Tidy Tuesday

Jon Harmon

TidyTuesday is a weekly podcast and community activity (https://tidytues.day) brought to you by the Data Science Learning Community (http://dslc.io/). Our goal is to help data-science learners learn in real-world contexts.

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Top 10 Tidy Tuesday Episodes

Goodpods has curated a list of the 10 best Tidy Tuesday episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Tidy Tuesday for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Tidy Tuesday episode by adding your comments to the episode page.

Episode 12: New Zealand Bird of the Year

Tidy Tuesday

11/26/19 • 3 min

Follow the show at @tidypod on Twitter!
For show notes and to subscribe see tidytuesday.com
Join us at r4ds.online @R4DSCommunity
Host: Jon Harmon @jonthegeek jonthegeek.com
Support us at patreon.com/tidytuesday

Last week's data on the New Zealand Bird of the Year: github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-11-19

Julia Watzek's Waffle Chart
Edgar Zamora's Waffle Chart
The waffle package is available on CRAN, but I recommend the dev version.

Torsten Sprenger's bar-chart race
The gganimate package is available on CRAN.

This week's Student Loan data: github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-11-26

Check out the patchwork package!

Episode 16: College Tuition

Tidy Tuesday

03/17/20 • 2 min

Follow the show at @tidypod on Twitter!
For show notes and to subscribe see tidytuesday.com
Join us at r4ds.io @R4DSCommunity
Host: Jon Harmon @jonthegeek jonthegeek.com
Support us at patreon.com/tidytuesday

Last week's data on college tuition, diversity, and pay: github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-03-10

Julia Silge's Screencast
The tidymodels suite of packages is available on CRAN!

Stephanie Spielman's Students

Episode 15: Spotify Songs

Tidy Tuesday

01/27/20 • 5 min

Last week's data on Spotify songs: github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-01-21

Georgios Karamanis's Sound Wave Charts

Cedric Scherer's cowplot poster
The cowplot package is available on CRAN!
So it patchwork!

This week's San Francisco tree data: github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-01-28/
dplyr::filter(qAddress != "302x Octavia St Frontage East")

Find me at [rstudio::conf(2020L)](rstd.io/conf)!
A function to calculate the distance from the rstudioconf hotel to a given point.

Check out RBERT and RBERTviz! And my factory package is on CRAN!

Listen to the #DataFemme podcast!

Episode 14: Passwords

Tidy Tuesday

01/21/20 • 4 min

Last week's data on passwords: github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-01-14

Ian Bell's Bar Chart
The cowplot package is available on CRAN!

Jake Kaupp's donut/dendogram
The ggraph package is available on CRAN!

This week's Spotify song data: github.com/rfordatascience/tidytuesday/tree/master/data/2020/2020-01-21/

Find me at [rstudio::conf(2020L)](rstd.io/conf)!

Episode 13: Christmas Eve 2019

Tidy Tuesday

12/24/19 • 2 min

Last week's data on Adoptable Dogs: github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-12-17

This week's Christmas Music data: github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-12-24

SPOILER WARNING, you may not want to read below before listening to the episode.

Twas the first Tidy Tuesday, in 2018.
And my R data options were getting quite lean.
I'd plotted gas mileage by displacement and cyls,
I'd tired of iris; from diamonds, no thrills.

My columns were features, my rows observations.
My plots weren't quite perfect (they lacked annotations).
But I'd learned all I could from Garrett and Hadley,
What I needed was data, and I needed it badly.

Then R4DS Online Learning Community
Announced a new R practice opportunity!
They'd post a new dataset once every week
And, most importantly, 'twould be unique!

The goal was for learners from novice to whiz
To use that new data for their own dataviz.
And whether those vizes were bars, lines, or maps
We'd share our R code (on github, perhaps).

We'd try out new packages, practice and play,
Then share in a tweet hashtag TidyTuesday.
And the rstats community would add their advice.
With new tips and tricks (don't worry, they're nice)!

So I started to read tweets by @thomas_mock
And I waited each Monday (around 2 o'clock).
Then I'd download the new dataset csv,
And read Thomas's tweet to see what it might be.

"Here's comics! Here's Star Wars! Here's US tuition!"
"Here's how NFL players are paid by position!"
"Here's video games! Here's Roman bloodlines!"
"Here's UFO sightings! And ratings of wine!"

And my plots? They improved! With new themes and palettes.
And Thomas kept posting brand new datasets.
I heard Thomas proclaim as he tweeted his tweet,
"It's Tidy Tuesday, y'all! Now go code something neat!"