Jack Kennedy 🐀 @_jcken@qoto.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.