I'm currently reading "History of Information Graphics" by Sandra Rendgen to learn from centuries of visualization masters before creating new approaches for computational biology 🙂‍↕️

Improvable data communication wastes great research, and we can overcome that by standing on giants' shoulders! 📚✨

🎓 My journey started at @upv.es studying biotechnology, then specialized in AI and Data Analysis through a masters' program with financial aid from @valgrai.bsky.social . In fact, last year I was honored to receive their Best Academic Record Award for my Masters'! 🏆

🎨✨ R isn’t just for crunching numbers – you can make art with it too! A colleague sent me this, and I had to try it myself 🎆 Fireworks with ggplot2!

Here’s to a creative and data-driven 2025! 🖼️💻

👉 Code here: pastejustit.com/to2jqgsjvd

#RStats #DataArt #HappyNewYear2025

And a Bonus Insight! Artist Diversity 🎼
Bubble chart showing top artists by total listening time AND number of unique tracks. Some artists I go deep with, others I sample widely. The data reveals my musical exploration! 🌄

The Ultimate Listening Heatmap 🔥
Combined days and hours to see EXACTLY when I listen most. White to red gradient shows intensity. It's like a musical heartbeat of my year! Don't ask me why, but on Thursday lunchtimes I always seem to be listening to songs! 🍽️🎶

Monthly Listening Trends 📈
Conspiracies aside, I created a visualization showing how my music consumption changes month to month, and added a smooth trend line to see the overall trajectory! Seeing this I realised that I listen to less music now! I'll have to compensate the rest of the year 🫡🎧

Track Deep Dive 🎧
Obviously, I've also looked at my top 10 most-played songs! And, again, there are disparities with Spotify Wrapped - what are they doing behind the scenes, so that the results don't match? 🤔

Top Artists Revealed! 🎤
Not just who I listened to most, but how MUCH I listened. Used a horizontal bar chart to showcase my musical champions. Some surprise entries, some consistent favorites (I 🧡 Oques Grasses). Surprisingly, there's a difference from my wrapped. Is Spotify hiding something? 🧐

Days of the week tell a story too! 📅
Analyzed which days I stream the most music. Some days are clearly more musical than others - work days? Weekends? I listen to music when I'm working. The data doesn't lie, take a look at this graph! A simple bar chart reveals all. #ListeningPatterns

Discovering my most active listening hours was eye-opening! 🕰️
I created line graphs showing my listening habits throughout the day, broken down by month. Special mention to the two peaks in September, coinciding with when I was going to and coming back from the office! 🚌🎵

🎵 Ever wondered what your Spotify data REALLY says about your music habits? I downloaded my streaming history and used R to uncover some fascinating insights! Get ready for a data journey through my musical year.

👇The code so you can perform it yourself is at the end of the 🧵!
#SpotifyDataScience

✨We won the European Green Capital 2024 Hackathon in Valencia! 🌿 Our team tackled food waste for supermarkets with NOSTRADAMUS: combining AI & advanced forecasting to predict demand, avoid surplus & reduce waste 🏆 #Hackathon #EuropeanGreenCapitality2024 #ClimathonVLC #Sustainability #AI

2️⃣ Want to add correlations to your scatterplots? Here’s how!

The code: pastejustit.com/wrteuznc4m

The plot 🖼️:

1️⃣ Let's create a group comparison plot with the corresponding t-test!

The code: pastejustit.com/xd4lt8cwfd

The plot 🖼️:

Wish your visualizations could communicate statistical insights more effectively? The 𝙜𝙜𝙨𝙩𝙖𝙩𝙨𝙥𝙡𝙤𝙩 package in R enhances 𝙜𝙜𝙥𝙡𝙤𝙩2 by adding statistical results, effect sizes, and more directly into your plots.

A game-changer for clear, actionable visuals! 🧵
#DataScience #RStats #DataVisualization