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Join Us Dec 10 at the COVID-19 Data Forum: Using Mobility Data To Forecast COVID-19 Cases

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Thurs, December 10th, 9am PDT/12pm EDT/18:00 CEST – Register now!

Hosted by the COVID-19 Data Forum/Stanford Data Science Initiative/R Consortium


Join the R Consortium and learn about mobility data in monitoring and forecasting COVID-19. COVID-19 is the first global pandemic to occur in the age of big data. All around the world, public health officials are testing and releasing data to the public, giving ample cases for scientists to analyze and forecast in real-time.

Despite having so much data available, the data itself has been limited by simplistic metrics rather than higher dimensional patient-level data. To understand how COVID-19 works within the body and is transmitted, scientists must understand why the virus causes harm to some more than others.

Sharing this type of data brings up patient confidentiality issues, making it difficult to get this type of vital data.

The COVID-19 Data Forum, a collaboration between Stanford University and the R Consortium, will discuss the ways in which people’s mobility data holds promises and challenges in combating the spread of SARS-CoV-2, as well as how the public has behaved in response to the pandemic. This data is vital in understanding the way individual’s patterns have shifted since the pandemic, helping us to better understand where people are going and when they are getting sick. 

The event is free and open to the public.

Speakers include:

  • Chris Volinksky, PhD Associate vice-president, Big Data Research, ATT Labs.
  • Caroline Buckee Associate Professor of Epidemiology and Associate Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health.
  • Christophe Fraser Professor of Pathogen Dynamics at University of Oxford and Senior Group Leader at Big Data Institute, Oxford University, UK.
  • Andrew Schoeder, PhD Vice-president Research & Analytics for Direct Relief.

Registration and more info: https://covid19-data-forum.org

Register now! Brazilian Conference on Data Journalism and Digital Methods – Coda.Br 2020

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This November 2nd-7th, 2020, the 5th edition of the Brazilian Conference on Data Journalism and Digital Methods (Coda.Br) will be taking place with 50 national and international guest speakers and 16 workshops. Coda.Br is the largest data journalism conference in Latin America and this year will be completely online.

Organized by Open Knowledge Brasil and Escola de Dados (School of Data Brazil), Coda.Br boasts the support of multiple large scale associations including the Brazilian Association of Investigative Journalism (Abraji), R Consortium, Hivos Institute, Embassy of the Netherlands and the United States Consulate. 

For 2020, the conference will be offering main panels, keynote presentations, lightning talks, and a ceremony to announce the winners of Cláudio Weber Abramo Data Journalism Award. Coda.Br and its partners will also be offering 150 free yearly subscriptions to the School of Data Brazil membership program, granting free access to all event activities.

Tickets will be available for R$180 (1 year subscription to Escola de Dados membership, which allows access to workshops and the event chat, among other benefits) and R$40 (workshops only). This translates approximately to USD$32 and $7, respectively.

For more information and to register, please visit the Coda.Br website.

R/Pharma October 2020

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The R/Pharma virtual conference this year was held October 13-15th, 2020. R/Pharma focuses on the use of R in the development of pharmaceuticals, covering topics from reproducible research to drug discovery to genomics and beyond.

Over 1,000 people signed up for the 3 day, free event this year!

Designed to be a smaller conference with maximum interaction opportunities, R/Pharma was a free event that allowed keynote speakers in the R world to present their research and findings in ways that allowed for maximum viewer participation.

All presentations are given in ways that showcase using R as a primary tool within the development process for pharmaceuticals.

If you are interested in seeing some of the exciting work showcased at R/Pharma from the R Validation Hub, you can do so below. The R Validation Hub is a cross-industry initiative meant to enable use of R by the bio-pharmaceutical industry in a regulated setting. The presentations Implementing A Risk-based Approach to R Validation by Andy Nicholls and useR! 2020: A Risk-based Assessment for R Package Accuracy by Andy Nicholls and Juliane Manitz are available for viewing. Learn about risk assessment and assessing package accuracy with the R Validation Hub team! 

September 2020 ISC Call for Proposals – Now Open!

By Announcement, Blog

The deadline for submitting proposals is midnight, October 1st, 2020.

