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R Notebooks - Posit

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Today we're excited to announce R Notebooks, which add a powerful notebook authoring engine to R Markdown. Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Notebooks are also an excellent tool for teaching and a convenient way to share analyses. You can try out R Notebooks today in the RStudio Preview Release. Interactive R Markdown As an authoring format, R Markdown bears many similarities to traditional notebooks like Jupyter and Beaker. However, code in notebooks is typically executed interactively, one cell at a time, whereas code in R Markdown documents is typically executed in batch. R Notebooks bring the interactive model of execution to your R Markdown documents, giving you the capability to work quickly and iteratively in a notebook interface without leaving behind the plain-text tools and production-quality output you've come to rely on from R Markdown. R Markdown Notebooks Traditional Notebooks Plain text representation ✓ Same editor/tools used for R scripts ✓ Works well with version control ✓ Focus on production output ✓ Output inline with code ✓ ✓ Output cached across sessions ✓ ✓ Share code and output in a single file ✓ ✓ Emphasized execution model Interactive & Batch Interactive This video provides a bit more background and a demonstration of notebooks in action: [youtube https://www.youtube.com/watch?v=zNzZ1PfUDNk&w=560&h=315] Iterate Quickly In a typical R Markdown document, you must re-knit the document to see your changes, which can take some time if it contains non-trivial computations. R Notebooks, however, let you run code and see the results in the document immediately. They can include just about any kind of content R produces, including console output, plots, data frames, and interactive HTML widgets. You can see the progress of the code as it runs: You can preview the results of individual inline expressions, too: Even your LaTeX equations render in real-time as you type: This focused mode of interaction doesn't require you to keep the console, viewer, or output panes open. Everything you need is at your fingertips in the editor, reducing distractions and helping you concentrate on your analysis. When you're done, you'll have a formatted, reproducible record of what you've accomplished, with plenty of context, perfect for your own records or sharing with others. Batteries Included R Notebooks can run more than just R code. You can run chunks written in other languages, like Python, Bash, or C++ (Rcpp). It's even possible to run SQL directly: This makes an R Notebook an excellent tool for orchestrating a reproducible, end-to-end data analysis workflow; you can easily ingest data using your tool of choice, and share data among languages by using packages like feather, or ordinary CSV files. Reproducible Notebooks While you can run chunks (and even individual lines of R code!) in any order you like, a fully reproducible document must be able to be re-executed start-to-finish in a clean environment. There's a built-in command to do this, too, so it's easy to test your notebooks for reproducibility. Rich Output Formats Since they're built on R Markdown, R Notebooks work seamlessly with other R Markdown output types. You can use any existing R Markdown document as a notebook, or render (knit) a notebook to any R Markdown output type. The same document can be used as a notebook when you're quickly iterating on ideas and later rendered to a wholly different format for publication – no duplication of code, data, or output required. Share and Publish R Notebooks are easy to share with collaborators. Because they're plain-text files, they work well with version control systems like Git. Your collaborators don't even need RStudio to edit them, since notebooks can be rendered in the R console using the open source rmarkdown package. Rendered notebooks can be previewed right inside RStudio: While the notebook preview looks similar to a rendered R Markdown document, the notebook preview does not execute any of your R code chunks; it simply shows you a rendered copy of the markdown in your document along with the most recent chunk output. Because it's very fast to generate this preview (again, no R code is executed), it's generated every time you save the R Markdown document. The generated HTML file has the special extension .nb.html. It is self-contained, free of dependencies, and can be viewed locally or published to any static web hosting service. It also includes a bundled copy of the R Markdown source file, so it can be seamlessly opened in RStudio to resume work on the notebook with all output intact. Try It Out To try out R Notebooks, you'll need to download the latest RStudio Preview Release. You can find documentation on notebook features on the R Notebooks page on the R Markdown website, and we've also published a video tutorial in our R Notebooks Webinar. We believe the R Notebook will become a powerful new addition to your toolkit. Give it a spin and let us know what you think!

