Discover and read the best of Twitter Threads about #gganimate
Most recents (7)
(1/n) I love telling stories with data and I have recently discovered #gganimate as a way to take my #dataviz to the next level.
gganimate.com
gganimate.com
Here is some sample code to generate a random walk, create a line plot, and layer in the animation. I love how it's simple to layer within the #ggplot2 framework. 
(3/n) Here is the resulting plot! using the `transition_reveal()` it keeps the previous points on the graph, but it is highly configurable. For example, I could use `transition_time()` or `transition_state()` to only show the point being added.
Last night I was playing a little with Openpose data in #RStats. I realized it's not too hard to wrangle the Openpose output and plot signing directly using #ggplot2 and #gganimate, like so:
But I decided to make some tweaks so you can change the color of the signer+clothes, which makes seeing the hands a bit easier (contrast!)...
But also, why not give your signer a pretty turtleneck to wear?
My first steps into #generativeart with #Python and #rstats.
Here are some #Mondrian style paintings made with #ggplot and #gganimate!
Here are some #Mondrian style paintings made with #ggplot and #gganimate!
I took my three point "windup" charts and animated them.
Note Steph Curry's quick release. The animation starts at the beginning of each player's shooting motion. The ball is out of Steph's hands much sooner than the rest.
Derived from 2013-2016 tracking data.
1/
Note Steph Curry's quick release. The animation starts at the beginning of each player's shooting motion. The ball is out of Steph's hands much sooner than the rest.
Derived from 2013-2016 tracking data.
1/
Here is the overhead view (the path the ball traces if viewed from directly above the player).
While the profile view all looks like a variation of an S-curve, there's a lot more variety here from player to player.
Very little wasted movement in Steph's shot.
2/
While the profile view all looks like a variation of an S-curve, there's a lot more variety here from player to player.
Very little wasted movement in Steph's shot.
2/
Lebron's chart has a "perfect loop" quality about it.
So, it's like *very* easy to process and reconstruct actual images with only a few lines of code. As in plotting software redrawing the image, pixel by pixel.
Here's is a gif of me made with #ggplot2 and #gganimate. Sunday = fun day!
Here's is a gif of me made with #ggplot2 and #gganimate. Sunday = fun day!
Like this is also #ggplot2 omg it's amazing!
And look! With some clever coding you can pixelate your gifs!
1) Hasta las 18:30 hrs. de ayer, 03/03, se habían confirmado 101,800 casos de #COVID19; un incremento diario de 3.5% y el mayor desde el 14/02.
🧵con actualización del análisis para el #BIADN40.
🧵con actualización del análisis para el #BIADN40.
2) De los 3,867 nuevos casos reportados ayer, 65% se registraron en tres países:🇮🇷(31.9%, 1,234 casos), 🇮🇹 (20.1%, 778) y 🇰🇷 (13.1%, 505).
Solamente el 3.9% (151) en 🇨🇳.
Solamente el 3.9% (151) en 🇨🇳.
3) Como ilustran la gráfica anterior y la de este tuit, en los últimos días se ha acelerado notablemente el ritmo de contagio en 🇮🇷, 🇮🇹 y 🇰🇷, así como en 🇫🇷 y 🇩🇪.
Del total de nuevos casos registrados ayer, 7.14% y 4.86% ocurrieron en los dos últimos países, respectivamente.
Del total de nuevos casos registrados ayer, 7.14% y 4.86% ocurrieron en los dos últimos países, respectivamente.
Unrealistic assumptions (baseline scenario: random disease distribution, random sampling) overestimate the detection probability (~ confidence in detecting #CWD). Agent-based framework for wildlife disease surveillance Belsare et. al ow.ly/6U7M30mQFTc #bioRxiv #gganimate
The model was simulated for Franklin County Missouri deer population. Alternate scenario simulates clustering of cases and non-random sampling. Very low prevalence thresholds reflect recent CWD introduction. User-friendly-sliders to set age-sex class specific harvest and testing.
Can be used to calculate region-specific sample sizes for efficient and sustainable CWD surveillance. Also facilitates inferring negative surveillance outcomes. #AdaptiveManagementApproach
