The Data Digest

[The Data Digest @DigestData]

@agraybee I did make a interactive chart for top IMDB shows that compares the average rating to the end (last 2 episodes):
https://t.co/4MHGR03BT0
Dexter and HIMYM also ended pretty badly.

[The Data Digest @DigestData]

@vidIQ 50

[The Data Digest @DigestData]

@vidIQ https://t.co/1bncCE2ItH

[The Data Digest @DigestData]

@JanBroderEngler Sweet. Thank you :)

[The Data Digest @DigestData]

@selcukorkmaz Does that mean that 3. Residuals are the remaining "errors" or variance that cannot be explained by the fixed effects and random effects (hospitals) but are unique to each individual in the study?

[The Data Digest @DigestData]

@JoachimSchork Looks very clean and informative indeed!
Also great to see that the am:cyl estimate CI95 overlaps with 0, hence the p-value > 0.05
However the most important factor for mpg is wt, explaining 74,46% of the variance alone. am, cyl etc. correlate with wt in the mtcars dataset.

[The Data Digest @DigestData]

No worries, I am glad I saw the post. Helped me to brush up on type1/type2 error distinction and I did not even know what F1 score is and how Precisions is defined. The fastml package looks really powerful. I will check it out when I dive deeper into tidymodels, or is that the wrong order? What would you recommend.

[The Data Digest @DigestData]

She was also the best performing woman in the Titled Tuesday rapid tournaments on chess com last year. With an average score of 6.82 in 37 participations.
A 9 game winning streak and a best place of 37.
Win White 59.8%, Win Black 50.5%
Average opponent rating she defeated: 2579
More 2024 analysis:
https://t.co/Irg6xH8Upa

[The Data Digest @DigestData]

@chesscom_in @ArjunErigaisi That means he is outperforming his 2024 results already. There he could not win a tournament in 39 tries and his longest winning streak was 9 games.
2024 stats: max score 9.5 (median 8), Win ⚪ 68%, Win ⚫ 66.5%.
More 2024 analysis here:
https://t.co/qWtfhxhgVp

[The Data Digest @DigestData]

@frankiethull view()

[The Data Digest @DigestData]

@R_Graph_Gallery I am one of the 34% because I already write code like the AFTER-image 😇. Not 100% but with tab and Enter R is doing most of the formatting already. But thanks for pointing out Air and formatter package. Will give it a try soon.

[The Data Digest @DigestData]

@micosapiens711 @AmazfitGlobal @ZeppGlobal Awesome :) I have a fitbit and it also tracks sleep. REM, deepsleep, overall duration and wake-time, and then builds an overall index. When I hit the gym to hard or to late in the day, I have trouble sleeping, but long walks outside or runs in the morning help a lot sleepwise.

[The Data Digest @DigestData]

Because the challenge category is time series, I’m showing life expectancy vs. GDP for 140 countries from 1952 to 2007. As countries develop economically, people also live longer lives.

[The Data Digest @DigestData]

Day 23 (Log_Scale | Time series) #30daychartchallenge
There are many ways to show continuous & skewed data on a log scale in #rstats 📉

1️⃣ Transform directly in aes(x = log10(gdpPercap))
2️⃣ Use scale_x_log10()
3️⃣ Use scale_x_continuous(transform = "log10")
4️⃣ Or go with coord_trans(x = "log10")

I like scale_x_log10(), because you can specify the breaks and labels with:
breaks = c(250, 500, 1000, 2500, 5000, ...),
labels = scales::dollar_format()

Also, annotation_logticks(sides = "b") is a great way to show where key log steps lie. #dataviz #ggplot2

[The Data Digest @DigestData]

Day 23 (Log_Scale | Time series) #30daychartchallenge
There are many ways to show continuous & skewed data on a log scale in #rstats 📉

1️⃣ Transform directly in aes(x = log10(gdpPercap))
2️⃣ Use scale_x_log10()
3️⃣ Use scale_x_continuous(transform = "log10")
4️⃣ Or go with coord_trans(x = "log10")

I like scale_x_log10(), because you can specify the breaks and labels with:
breaks = c(250, 500, 1000, 2500, 5000, ...),
labels = scales::dollar_format()

Also, annotation_logticks(sides = "b") is a great way to show where key log steps lie. #dataviz #ggplot2

[The Data Digest @DigestData]

@nastengraph I like these double log charts to visualize power-law relationships. I think body mass vs. heart rate also leads to a straight line. As does log(heart rate) vs. life expectancy for mammals.

[The Data Digest @DigestData]

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