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POSIT.CO

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09.17.25-NMDP-PublicEdit-Final NonConf

Hi. My name is Pierce.

I am nine years old, and I'm going into fourth grade. When I was young, I had leukemia.

So, we thought Pierce had just a series of ear infections.

When we went in for the third or fourth time that's when we realized maybe something else is going on. And, we were at the doctor's office around noon, twelve thirty. And, by three or four, the blood is was taken and we were sat down by the physician and they said your your son has cancer.

Three days later, he had his first surgery.

He proceeded with what ended up being nineteen surgeries, thirty one platelet transfusions, and sixteen blood transfusions.

He had two rounds of chemo in his bone marrow transplant.

The appearance worst nightmare.

I think about data science and data scientists as we're not typically, you know, superheroes that are, like, super buff or we can, like, move cars with one hand, but I feel like our ability to understand, communicate, and analyze data is kind of our superpower. But with great power comes great responsibility.

One of the major issues facing patients is that less than half of donors we contact are willing or able to donate. But one tool we made for those hospitals to help them is this donor readiness score. And what the donor readiness score is supposed to predict is the likelihood that a donor will say yes if we ask them to donate.

This donor readiness score is built and trained on the data we have for all the cases where we reach out to a donor and ask them, are you able and willing to donate? We have registry engagement with our members, so we reach out to them every year and ask them if they're still interested in being on the registry. And those that respond to our outreach tend to be much more likely to say yes when called upon.

Once we understand how all these factors like age and race and registry engagement are linked to whether they'll say yes or no, then we're able to train our machine learning model to make predictions on future cases.

To help explain the idea, let's look at a theoretical example. Every search through NMDP provides patients with access to more than forty two million potential blood stem cell donors. For a patient, there may be a thousand potential donors that are a match based on genetic factors.

After a doctor filters for age, the pool decreases to one hundred, and from that group, a a doctor may eliminate ninety based on additional factors like blood type matching. So now we're left with ten potential matches for the patient. This is where the donor readiness score is really important. Based on all these factors, plus a myriad of engagement metrics, the score is designed for the single purpose of finding donors who are most likely to say yes when they are called to donate.

It's a probability, and all things being equal, a transplant center would probably wanna pick someone with a seventy score over someone with a thirty percent probability of saying yes or being available. The ultimate downstream impact of our work, we talk about it all the time here is lives.

You know, saving more lives.

That's what we're all working towards.

I'm a data scientist. I work in health care.

But really, no matter what industry we're in, we're all kinda solving variations of the same problem. We love that Pozett has developed such a welcoming ecosystem, an ecosystem of tools and tooling.

Philosophically, I think the data science team is very committed to the same sorts of things that Posit is committed to.

We believe that data should be public and easily accessible and where possible free. We believe tools should be open sourced. From an infrastructure standpoint, it's core to our working style. So we use Connect to show off results and schedule automated jobs. We use Package Manager to host our own internal packages to make collaboration easier.

One of the ways we use Pzl Connect is to host our data science storefront. It serves as our one stop data science shop where stakeholders across the business work in different ways to get at these same key metrics, but they can all come here and see what we project for the next month in terms of how many lives do you think will impact. Are there any headwinds we might be facing? How should we pivot?

So we actually see a lot of our patients who get to meet their donors after about a year. And every time you see that first meeting between the donor and the patient, there's so much raw, authentic emotion, tears coming out, everyone's just hugging each other. And it really it really reminds you that, hey, these aren't just numbers. These are lifesaving numbers.

Three, two, one.

Our son's name is Pierce. We call him Fierce Pierce.

In twenty seventeen, he was diagnosed with acute milder leukemia. Now, more than seven years later, he's cancer free and happy and healthy.

I see him vibrant. I see him as full of life.

He's just a neat kid, and I wouldn't have been able to get to know him without the work that was done.

