Same plot, with some important fixes ;) pic.twitter.com/kHgrhdGVZH
— Otho (@othomn) July 16, 2019
I should add hover events to this plot :)
— Otho (@othomn) July 2, 2019
A bit of fun packing circles as in this great blog post by @chisatini https://t.co/xeKlWH5haW
🗞️ https://t.co/HdtNACbEys
👨 💻 https://t.co/8B5M8AUHz1
Happy #TidyTuesday ! 🙌 pic.twitter.com/UDFmcHtNuW
UFO ❤️ NY
— Otho (@othomn) June 25, 2019
Data: https://t.co/KTpDAvmcK9
Code: https://t.co/9fGc0nfUnF
👽Happy #TidyTuesday😁 pic.twitter.com/WKsw4RvN6H
Could not resist using a bird icon for this #TidyTuesday 🕊️🦆😅
— Otho (@othomn) June 18, 2019
Thanks @_sharleen_w for the nice dataset :)
data: https://t.co/xwoNHLBzIc
code: https://t.co/Q4y4agfgnb
⬇️⬇️⬇️The plot is long⬇️⬇️⬇️ 😁 pic.twitter.com/E1pLqoMbgP
New #TidyTuesday! Thanks to @malinfax for this nice dataset on meteorites☄️☄️☄️
— Otho (@othomn) June 12, 2019
Plot inspired to the one of @Christi58451746
Data: https://t.co/ptnd2E9K7y
Code: https://t.co/100P1DX41S pic.twitter.com/eciHpCMcpU
Ramen ratings for the 10 countries in the dataset with most measurements.#Tidytuesday #dataviz
— Otho (@othomn) June 4, 2019
🍜🍜🍜 rated by https://t.co/F46UiOAj5D
code: https://t.co/nvYAAN8aXp
Malaysia wins 🥇 pic.twitter.com/2Re9U7bRUb
The good news is that wine prices don't skyrocket until a rating of 95 or higher.
— Otho (@othomn) May 28, 2019
Happy #TidyTuesday 🍷😋😁.
Data: https://t.co/lCCobClmIu
Code: https://t.co/uqrKmofyX3 pic.twitter.com/kUcpixn3lT
#TidyTuesday, trying also a bit of clustering of:
— Otho (@othomn) May 23, 2019
- GDP,
- plastic waste,
- mismanaged plastic w.
Clusters overlap a bit, but you can see three patterns of plastic use and mismanaged wastes in high, medium and low GDP countries.
Untidy 😬🙄 code https://t.co/xHtuIbmSmk pic.twitter.com/JXKLfHuYgE
My Wednesday #TidyTuesday 😋 #rstats
— Otho (@othomn) May 22, 2019
Is inequality mirrored also in waste plastic management?
I checked the distribution of the percentage of mismanaged plastic waste, and it's kind of bimodal.
🗞️ https://t.co/tbLganZ3V2
👨 💻 https://t.co/xHtuIbmSmk pic.twitter.com/f7BTsS6y3Q
Happy #TidyTuesday!
— Otho (@othomn) May 14, 2019
I took inspiration from the concept of @MargaCorralG, showing Nobel categories by age and gender.
Data source: https://t.co/BAMDMewGwW
Code: https://t.co/2XblXycrp5
…way beyond gender imbalance pic.twitter.com/cmVBongNRq
For a late #FunctionFriday, I would propose ggforce::geom_link().
— Otho (@othomn) May 10, 2019
It draws a segment, and it computes the ..index.. variable. So you can change aesthetics (such as alpha and size) along that segment.
⬇️⬇️Like in the plot below⬇️😋 pic.twitter.com/2EOT9GF8Xa
Refined a bit the #rstats model (still, big margin for improvement 😅).
— Otho (@othomn) May 3, 2019
New plot: Every species ranked from the most likely to be vulnerable to windows light. The last rows contain only very sparse observations.
⬆️⬆️Data source and code: see above ⬆️⬆️#Tidytuesday pic.twitter.com/xRVt4CHH2R
For this #tidytuesday I tried to make a graph of Anime genres, and how they are connected.
— Otho (@othomn) April 26, 2019
Source: https://t.co/sYi52aZbqG
code: https://t.co/GgQljYmCNc#rstats pic.twitter.com/hU4HJIyhKm
For this #TidyTuesday I tried to tackle the plot “Still a man's world”, which is left as an open question in this very interesting blog post by @MissSarahLeo
— Otho (@othomn) April 17, 2019
Source blog post 🗞️ : https://t.co/nJL4RPKNiI
code 👨 💻: https://t.co/yGqK7wVjQi pic.twitter.com/Ay9S3pj7WG
365 days cycling in Seattle 🚴 ♂️🚴 ♀️🚴 ♂️🚴 ♀️
— Otho (@othomn) April 6, 2019
For this #tidytuesday I tried to make a big poster with bike measurements for every day in 2017, to practice a bit layouts of heavily faceted plots.
data: https://t.co/KnjLuLZH0R pic.twitter.com/MnVQ8HshAd
When you want to highlight or discuss (relatively) small effects, what about a ridge plot, with arrows that show the median percent different from the reference group?