The September 2020 ISC Call for Proposals is now open. The R Consortium’s Infrastructure Steering Committee (ISC) solicits progressive, pioneering projects that will benefit and serve the R community and ecosystem at large. The ISC’s goal is to foster innovation and help bring your ideas into tangible realities. 


Although there is no set theme for this round of proposals, grant proposals should be focused in scope. If you are currently working on a larger project, consider breaking it into smaller, more manageable subprojects for a given proposal. The ISC encourages you to “Think Big” but create reasonable milestones. The ISC favors grant proposals with meaningful detailed milestones and justifiable grant requests, so please include measurable objectives attached to project milestones, a team roster, and a detailed projection of how grant money would be allocated. Teams with detailed plans and that can point to previous successful projects are most likely to be selected.


To submit a proposal for ISC funding, read the Call for Proposals page and submit a self-contained pdf using the online form.

From R Hub – JavaScript for the R package developer

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Originally posted on the R Hub blog

JS and R, what a clickbait! Come for JS, stay for our posts about Solaris and WinBuilder. 😉 No matter how strongly you believe in JavaScript being the language of the future (see below), you might still gain from using it in your R practice, be it back-end or front-end.

In this blog post, Garrick Aden-Buie and I share a roundup of resources around JavaScript for R package developers.

JavaScript in your R package

Why and how you include JavaScript in your R package?

Bundling JavaScript code

JavaScript’s being so popular these days, you might want to bundle JavaScript code with your package. Bundling instead of porting (i.e. translating to R) JavaScript code might be a huge time gain and less error-prone (your port would be hard to keep up-to-date with the original JavaScript library).

The easiest way to interface JavaScript code from an R package is using the V8 package. From its docs, “A major advantage over the other foreign language interfaces is that V8 requires no compilers, external executables or other run-time dependencies. The entire engine is contained within a 6MB package (2MB zipped) and works on all major platforms.” V8 documentation includes a vignette on how to use JavaScript libraries with V8. Some examples of use include the js package, “A set of utilities for working with JavaScript syntax in R“; jsonld for working with, well, JSON-LD where LD means Linked Data; slugify (not on CRAN) for creating slugs out of strings.

For another approach, depending on a local NodeJS and Node Package Manager (NPM) installation, see Colin Fay’s blog post “How to Write an R Package Wrapping a NodeJS Module”. An interesting read about NPM and R, even if you end up going the easier V8 route.

JavaScript for your package documentation

Now, maybe you’re not using JavaScript in your R package at all, but you might want to use it to pimp up your documentation! Here are some examples for inspiration. Of course, they all only work for the HTML documentation, in a PDF you can’t be that creative.

Manual

The roxygenlabs package, that is an incubator for experimental roxygen features, includes a way to add JS themes to your documentation. With its default JS script, your examples gain a copy-paste button!

Noam Ross once described a way to include a searchable table in reference pages, with DT.

In writexl docs, the infamous Clippy makes an appearance. It triggers a tweet nearly once a week, which might be a way to check people are reading the docs?

For actual analytics in manual pages, it seems the unknown package found a trick by adding a script from statcounter.

Vignettes

In HTML vignettes, you can also use web dependencies. On a pkgdown website, you might encounter some incompatibilities between your, say, HTML widgets, and Boostrap (that powers pkgdown).

Web dependency management

HTML Dependencies

A third, and most common, way in which you as an R package developer might interact with JavaScript is to repackage web dependencies, such as JavaScript and CSS libraries, that enhance HTML documents and Shiny apps! For that, you’ll want to learn about the htmltools package, in particular for its htmlDependency() function.

As Hadley Wickham describes in the Managing JavaScript/CSS dependencies section of Mastering Shiny, an HTML dependency object describes a single JavaScript/CSS library, which often contains one or more JavaScript and/or CSS files and additional assets. As an R package author providing reusable web components for Shiny or R Markdown, in Hadley’s words, you “absolutely should be using HTML dependency objects rather than calling tags$link()tags$script()includeCSS(), or includeScript() directly.”

htmlDependency()

There are two main advantages to using htmltools::htmlDependency(). First, HTML dependencies can be included with HTML generated with htmltools, and htmltools will ensure that the dependencies are loaded only once per page, even if multiple components appear on a page. Second, if components from different packages depend on the same JavaScript or CSS library, htmltools can detect and resolve conflicts and load only the most recent version of the same dependency.

