adityadahiya, to magick
@adityadahiya@mastodon.social avatar

Rolling Stone Album Rankings. Correlation between
@spotify popularity vs. Weeks spent in by @billboard.
Data by @thepudding and @RollingStone
Code🔗https://tinyurl.com/tidy-albums
Tools , , by @jeroenooms,

juliasilge, to random
@juliasilge@fosstodon.org avatar

I have a new screencast up, showing how to use tidymodels and vetiver with Posit Team! 💥 This video walks through how to approach the ML lifecycle (EDA through model development to model deployment) with a recent dataset on educational attainment in UK towns:

https://youtu.be/FZW_0HB-Eas

nrennie, to random
@nrennie@fosstodon.org avatar

📢New blog post 📢

I built a Shiny app to display my plots, which updates automatically every time there's a new plot!

📦 Data extracted from the plot R scripts
💻 GitHub Actions to update the data
🕸️ Deployed with Shinylive

Read about the process in this blog post: https://nrennie.rbind.io/blog/webr-shiny-tidytuesday/

R4DSCommunity, to datascience
@R4DSCommunity@fosstodon.org avatar

The R4DS Online Learning Community has thousands of members, hundreds of which are active on our Slack every week. You might be wondering: Why not charge those learners? Why is the Community funded through donations?

🧵1/5

bjnnowak, to random
@bjnnowak@fosstodon.org avatar

:blobcatgooglytrash: For , the analysis of items collected by Trash Wheels in Baltimore harbour show that plastic bottles are by far the main trash found in the water. One more reason to replace them with reusable bottles!

🔗 code: https://github.com/BjnNowak/TidyTuesday/blob/main/trash_wheel.R

deepali, to Figma

week 7 - Valentine's day consumer data in the US.

adityadahiya, to random
@adityadahiya@mastodon.social avatar

Used {tidygeocoder} R package to locate new or changed addresses of US Polling Places between 2012 to 2020. Some patterns emerge on Churches vs Schools in different States.
Code🔗https://shorturl.at/jvIZ9
Data: The Center for Public Integrity
Tools: @R4DSCommunity
Credits for {tidygeocoder}: @dhernangomez @dpprdan

hrbrmstr, to random
@hrbrmstr@mastodon.social avatar

Good dataset for https://www.404media.co/email/fc206713-b397-4c0e-a714-b9f40b4c4877/

adityadahiya, to 13thFloor
@adityadahiya@mastodon.social avatar

Rising popularity of group of packages (by @hadleywickham
and others) amongst top R packages used as imports by other R packages.

Code & Analysis🔗: http://tinyurl.com/tidy-r-pkgs
Data Credits: Mark Padgham & @noamross

adityadahiya, to datascience
@adityadahiya@mastodon.social avatar

Week 52. A stream-plot: Different Licenses of packages in last 2 decades - rising popularity of MIT license!
Analysis & Code🔗: http://tinyurl.com/tidy-r-pkgs
Data: Mark Padgham & @noamross

hrbrmstr, to random
@hrbrmstr@mastodon.social avatar

Just cranked out an almost-complete new package — {ustvdb} https://gitlab.com/hrbrmstr/ustvdb — which retrieves data from https://ustvdb.com/ (their data is backed by Nielson).

Just a cpl "endpoints" left to go.

It was born out of me fact-checking some "NewsNation" stats in a blog and podcast today.

Prbly a candidate for shenanigans.

DataAngler, to random
@DataAngler@vis.social avatar

My contribution is a map of life expectancy in South America.

I used the "compact" data file from this United Nations data source (full of lots of stats for the world): https://population.un.org/wpp/Download/Standard/MostUsed/

With this data set, you could take 1950 and 2021 data years and use lag() to compute percent change for all kinds of vital statistics for nations.

Code: https://github.com/bardolater/R_Projects/blob/main/12_SouthAmerica_TidyTuesday.R

juliasilge, to random
@juliasilge@fosstodon.org avatar

This week's is about Doctor Who :tardis: and in this screencast I show how to use empirical Bayes to estimate the rating for different episode writers:

https://youtu.be/OtDpYeiwbj8

R4DSCommunity, to random
@R4DSCommunity@fosstodon.org avatar

On this , we're asking for your help prioritizing our backlog! Visit https://r4ds.io/donate.html to read about our upcoming projects, and vote with your tax-deductible donation!