— Otho (@othomn) April 1, 2019
code on a simulated dataset: https://t.co/G7esC1pJ7q
Done with #rstats and #ggplot2 pic.twitter.com/fLgEjANggY
TIL,
— Otho (@othomn) March 30, 2019
a detailed choropleth map might be easier to understand if you show only selected borders, instead of all of them.
Of course it depends from you audience, and which area/administrative region you think they know best.#dataviz in #ggplot2 pic.twitter.com/kiTpsvhZNH
An alternative version to the previous #tidytuesday. Always dataviz in #ggplot2
— Otho (@othomn) March 25, 2019
Inspired to the famous “Baby Spike” design by @NadiehBremer and @zanstrong https://t.co/uLDLSrQzsg
Data maintained by Stanford Open Policing
code: https://t.co/ZEI4ZhQOsx pic.twitter.com/q88y1KRYIy
Late #tidytuesday. A bit of #dataviz in ggplot2.
— Otho (@othomn) March 24, 2019
Trying to reproduce the mirrored density plot discussed here https://t.co/sYHIxR5WF4 by @kennelliott.
data source: https://t.co/ZbUkDOUak5
code: https://t.co/V2gITxIHEf pic.twitter.com/fgEHmAo1TZ
#TidyTuesday week 10, #rstats
— Otho (@othomn) March 5, 2019
No margin for doubts.
Source: https://t.co/8gLWOO5MCD
Code: https://t.co/rhIeLBgegF pic.twitter.com/LmWRO8nnnH
#TidyTuesday week 10, #rstats
— Otho (@othomn) March 5, 2019
No margin for doubts.
Source: https://t.co/8gLWOO5MCD
Code: https://t.co/rhIeLBgegF pic.twitter.com/LmWRO8nnnH
Last minute blogpost on the last week #TidyTuesday.
— Otho (@othomn) February 19, 2019
A bit of scaling and clustering observations :) #rstats
🙌😊https://t.co/0jrwMGbEcX🌙☀️ pic.twitter.com/3Hn6KPrgiV
I was searching new visualizations for a #TidyTuesday, instead I found some @accidental__aRt 😁 pic.twitter.com/VSiYDODKN6
— Otho (@othomn) February 14, 2019
2⃣nd plot for this #TidyTuesday 😅
— Otho (@othomn) February 13, 2019
I tried to scale the spending between 0 and 1 (to highlight relative patterns), and to visualize clusters with heatmaps.
👨 💻https://t.co/yO7S3jQO1D pic.twitter.com/GaotwRsEFr
Two clusters of R&D spending for this week #TidyTuesday? #rstats
— Otho (@othomn) February 13, 2019
Coming soon:
1⃣A better way to scale/normalize data?
2⃣What happens if you cluster only the Pres. Obama era?
3⃣A blog post with tidy code
for now:
🗞️https://t.co/SJElIPfJXg
👨 💻https://t.co/znoeo6IbvV pic.twitter.com/wd6HivpAE2
Late #TidyTuesday , #dataviz
— Otho (@othomn) February 1, 2019
Visualization practice, I'm showing consumption of butter and its percent change compared to the previous year.
I did this plot on “butter” just because its consumption changes.
Source: https://t.co/yNp2qkOVO5
Code: https://t.co/flNedKO1kh pic.twitter.com/yoTHspwBxZ
A small modification to an old #TidyTuesday, with a bit of #rstats 🙂.
— Otho (@othomn) January 16, 2019
Probabability estimates that a new captive cetaceans was acquired by friendly means (not captured) as a function of time, by logistic regression.
⬇️⬇️⬇️source [<a href="https://twitter.com/puddingviz?ref_src=twsrc%5Etfw">@puddingviz</a>] and code in tweet below ⬇️⬇️⬇️ pic.twitter.com/W6M0yYHwiq
Week 38 of #tidytuesday #rstats, luckily, the practice of capturing cetaceans has declined in the '90s.
