.com/nrennie/tidytuesday/tree/main/2023/2023-10-10

#RStats #R4DS #DataViz

Just downloaded #RStats v1.0.0 (2000) and it contains a text file called 'Y2K'. A reminder to us all: please write R programs and manipulate data in ways that are Y2K compliant, thank you. 🙏

Such a lovely week catching up with old friends and making new ones at Royal Statistical Society Conference. A great combination of #statistics, #datascience and #rstats. See you in Brighton? #RSSConf2023 #RSSConf2024

Slides 👇

🗺️ Making Maps! Workshop
https://github.com/jumpingrivers/rss-making-maps

🧹 Taking the stress out of your code mess
https://bit.ly/rss-code-mess

💬Share your data story
https://statsrhian.github.io/talks/2023/2023-09-04-share-your-data-story/slides.html

Issue 2023-W37 of @rweekly is out now! https://rweekly.org/2023-W37.html

This week's highlights:

🕸️ Preloading your R packages in webR in an Express JS API by Colin Fay

🦮 A guide to annotating equations in quarto documents by @lwpembleton

👽 Using Stan to analyse global UFO sighting reports by @_jcken

Thanks to all contributors - keep sending your submissions via RSS feed and/or pull request https://github.com/rweekly/rweekly.org/compare

Here's an example of #ggblend with #ggdist: ggdist can create gradient lineribbons using color ramps, but can have problems when they overlap

ggblend can partition a geometry and blend those partitions: e.g. using "multiply" to blend overlapping lineribbons together

#Day8 of #30DayChartChallenge (distribution/human) : How do 👉you👈 map the distribution of human population? #gischat

🔗to #Rstats code for the 6 maps : https://github.com/BjnNowak/TidyTuesday/blob/main/SC_HumanPop.R

“We trained weasels to count how many customers came into the store” - reasonable; unproblematic; probably a thing you could do.

“We trained weasels to screen resumes of people applying to jobs” - probably a bad idea, especially if the weasels are racist or sexist (the weasels are both racist and sexist).

“We trained weasels to drive a two-ton vehicle on busy city streets” - terrible idea; someone is almost certainly going to die.

My wife says that any time someone proposes doing anything with an ML model, you should replace “AI” in the proposal with “trained weasels” and if it still sounds like a good idea you can go ahead with it.