Here’s an example from the applause package. This package wraps applause-button, a zero-configuration button for adding applause/claps/kudos to web pages and blog posts. It was also created to demonstrate how to package a web component in an R package using htmltools. For a full walk through of the package development process, see the dev log in the package README.

html_dependency_applause <- function() {
  htmltools::htmlDependency(
    name = "applause-button",
    version = "3.3.2",
    package = "applause",
    src = c(
      file = "applause-button",
      href = "https://unpkg.com/applause-button@3.3.2/dist"
    ),
    script = "applause-button.js",
    stylesheet = "applause-button.css"
  )
}

The HTML dependency for applause-button is provided in the html_dependency_applause() function. htmltools tracks all of the web dependencies being loaded into a document, and conflicts are determined by the name of the dependency where the highest version of a dependency will be loaded. For this reason, it’s important for package authors to use the package name as known on npm or GitHub and to ensure that the version is up to date.

Inside the R package source, the applause button dependencies are stored in inst/applause-button.

applause
└── inst
    └── applause-button
          ├── applause-button.js
          └── applause-button.css

The packagesrc, and script or stylesheet arguments work together to locate the dependency’s resources: htmlDependency() finds the package‘s installation directory (i.e. inst/), then finds the directory specified by src, where the script (.js) and/or stylesheet (.css) files are located. The src argument can be a named vector or a single character of the directory in your package’s inst folder. If src is named, the file element indicates the directory in the inst folder, and the href element indicates the URL to the containing folder on a remote server, like a CDN.

To ship dependencies in your package, copy the dependencies into a sub-directory of inst in your package (but not inst/src or inst/lib, these are reserved directory names1). As long as the dependencies are a reasonable size2, it’s best to include the dependencies in your R package so that an internet connection isn’t strictly required. Users who want to explicitly use the version hosted at a CDN can use shiny::createWebDependency().

Finally, it’s important that the HTML dependency be provided by a function and not stored as a variable in your package namespace. This allows htmltools to correctly locate the dependency’s files once the package is installed on a user’s computer. By convention, the function providing the dependency object is typically prefixed with html_dependency_.

Using an HTML dependency

Functions that provide HTML dependencies like html_dependency_applause() aren’t typically called by package users. Instead, package authors provide UI functions that construct the HTML tags required for the component, and the HTML dependency is attached to this, generally by including the UI and the dependency together in an htmltools::tagList().

applause_button <- function(...) {
  htmltools::tagList(
    applause_button_html(...),
    html_dependency_applause()
  )
}

Note that package authors can and should attach HTML dependencies to any tags produced by package functions that require the web dependencies shipped by the package. This way, users don’t need to worry about having to manually attach dependencies and htmltools will ensure that the web dependency files are added only once to the output. This way, for instance, to include a button, using the applause package an user only needs to type in e.g. their Hugo blog post3 or Shiny app:

applause::button()

Some web dependencies only need to be included in the output document and don’t require any HTML tags. In these cases, the dependency can appear alone in the htmltools::tagList(), as in this example from xaringanExtra::use_webcam(). The names of these types of functions commonly include the use_ prefix.

use_webcam <- function(width = 200, height = 200, margin = "1em") {
htmltools::tagList(
    html_dependency_webcam(width, height)
  )
}

JS and package robustness

How do you test JS code for your package, and how do you test your package that helps managing JS dependencies? We’ll simply offer some food for thought here. If you bundle or help bundling an existing JS library, be careful to choose dependencies as you would with R packages. Check the reputation and health of that library (is it tested?). If you are packaging your own JS code, also make sure you use best practice for JS development. 😉 Lastly, if you want to check how using your package works in a Shiny app, e.g. how does that applause button turn out, you might find interesting ideas in the book “Engineering Production-Grade Shiny Apps” by Colin Fay, Sébastien Rochette, Vincent Guyader and Cervan Girard, in particular the quote “instead of deliberately clicking on the application interface, you let a program do it for you”.

