Cristina @biologeek

Metabolomics, #Rstats and feature importance curiosity 🤔 here::here() Joined May 2009

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Thomas Lin Pedersen @thomasp85

6 years ago

It never ceases to amaze me what people can make with gganimate #rstats

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Colin 🤘🌱🏃‍♀️ @_ColinFay

6 years ago

#RStats — Can we scrape the online documentation of an API to automate the creation of an R wrapper 📦? Spoiler: yes. "Automate the Creation of an API Wrapper package by Scraping its Online Documentation" colinfay.me/fun-from-api-d…

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Dilsher Singh Dhillon @dhillon_stats

6 years ago

@sharlagelfand I think dplyr::all_equal() should do most of that. Not sure about types

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@[email protected] @BenjaminWolfe

6 years ago

@charliejhadley omg I am always using this now! usethis.r-lib.org/reference/git_…

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John Sheffield @johnmsheffield

6 years ago

@_ColinFay upvoting qs- handles any R object and comparable to fst in speed. The main difference from fst is qs doesn’t support random access, eg how fst allows reading only specific cols/rows. But read/write speeds overall close. I think they share a bunch of implementation strategies.

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Daniël Lakens @lakens

6 years ago

Retweeting because I am really excited about this. I am willing to bet that 1) thinking about the next hypothesis you will test in machine readable terms will immediately improve what you are doing, and 2) better meta-data will make science massively more efficient.

Daniël Lakens @lakens

6 years ago

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Birunda Chelliah @cbirunda

6 years ago

TIL: I learnt about the conflicted 📦 My filter function always gets masked, so my solution till today was dplyr::filter. But there is a better way! You can set your function:library preference at the top of your script! 😭🙏 e.g. conflict_prefer("filter", "dplyr") #rstats

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Nick Strayer @NicholasStrayer

6 years ago

gist.github.com/nstrayer/7af11…

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Ryan Holbrook @ryanpholbrook

6 years ago

A thread of classifiers learning a decision rule. Dashed line is optimal boundary. Animations with #gganimate by @thomasp85 and @drob. #rstats Logistic regression {stats::glm} with each class having normally distributed features. (1/n)

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Max Kuhn @topepos

6 years ago

@thomasp85 I finally got around to looking up the linear algebra of matrix rotations for my PCA explanation.

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Maarten van Smeden @MaartenvSmeden

6 years ago

@92jackzou We explain the concept of calibration in the link below. In short, calibration is about the predicted risks (probabilities) that come out of your prediction model and whether or not these risks are consistent with the proportion of events you observed

Maarten van Smeden @MaartenvSmeden

6 years ago

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Maarten van Smeden @MaartenvSmeden

6 years ago

@SPilleron You can read about it in @ESteyerberg's book, and @BenVanCalster wrote quite a few things about it, see: ncbi.nlm.nih.gov/pubmed/26772608 ncbi.nlm.nih.gov/pubmed/31842878

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Steph Locke @TheStephLocke

6 years ago

Instead of referring to myself as self-taught, I'm gonna start referring to myself as community-taught. The sites, the blogs, the books, the user groups, the confs, the forums ... all community efforts that I used to learn and advance my programming and data science knowledge.

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Maarten van Smeden @MaartenvSmeden

6 years ago

Computer: change your password Me: ********** Computer: new password does not meet requirements Me: **************** Computer: new password does not meet requirements Me: ************************** Computer: new password does not meet requirements Me:

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Jenny Bryan @JennyBryan

6 years ago

@Gummifot The null-coalescing operator %||% is in the miscellany section of this talk on making conditional logic easier to read and maintain (links to a specific slide): speakerdeck.com/jennybc/code-s… %||% is key to some nice design patterns eg, using NULL as the default val of optional args.

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Cristina @biologeek

6 years ago

This is awesome! And happy to see @thomasp85 #geneRativeart projects included in the list 🎨💻

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Robert Plenge @rplenge

6 years ago

Introducing PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP starts with EMR data (structured and NLP) and outputs a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no). nature.com/articles/s4159…

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Xavier Domingo-Almenara @XaviDomingo1

6 years ago

Interested in small molecule retention time prediction? Check out our new paper in @NatureComms introducing the SMRT dataset, containing the experimental RT of 80K molecules generated in @kadzuis lab at @scrippsresearch #metabolomics #MachineLearning nature.com/articles/s4146…