Collin Berke
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collinberke.bsky.social
Collin Berke
@collinberke.bsky.social
Media Research Analyst | #rstats | data enthusiast | news, sports, and podcast aficionado

Website: https://www.collinberke.com/
GitHub: https://github.com/collinberke
LinkedIn: https://www.linkedin.com/in/collinberke/
Note to self: It's been awhile since I've needed to do back indexing to return a single row of data while using #RStats.

So, here's a Base R and dplyr refresher 👇

#dataBS
October 1, 2025 at 8:34 PM
I recently enjoyed using the {gganimate} #RStats package for #DataVis animations. So, I explored it further and drafted some notes. What resulted was some example animations using the palmer `penguins` dataset and some B1G QB passing data from {cfbfastR}.

I was pleased with the outcome. Links 👇
August 26, 2025 at 4:04 AM
Here's my #TidyTuesday contribution for 2025-08-05.

📈 Plot: #ggplot2
🔄 Animation: {gganimate}
🔤 Font coloring: {ggtext}
🔗 Code and data links 👇

Thought about exploring imputation, but opted to forward fill values using the #tidyverse instead.

#RStats #DataViz #r4ds

1/3
August 11, 2025 at 7:19 AM
Here's my contribution to this week's #TidyTuesday data:

Competing with sleep: The NETFLIX shows driving reach and engagement

📈Plot: #ggplot2
🏷️Labels: {ggrepel}
⚖️Scale text: {scales}
🔤Fonts: {sysfonts}, {showtext}, and {fontawesome}
🔗Code and data links 👇

#RStats #DataViz #tidyverse #r4ds 1/4
July 31, 2025 at 5:23 PM
Here's my contribution to this week's #TidyTuesday data: MTA Permanent Art Catalog.

New York City: Buildings of steel, subways filled with glass art

⚙️Data transformation using {tidyverse}
📈Plot using D3.js
🔗Code and data links 👇

#RStats #DataViz #tidyverse #r4ds
July 28, 2025 at 4:29 AM
My final session for the Nebraska.Code() 2025 conference:

Root Cause Analysis

Some key points I took away 👇

#nebrcode @nebraskacode.bsky.social
July 25, 2025 at 8:58 PM
Next up for the 2025 Nebraska.Code() conference:

Beware The Horsemen of Software Slowdown: Debt, Decay, and the Good Idea Fairy

#nebrcode @nebraskacode.bsky.social

Takeaways to follow, later 👇
July 25, 2025 at 6:19 PM
Next up for the 2025 Nebraska.Code() conference:

Avoidifying Over-Complexification: Rooting Out Over-Engineering in Your Projects

#nebrcode @nebraskacode.bsky.social

Takeaways to follow 👇
July 25, 2025 at 4:19 PM
First morning session for the 2025 Nebraska.Code() conference now complete:

Roadmapping with Critical Thought

Glad I sat in on this session. Lots of great info. Takeaways to follow 👇

#nebrcode @nebraskacode.bsky.social
July 25, 2025 at 4:07 PM
Wrapped up the morning keynote from 2025 Nebraska.Code() conference

Decoding Amazon's Innovation Engine: A Blueprint for Tech-Driven Success

#nebrcode @nebraskacode.bsky.social

Takeaways, when I get a moment 👇
July 25, 2025 at 3:03 PM
Day 2 Nebraska.Code() 2025 about to kick off. Ready for another day of sessions and learning.

@nebraskacode.bsky.social #nebrcode
July 25, 2025 at 1:45 PM
Rounding out day 1 of the nebraska.code() conference by attending From CI/CD pipelines to Yoga Mats: Parallels in Pursuit of Continuous Improvement by Ed LeGault (www.linkedin.com/in/ed-legaul...)

@nebraskacode.bsky.social #nebrcode

Takeaways 👇

1/4
July 24, 2025 at 10:34 PM
Attended the Functional Programming: More than just a coding style by Matthew Watt(twopoint.dev/about/) during the nebraska.code() conference: @nebraskacode.bsky.social #nebrcode

Being an #rstats user, the key points about functional programing resonated.

Takeaways 👇

1/2
July 24, 2025 at 10:21 PM
Wrapped up the last session from nebraska.code() conference: @nebraskacode.bsky.social.

The Field Guide For the Accidental Manager

Thanks, @danielmallott.bsky.social.

Takeaways 👇
July 24, 2025 at 7:38 PM
Oh, and a great reminder 3/x:
July 24, 2025 at 6:08 PM
Just wrapped up the last session @nebraskacode.bsky.social.

Digging into the Matrix: Practicing code archeology

Thanks, @arthurdoler.com.

Some great takeaways 👇 1/x

#nebrcode
July 24, 2025 at 4:23 PM
First keynote of the day on the books. #celebrateYourExpert by @jayharris.com. Some great takeaways:

"Imposter syndrome is not a measure of your skill, but your ego"

#nebrcode

2/x
July 24, 2025 at 3:03 PM
Day 1 of @nebraskacode.bsky.social.

Excited to sit in on sessions, learn
#nebrcode.

1/x
July 24, 2025 at 2:39 PM
[13/x] Today's D3.js focus was to add axes to my scatterplot example. Began to better understand group elements. I also learned about arrow function expressions in JavaScript: developer.mozilla.org/en-US/docs/W...

👨‍💻 Commit w/ code: github.com/collinberke/...
📈 Progress 👇
July 12, 2025 at 10:15 PM
[12/x] More progress learning D3.js. I got a scatterplot started. Today's focus: scales. I really like @ocks.org 's definition: "Scales are functions that map from an input domain to an output range." (via www.amazon.com/Interactive-...)

👨‍💻 Commit w/ code: github.com/collinberke/...
📈 Progress 👇
July 10, 2025 at 2:58 AM
[10/x] Made some progress today with the #rstats {htmlwidgets} package and D3.js. Today's challenge: transform data from R to JavaScript. {jsonlite} was helpful in creating the array of arrays needed for D3. `console.log()` was indispensable.

Link to commit to follow. Progress 👇.
July 8, 2025 at 11:46 PM
[6/x] Today's D3.js focus: better understand how to work with the coordinate system while using {r2d3} #rstats' package. Mozilla's web docs were useful: developer.mozilla.org/en-US/docs/W.... It helped me solve the positioning of the plot's labels, which was related to bar placement. My progress👇.
July 6, 2025 at 12:58 AM
[5/x] I should have read the docs closer. I'm still wrestling with how to map elements to the SVG's x & y coordinates.

👨‍💻 GitHub Gist link of progress thus far: gist.github.com/collinberke/...
📦 docs for #rstats {r2ds3} regarding 'D3 variables': rstudio.github.io/r2d3/
July 3, 2025 at 6:38 PM
[4/x] Made some progress with D3.js today. It took me a bit to understand how to translate example code when using the #rstats {r2d3} package.

GitHub Gist link to follow. Progress thus far 👇
July 3, 2025 at 6:38 PM
Today I learned about #rstats {dplyr}'s `group_map()` and `group_walk()`. `group_map()` is really useful for iterating a function on groups within a tibble. `group_walk()` is great for splitting and writing a .csv file by group.

Here are a few examples using the palmer penguins data 👇
June 28, 2025 at 11:34 PM