Learning and showing JavaScript from R

Now, what if you want to learn JavaScript? Besides the resources that one would recommend to any JS learner, there are interesting ones just for you as R user!

Learning materials

The resources for learning we found are mostly related to Shiny, but might be relevant anyway.

Literate JavaScript programming

As an R user, you might really appreciate literate R programming. You’re lucky, you can actually use JavaScript in R Markdown.

At a basic level, knitr includes a JavaScript chunk engine that writes the code in JavaScript chunks marked with ```{js} into a <script> tag in the HTML document. The JS code is then rendered in the browser when the reader opens the output document!

Now, what about executing JS code at compile time i.e. when knitting? For that the experimental bubble package provides a knitr engines that uses Node to run JavaScript chunks and insert the results in the rendered output.

The js4shiny package blends of the above approaches in html_document_js(), an R Markdown output for literate JavaScript programming. In this case, JavaScript chunks are run in the reader’s browser and console outputs and results are written into output chunks in the page, mimicking R Markdown’s R chunks.

Different problem, using JS libraries in Rmd documents

More as a side-note let us mention the htmlwidgets package for adding elements such as leaflet maps to your HTML documents and Shiny apps.

Playground

When learning a new language, using a playground is great. Did you know that the js4shiny package provides a JS playground you can use from RStudio? Less new things at once if you already use RStudio, so more confidence for learning!

And if you’d rather stick to the command line, bubble can launch a Node terminal where you can interactively run JavaScript, just like the R console.

R from JavaScript?

Before we jump to the conclusion, let us mention a few ways to go the other way round, calling R from JavaScript…

Shiny, “an R package that makes it easy to build interactive web apps straight from R.”, and the golemverse, a set of packages for developing Shiny apps as packages

OpenCPU is “An API for Embedded Scientific Computing” that can allow you to use JS and R together.

If you use the plumber R package to make a web API out of R code, you can then interact with that API from e.g. Node.

Colin Fay wrote an experimental Node package for calling R.

Conclusion

In this post we went over some resources useful to R package developers looking to use JavaScript code in the backend or docs of their packages, or to help others use JavaScript dependencies. Do not hesitate to share more links or experience in the comments below!

Thank you to our Speakers and Participants – COVID-19 Data Forum II

By Blog

The second COVID-19 Data Forum, co-sponsored by the Stanford Data Science Institute and the R Consortium, was held August 13, 2020. This series of forums brings together experts working to collect and curate data needed to drive scientific research and formulate effective public health responses to the pandemic.

The forum utilized Zoom as the video platform and allowed keynote speakers to present, as well as interact during a Q&A session.

The moderator was Sherri Rose, an associate professor at Stanford University in the Center for Health Policy and Center for Primary Care and Outcomes Research and Co-Director of the Health Policy Data Science Lab. 

Speakers covered topics such as current issues facing researchers during the COVID-19 pandemic such as data sharing or research duplication, how phenotype impacts severity of cases, and data inequality for under-serviced communities. Speakers also answered questions from the moderator and the chat about their work and ways individuals can get involved at all R literacy levels.

Speakers

See the COVID-19 Data Forum site to learn more about future Data Forum series virtual events!

We’ll be at R/Pharma – Oct 12-15, 2020

By Blog, Events

This fall, the R Consortium’s support for advancing data science in medicine continues with the third of three exceptional events, pulling together experts in their fields, including the Covid-19 Data Forum, R/Medicine, and R/Pharma.

What is R/Pharma?

R/Pharma is an ISC working group under the R Consortium. The entire event is a community-lead effort and 100% volunteer run. R/Pharma is vendor neutral and very much an academic conference. Harvard has been very helpful in hosting the event.

To find out more and how you can participate,

https://rinpharma.com/

Join us at R/Medicine – Aug 27-29, 2020

By Blog, Events

August 27-29, 5:30am PDT / 8:30am EDT / 2:30pm CEST – Register now!

Brought to you by the Children’s Hospital of Philadelphia, Yale School of Public Health, and the R Consortium, the R/Medicine conference encourages the adoption of statistical modeling and reproducible data processing in clinical practice.