— Otho (@othomn) December 18, 2018
Check the code :) https://t.co/4y6611Ilbn pic.twitter.com/Q0eIpbDYyq
Yes #tidytuesday (or tardythursday 😅) #36, again based on plots found on https://t.co/cxBXdnkZPd #rstat
— Otho (@othomn) December 7, 2018
Check the code: https://t.co/7X7sHmwKfu pic.twitter.com/0wMtrgCiVR
An alternative version, with ggalluvial. Great package :)#tidytuesdayhttps://t.co/4pPaHyBMQn pic.twitter.com/1qBa29FiYt
— Otho (@othomn) November 21, 2018
Also, a week old #TidyTuesday on CRAN downloads.
— Otho (@othomn) November 10, 2018
I have drafted a small script that models package download data as Poisson and tells you which packages, among those you have installed, had the most unexpected surge in download counts. #rstat
Code 🙂: https://t.co/AZqm58m9G5 pic.twitter.com/QX907N312H
Still much to learn about plotting maps. This is my take on last week #TidyTuesday #dataviz
— Otho (@othomn) November 10, 2018
Check the code 🙂: https://t.co/ym8MuX4H4s pic.twitter.com/fi18MKDJox
Old #TidyTuesday, but I had it ready. Week28, US election. Indeed midterm elections are consistently less attended. #rstat
— Otho (@othomn) October 23, 2018
Data (PDF):https://t.co/6Qkgy9fgOH
Code: https://t.co/EAYRBjvdHC pic.twitter.com/VxbVSda7UD
I had to keep this week #tidytuesday simple, because: time 😅
— Otho (@othomn) October 4, 2018
That pattern though, amazing. Now I want to know who of the people that I know was born on a weekend, looks like a rarity 😄
check the code: https://t.co/syC68dXXsC pic.twitter.com/mtoXYz7Ghk
Some takes on this week #tidytuesday #r4ds
— Otho (@othomn) September 29, 2018
Italy has a rather low threat
code: https://t.co/cVO2XgBSY0 pic.twitter.com/WG4xcpZI2C
I'm sure that this is, by far, not the right way to model this data😅
— Otho (@othomn) September 23, 2018
Anyway, late #tidytuesday #r4ds, passengers in 2012 vs. increase in passengers between 2012 and 2017. With a robust fit, prediction boundaries and labelled outliers.
code: https://t.co/pb5XBduKRo pic.twitter.com/R3F8dgnLun
Households with cats and dogs in the US for this week #TidyTuesday #r4ds
— Otho (@othomn) September 11, 2018
Again, modeled after the plots in @kjhealy great Socviz book.
Code: https://t.co/0yhFHC0hFd pic.twitter.com/uFkn8dackk
My take on #tidytuesday fast-food dataset.
— Otho (@othomn) September 9, 2018
Which word in item's names is associated with the most calories?
A lot more tidying to do to get nice results 😅 but it's funny to see “Tenders” up there at the top with “King”, “Super” and “Footlong”.#r4ds pic.twitter.com/77sbsKbITA
Week old #TidyTuesday #r4ds, but I had it there.
— Otho (@othomn) September 6, 2018
Not an expert on football, took me some guessing, and I'm still not sure I'm guessing it right.
The more rush attempts, the more yards rushed. I also labeled some outliers that did better then expected.https://t.co/NRoF466Gd6 pic.twitter.com/t8ZXGuGI3C
Yes, done it! :) #TidyTuesday #r4ds
— Otho (@othomn) August 24, 2018
Is there a 6 year pattern in big fires? Does it match heatwaves?
Also, overall, are big fires increasing since the '90s?
data: https://t.co/xXeZjjSlrc
code: https://t.co/3p6m4k7WTk pic.twitter.com/Q1JwNkkbM3
Very late #tidytuesday #r4ds
— Otho (@othomn) August 20, 2018
IRA tweets in the @FiveThirtyEight dataset, curated by @ollie , credits for the original set to Darren Linvil and Patrick Warren.
Russian tweets among the most followed.
data: https://t.co/eRA9aVoZnM
code: https://t.co/uQrujKzvZ5 pic.twitter.com/5cYs6BtzPN
A bit of data wrangling with the #tidyverse for this #TidyTuesday
— Otho (@othomn) June 20, 2018
Checkout the code on github :) https://t.co/xtEA7jSRZr pic.twitter.com/9tfaAnwTe2
Fun with visualization for this #TidyTuesday :)
— Otho (@othomn) June 12, 2018
Two versions, with and without number.
Inspired by: https://t.co/Jd6IZCC56h
Code here: https://t.co/QCYP6gB1H2 pic.twitter.com/6vLbYPMEfO
Great idea this #TidyTuesday :) Code here https://t.co/q3W7dHu3Zu pic.twitter.com/rifC5iw2tZ
— Otho (@othomn) June 10, 2018
