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Karandeep Singh Profile picture
@kdpsinghlab
Apr 2 • 5 tweets • 3 min read Twitter logo Read on Twitter
🧹 Tidier.jl v0.7.1 is now on the #JuliaLang registry.

What’s new?

- drop_na()
- lag() and lead() - re-exported from ShiftedArrays.jl
- Bugfix to ntile() if all values are missing

Thanks to @KriseScheuch for feature suggestions!

github.com/kdpsingh/Tidie… ImageImage
One interesting thing is that lag() and lead() take in a vector and return a vector (similar to ntile).

This means that these functions *should not* be auto-vectorized. So in addition to re-exporting, they are included on the package’s do-not-vectorize list.
In the near future, we will provide a mechanism to edit the package’s do-not-vectorize list of functions.

For frequently used functions, this means you won’t have to use the tilde-prefix notation to call them.

See here for details on auto-vectorization: kdpsingh.github.io/Tidier.jl/dev/…
Continuing to chip away at our to-do list.

(Only a small sample shown here)

github.com/kdpsingh/Tidie… Image
As of v0.7.1, Tidier.jl supports the following functionality from #rstats {tidyverse}: ImageImage

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More from @kdpsinghlab

Karandeep Singh Profile picture
Apr 3
Why does a proprietary sepsis model “work” at some hospitals but not others?

Is it generalizability? Measurement? Intervention? Patient population? Margin for improvement? Resource constraints?

Working with a team led by @_plyons, we looked at a 9-hospital network.

A story.
In earlier single-center study @umichmedicine, our paper and accompanying editorial framed our AUC 0.63 as a failure of “external” validity. The result was somewhat surprising bc other studies reported higher AUCs/sens/spec.

Why?

jamanetwork.com/journals/jamai… https://t.co/Bdtv4h9LW7
Colleagues at UColorado reported an AUC of 0.73, and at Case Metro reported sens/spec of 90%/68% - quite good.

But both of those results included scores calculated after sepsis onset (and in some cases recognition).

And this is especially important here because… Image
Read 22 tweets
Karandeep Singh Profile picture
Mar 18
🧹Tidier.jl 0.6.0 is available on the #JuliaLang registry.

What’s new?

- New logo!
- distinct()
- n(), row_number() work *everywhere*
- `!` for negative selection
- pivoting functions are better
- bug fixes to mutate() and slice()

Docs: kdpsingh.github.io/Tidier.jl/dev/

A short tour.
If you use distinct() without any arguments, it behaves just like the #rstats {tidyverse} distinct().

It checks if rows are unique, and returns all columns just as you would expect.
If you use distinct() with arguments as shown here, then it returns all columns for unique values of the supplied column.

This is slightly different behavior than {tidyverse} distinct(), but I kind of like it. Can easily pair this with select() to mimic dplyr behavior.
Read 9 tweets
Karandeep Singh Profile picture
Feb 25
A Visual Tour of the Meta-Tidyverse

For years, I’ve been trying out different non-tidyverse implementations of tidyverse. It’s fun seeing folks mold languages to run analysis code inspired by it.

If you like screenshots of code, come along for a visual tour.

Let’s start w/ R.
If you thought that one tidyverse was enough for R, you would be wrong.

There are 2 fully independent re-implementations: {poorman} and {tidytable}.

{poorman} is powered by base R only - no dependencies! It’s a great pkg to use with binder/CI workflows.

cran.r-project.org/web/packages/p…
{tidytable} has a similar premise, except it relies primarily on {data.table} and {tidyselect}.

While it’s similar to {dtplyr} in some ways, the syntax is even cleaner bc you don’t need to declare your data.table or use collect() to get the results.

github.com/markfairbanks/…
Read 16 tweets
Karandeep Singh Profile picture
Feb 23
If a tree falls in the forest but there’s no one around to hear it, does it really make a sound?

If a model detects a patient in need of ICU-level care but there are no ICU beds available, did the model really help the patient?
When we link an intervention to a model threshold (eg alerts), we often worry about overalerting.

Overalerting can take on multiple forms. Either there are too many alerts bc many alerts are wrong. Or, there are too many alerts bc we lack capacity to act even if they are right.
Consider this: a model scoring 10 patients. Using a threshold of 20%, you identify 4 out of 5 patients needing ICU-level care.

The sensitivity is 80%, right?

But what if you only have 3 beds available?
Read 8 tweets
Karandeep Singh Profile picture
Feb 22
Why do seemingly useful models fail to improve clinical outcomes when implemented? Resource constraints.

In this paper, we describe constraints, how they affect net benefit, and how they apply to other measures.

Paper: academic.oup.com/jamia/advance-…

R pkg: github.com/ML4LHS/modelre… Image
We use 4 case studies to show how a resource constraint diminishes the usefulness of a model and changes the optimal resource allocation strategy.

We show that some of the usefulness can be recouped by introducing a relative constraint (and relaxing the absolute constraint). ImageImage
All of the results in the paper can be reproduced using the accompanying {modelrecon} #rstats package and are shown in the README file accompanying the package.

github.com/ML4LHS/modelre… ImageImageImageImage
Read 5 tweets
Karandeep Singh Profile picture
Jan 31
My lab is moving to #JuliaLang, and I’ll be putting together some R => Julia tips for our lab and others who are interested.

Here are a few starter facts. Feel free to tag along!

Julia draws inspiration from a number of languages, but the influence of R on Julia is clear.
Let's start with packages.

Like R, Julia comes with a package manager that can be used to install pkgs from within the console (or REPL). The Pkg package isn't automatically imported in Julia but it's easy to do.

Both are different from Python's command line approach to pkgs.
Julia natively takes pkg management much further than R. Want to install a package from GitHub? Easy, just add a url argument to the add function.

Pkg.add(url = "github.com/kdpsingh/TidyT…")
Read 33 tweets

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