R is the gold standard in reproducible research in academia and industry and has powerful capabilities to create highly-customizable interactive analytic dashboards, as well as predictive models that employ machine learning, deep learning, and artificial intelligence. 

Presentations will showcase how the R ecosystem is currently leveraged in medical applications including clinical trial design and analysis, personalized medicine, the development of machine learning models using  laboratory and patient record data, and reproducible research. 

Registration and more info: https://events.linuxfoundation.org/r-medicine/

Sponsors

Beyond Case Counts: Making COVID-19 Clinical Data Available and Useful – Aug 13, 2020

By Blog, Events

Thurs, August 13th, 9am PDT/12pm EDT/18:00 CEST – Register now!

Hosted by the COVID-19 Data Forum/Stanford Data Science Initiative/R Consortium

COVID-19 is the first pandemic to occur in the age of open data. Public health agencies around the world are releasing case counts to the public, and scientists are providing analyses and forecasts in real-time. However, the content of this data has so far been limited to simple metrics like cases, deaths, and hospitalizations at coarse geographic and demographic scales. To drive the next phase of COVID-19, scientists need access to higher-dimensional patient-level data, so we can understand how the virus causes disease, why are some more at risk than others, when and how is transmission occurring, what therapeutics are more likely to work, and what healthcare resources are being used. But sharing such data brings up tremendous challenges in terms of patient privacy and data standardization. The COVID-19 Data Forum, a collaboration between Stanford University and the R Consortium, is hosting the event “Beyond case counts: Making COVID-19 clinical data available and useful” to push the conversation forward on these issues. The event will include talks by representatives from international collaborative teams who are working to collect and share detailed clinical and biological data from individuals with COVID-19. The event will be open to the public, and is part of a continuing series focusing on data-related aspects of the scientific response to the pandemic.


Speakers include:

  • Jenna Reps, Observational Health Data Sciences & Informatics (OHDSI) Consortium /Janssen R&D
  • Andrea Ganna, COVID-19 Host Genetics Initiative/Harvard Medical School/Finland Institute for Molecular Medicine
  • Ken Massey, EndPandemic National Data Consortium/Saama Technologies
  • Ryan Tibshirani, DELPHI epidemic forecasting group/Dept of Statistics, Carnegie Mellon University

Registration and more info: https://covid19-data-forum.org

Latin American Communities and Organizations: useR!2020 Video

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This useR!2020 session and video was organized by Laura Acion, Yanina Bellini Saibene, Paola Corrales, and Paloma Rojas Saunero. Leonardo Collado Torres coordinated the blog post submission.

On June 19th, 2020, we filmed a video for useR!2020 showcasing the communities and organizations we are involved in that are for Latin Americans or have Latin American participants. In this blog post, we wanted to highlight these initiatives and remind everyone that we are more than happy to help you launch similar initiatives in your local communities.