Having the chance to grow up is such a privilege. Having the chance to have gray hair is such a privilege.

Just being able to see him grow, to where he is right now is such a privilege, and not all parents get to see that.

When I grow up, I wanna be a doctor to help kids like me. I wanna save other people like other people save me.

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Posit-Academy

[MUSIC PLAYING]

Data science training is broken.

You might think you're learning and you might feel like you're learning, but it's really difficult for that knowledge to stick.

And here's how we fixed it. We've replaced lectures and workshops with projects.

Academy is not a typical data science training. It's really structured more like an apprenticeship. So that means that learning data science is going to be very hands on. It's going to be meaningful. It's focused around a real data science project, which we think makes the learning experience much more realistic

The thing that I think is great about RStudio Academy, you work together with a team of your peers, with really engaging material, regular check-ins with mentors, that forces you to really dive deep into the material and understand it.

Having an opportunity to have these mentored sessions, where they get together and actually talk about what they're learning and discuss with each other and compare notes, having this extra support structure at the beginning can be incredibly valuable.

So the program is really customized in that everything is centered around a project. And that project is developed with you. The project is going to be something that's relevant to your field.

Learning data science in bursts and spurts, maybe you do a two-day workshop here, a two-week camp there. I think there are a lot of these opportunities. But if you're not going to keep up with it, it becomes very difficult for that to be a real part of your life.

What's neat about Academy, I think is the focus on quality. It's not about how many people we can train, but how can we train people the best way possible.

When you get a little bit of time to really digest it in between the learning sessions, I think there's a lot more chance of being able to retain that knowledge. You're meeting with a mentor. So someone who can give you very tailored and customized guidance.

Even if you've never done it before, you can learn how to code, to do data science in R. I think it's actually a really great way to learn how to program because you can do all of the stuff that's really fun. You can create fantastic visualizations, that there's no way you could do in Excel or other tools.

Indeed, it provides a way to discover things about the world that you cannot learn anywhere else. The secrets that you can uncover by analyzing data are things that in the past only magicians or wizards would claim to be able to know. But here we can know it. And it's not magic. It's real science.

I personally find it super enjoyable to work with raw data.

At its heart, code is a mechanism of communication. You've got almost unlimited freedom to construct exactly the right solution to the problem you're facing.

There's no doubt that you could serve your customers better, create value better, save the world more efficiently if you're paying a close attention to what the data is telling you about the world.

I think that's really what makes Academy so effective is that you're learning skills and then applying those skills in a project. It's going to be more meaningful. It's going to be a better learning experience. And it's going to stick with you a lot longer.

I certainly get so much joy out of programming and interacting with data via code. And I really believe that anyone can experience that same joy, no matter whether or not you've programmed before.


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Requests	IP Address	Location	AS Autonomous System
13	216.58.206.67	United States	AS15169GOOGLE
2	52.222.236.105	United States	AS16509AMAZON-02
2	192.0.72.20	San Francisco, California, United States	AS2635AUTOMATTIC
2	18.173.205.104	United States	AS16509AMAZON-02
2	216.58.206.72	United States	AS15169GOOGLE
2	142.250.186.106	United States	AS15169GOOGLE
2	142.251.140.164	United States	AS15169GOOGLE
2	18.173.205.116	United States	AS16509AMAZON-02
2	151.101.129.229	San Francisco, California, United States	AS54113FASTLY
2	151.101.1.229	San Francisco, California, United States	AS54113FASTLY
105	47	-	-

Security Scan Report: posit.co

Summary

AI Security Verdict

Safe Website

Safety Factors

Details

Screenshot

Page Load Overview

Language Analysis

Primary Language

Detection Details

Website Classification

Primary Category

All Detected Categories

Detected Features

Domain & IP Information

Detected Technologies13

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

ssdeep (Context Triggered Piecewise Hashing)

sdhash (Similarity Digest Hashing)

Image Hashes

Perceptual Hashes

Other Hashes

Scan History