EvaMaeRey, to random

Who are the CRAN extenders??? Network showing "^gg" CRAN packages w/ ggplot2 depends/imports. ; data via project. Closer look at figure: https://evamaerey.github.io/featurette/2023-11-27-ggedgelist-gg-cran-extenders/ggedgelist-gg-cran-extenders_files/figure-html/feature_auto_12_output-1.png

meghansharris, to random
@meghansharris@fosstodon.org avatar

It's been a WHILE since I've been able to do a in (Or post anything at all! Hi. Yes. I'm alive).

This week's data is R Ladies Chapter Events. Here, we're simply looking at the total number of in-person and online meetups through the years!

💻 Code for this Visual: https://rb.gy/tc7p3h

🌎R Ladies Global Site: https://rladies.org/

fgazzelloni, to datascience

2023 RLadies Chapters

@R4DSCommunity

deepali, to random

week 47 - R Ladies chapters.
Created this interactive viz using D3.js in Observablehq. Each city is represented as a flower with petals equal to the total number of events.
Kudos to all the amazing
Code: https://observablehq.com/d/d30791feffacf739
👏

seav, (edited ) to worldwithoutus
@seav@en.osm.town avatar

🗺️ Day 1️⃣6️⃣: 🌏

A sad chapter in the history of Oceania are the hundreds of nuclear tests that have been conducted by the 🇺🇸, the 🇬🇧, and 🇫🇷. This map shows the locations in 🇦🇺, the 🇲🇭, 🇰🇮, and 🇵🇫 where these three nations have detonated nuclear devices along with estimated yields in kilotons of TNT equivalent. and are also shown for comparison.

1/2

seav,
@seav@en.osm.town avatar

Data sources:
▪︎ Nuclear explosions data from : https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-08-20
▪︎ Base map from ’s 1:50m physical vectors: https://www.naturalearthdata.com/downloads/50m-physical-vectors/

2/2

gavin, to random
@gavin@fosstodon.org avatar

Hi

Does anyone have any demos of how to render 3-D bar plots in the polygons of a map plot?

I feel like I saw a plot like this once in , but it might have been on the other site.

deepsha, to random

Day 10 - North America - I’ve been wanting to learn to make a US chart map with @observablehq Plot and todays combined perfectly with this weeks to make that happen. I couldn’t decide between the two versions though!
Code: https://observablehq.com/d/f970cc6784ed1624

Us map with each state as a tile and house elections results marked in red and blue bars for republic and Democrat between 1976-2022 and height of bar is the vote %

juliasilge, to random
@juliasilge@fosstodon.org avatar

Happy Election Day, everyone! 🇺🇸

This week's is about US House elections, and this screencast shows how to use logistic regression to analyze vote share:
https://youtu.be/C143WxnBLFo

frankhaenel, to TaylorSwift German

Week 42: Exploring Taylor Swift's album vibes! 📊 Check out the danceability, energy, and more in her music with this stylish chart. 🎵
code: https://bit.ly/3S3QEVM

nrennie, to random
@nrennie@fosstodon.org avatar

This weekend I finally got around to deploying a Shiny app wih webR! I've finally put together (almost) all of my plots into one place and it made it easier to browse based on the packages used! 📦

You can view the webR-powered app here: https://nrennie.github.io/tidytuesday-shiny-app/

(may take a while to load - not advised to try on mobile!)

Code on GitHub: https://github.com/nrennie/tidytuesday-shiny-app

Screen recording gif of Shiny app showing user making selections and the plot images updating.

nrennie,
@nrennie@fosstodon.org avatar

This fantastic tutorial from @ramikrispin made it relatively straightforward to get up and running quickly: https://github.com/RamiKrispin/shinylive-r

The app itself is very simple, and works because my GitHub repo uses the same directory structure for each week. This was made easier by using template files, and you can read about that in the blog post I wrote last month: https://nrennie.rbind.io/blog/script-templates-r/

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