  • LatinR: LatinR is a trilingual international conference on the use of R in research and development across Latin America. Since launching in 2018, our annual meetings have been a starting point for new packages, local user groups, reading clubs, R-Ladies chapters, translations, and other initiatives in the region.
  • ConectaR: ConectaR 2019 took place during January 24-26, 2019 at the University of Costa Rica, in San José, Costa Rica. It was the first event in Central America endorsed by The R Foundation, and it was held completely in Spanish. You can find more information here.
  • satRday: SatRday is a conference about R and its applications, that happens all over the world, and it is organized by the local community. Two satRdays events happened in Latin America: in Santiago – Chile and São Paulo – Brazil. If you want to organize a satRday anywhere in Latin America, please get it touch so we can help each other!
  • R-Ladies: R-Ladies is a global organisation that promotes gender diversity in the R community. It has 123 active chapters in 51 countries around the world, of which 49 are found across 10 Latin American countries. Some Latin Americans are part of the R-Ladies Global Team, including its leadership. COVID-19 has not stopped us, instead, we have migrated online and fostered alliances among different chapters. All in an effort to give gender minorities in the R community the opportunity to learn R in a safe and supportive environment. Join us!
  • rOpenSci: R for open science, rOpenSci, provides free technical review of R packages to improve the quality of open source software in order to maximize readability, usability, usefulness, and minimize redundancy. Their peer-review process will soon be translated to Spanish and you can get involved!
  • CDSB: the Community of Bioinformatics Software Developers (CDSB in Spanish) was born in 2018 with the goal of helping Latin American R users become R & Bioconductor developers and increase the representation of Latinx in these communities. For more information about CDSB check bit.ly/cdsbpost2020.
  • RUGs: there are several R User Groups in Latin America, some of which are officially sponsored by the R Consortium. We believe that creating a welcoming space is crucial for keeping the ideas flowing, which allows for meaningful networking and, consequently, the development of new projects. We can help you start your own group!
  • R4DS in Spanish + datos package: the resources to learn R in English are many, awesome, online, and free. But in Latin America few people can afford to learn English, and the resources in Spanish are few. To help solve this problem, we community-translated to Spanish the “R for Data Science” book and developed a package with the translation of all the datasets used in it: datos. The workflow to contribute to the package was designed to engage first-time contributors, and is now guiding the development of a new version in Portuguese that will be released in the next few months.
  • #DatosDeMiércoles + #30díasdegráficos: The @R4DS_es Twitter account was created as a way to share projects like the R4DS translation and to developed initiatives to foster the Spanish-speaking R community,  like #datosdemieRcoles, the Latin American cousin of #TidyTuesday. The idea is not only to use datasets that are in Spanish, but also datasets that are relevant for our Region. This initiative has been complemented with the 30 days plot challenge #30díasdegráficos. If you want to participate proposing a dataset for #datodemiéRcoles, please visit our github repo.
  • The Carpentries: The Carpentries builds global capacity in essential data and computational skills for conducting efficient, open, and reproducible research. Building a sustainable and active community in Latin America includes several initiatives: lesson translations, instructor training, workshop coordination, and fundraising. Get in touch with us through the mailing list and the carpentries-es channel at the Carpentries Slack workspace.
  • ReproHack: ReproHack is a growing community for researchers that are fighting the reproducibility crisis by sharing their experiences across disciplines. It is focused on organizing hackathons where participants attempt to reproduce published research from a list of proposed papers with public code and data. We are planning the first ReproHack in Spanish for October 2020 and you can get in touch with us through Twitter.
  • AI Inclusive: AI Inclusive is an organization that promotes diversity in the AI Community. We want to bring awareness around Artificial Intelligence issues and empower the community so they can enter in the AI field, a field that is not diverse at all. In December 2019, we had our launch events in Rio de Janeiro, Brazil and San Francisco, California. Follow us and join us!
  • Data Latam: in May 2016 we started with the first Data Latam podcast, aimed at offering an easy entry point, in Spanish, to those interested in data science. We always ask our interviewees: “How did you get where you are?”, and the diversity of stories has been enormous. Today Data Latam is a Latin American community of professionals and academics, who apply data science in their day to day work and we invite you to participate!

What happens in the R Community doesn’t stay in the R Community. All the good practices of inclusive and diverse communities learned in several of the initiatives presented before, generate strong work teams within and beyond the R community. There is still a lot to be done, but what we’ve already achieved is very encouraging and provides a solid foundation for the future.

These initiatives are sustained by many people making a great, mostly volunteer, effort behind the scenes. Some of the challenges that the communities face are translated into multiple positives, sustained, and a lot of invisible hard work. Some of them are: finding international funding due to limited local options, translating content, joining forces across organizations, organizing regional conferences, and becoming active developers of the technology.

The Latin American R community is growing fast and so does the responsibility to make this growth solid and safe. Some of the future work that we, as community builders, look forward to fulfilling are: consolidating regional conferences with support of international sponsors; acquiring funding to sustain translations; amplifying the voices of regional minorities; importing educational material and work opportunities; connecting expats with their local communities; helping other groups such as RUGs and RLadies; connecting with other initiatives such as R-Forwards, Africa-R, MiR, among others; Increase our and other minorities representation in the R Core Team, the R Foundation, and the R Consortium.

Thank you! Please watch our useR! 2020 video on YouTube.