R Community on R Views

Data Challenges for R Users

Thu, 10 Mar 2022 00:00:00 +0000

Today, I want to write about “data challenges”, where participants partake in a series of prompts designed for a variety of skill sets and levels. These challenges are opportunities to practice programming skills, work on algorithmic efficiency, or learn something new. Often, participants share the code for their submissions, teaching others their processes and techniques.

The challenges test the participants’ tenacity with time-bound but flexible prompts. TidyTuesday was originally borne out of the R4DS Online Learning Community and the R for Data Science textbook. RStudio’s Tom Mock shares a dataset every Tuesday. The challenge is to explore the dataset and create a chart within seven days. Participants decide what variables to focus on and how to best visualize them.

Using a recent TidyTuesday dataset, Moriah Taylor visualizes alternative fueling stations in the U.S. The stacked tilted maps communicate relationships, density, and location while demonstrating beautiful design.

Moriah’s code introduced me to the layer package, which tilts maps and turns them into ggplot2 objects:

#plot tilted maps
plot <- plot_tiltedmaps(maps_list,
                layer = c("value", "value", "value", "value"),
                palette = c("magma", "magma", "magma", "magma"))

Data challenges can also be language-agnostic. Genuary encourages participants to create works of generative art with the programming language of their choice. R users apply their coding skills and tools in imaginative ways. On Day 6 of genuary, Ryan Timpe “destroys the square”:

Taking a look at the code, we learn how to plot a square in R with the tidyr and dplyr packages:

library(tidyverse)
set.seed(12238)

#The square

square_size = 400

the_square <- expand_grid(
  x=1:square_size,
  y=1:square_size
) %>%
  mutate(
    color = "#87ceeb"
  )

plot(the_square$x, the_square$y)

We can also see how Ryan inserts randomness to ‘destroy’ the square through the strategic use of functions like sample() and runif():

circ_origin_size = square_size * runif(1, 2/4, 3/4)

Interested in participating in a data challenge? The 30 Day Chart Challenge is coming up. The organizers offer different chart types for you to visualize in April. Showcase your R skills, test your perseverance, and use the hashtag #30DayChartChallenge to share your work with other participants.

We started to compile a list of other data challenges to join. Please leave a comment if you are aware of others.

Timeframe	Name	URL
January	genuary	http://genuary.art/
April	30 Day Chart Challenge	https://twitter.com/30DayChartChall
October	Hacktoberfest	https://hacktoberfest.digitalocean.com/
November	30 Day Map Challenge	https://github.com/tjukanovt/30DayMapChallenge
December	Advent of Code	https://adventofcode.com/
Monday	Makeover Monday	https://www.makeovermonday.co.uk/
Tuesday	Tidy Tuesday	https://github.com/rfordatascience/tidytuesday
Thursday, Summer Months	Recreation Thursday	https://github.com/sharlagelfand/RecreationThursday
Sunday	SportsViz Sundays	https://data.world/sportsvizsunday
Monthly	SWD	https://community.storytellingwithdata.com/
Ongoing	100 Days of Code	https://www.100daysofcode.com/

Thank you to Moriah Taylor and Ryan Timpe for sharing their visualizations.

Interview with Oscar Baruffa, Creator of the Big Book of R

Tue, 04 Jan 2022 00:00:00 +0000

Welcome to the new year! If you’re itching to improve your R skills in 2022, we have the resource for you.

We’re excited to share the Big Book of R. “Your last-ever bookmark”, the Big Book of R is an impressive collection of R-related books from a variety of subjects. Creator Oscar Baruffa first published the book in August 2020. Since then, it has grown from a list of 80 to over 200, garnering 73,000 unique visitors and 195,000 pageviews from readers around the globe.

The Book is organized by different subjects that range from introduction to R programming to big data to archeology. Its organization and search functionality make it easy for newcomers to find books related to their topic of interest.

The Big Book of R is a wonderful example of collaboration in the R Community. Oscar wrote the book using the bookdown package. Contributors can file an issue or create a pull request on Github. And of course, the book would not be possible without the authors who have written books to guide others on their R journey.

While you are preparing for 2022, we encourage you to cozy up to one of the great books in the Big Book of R.

Check out the Big Book of R
Contribute to the Github Repository
Follow Oscar on Twitter
Subscribe to the newsletter

Interview with Oscar

Hello! Could you tell us a bit about yourself, please?

I’m a South African now living in the Netherlands, working as a Data Specialist in an international development non-profit focused on sustainable trade systems. My role is basically that of a senior analytics manager and I’m the first one, so I get to have all the fun directing the development of our data pipeline! No really, it’s a lot of fun :).

I studied Mechanical Engineering in my undergraduate and masters degrees and have been dabbling in tech-related side projects and hobbies for many years.

How did you get started with the R Community?

I think it was sometime in late 2018 when I was busy learning a bit of Python and Jesse Mostipak started popping up on my Twitter feed. She made R sound quite fun so I thought I’d give it a try. After I got to the exercise in R for Data Science by Hadley Wickham and Garrett Grolemund where you’re introduced to faceting a plot, my mind was blown and I was hooked.

I then started participating in the #TidyTuesday challenge and recorded some screencasts on my “Other People’s RStats” YouTube channel. I took a bit of a leap of faith submitting a lightning talk for satRday Johannesburg in April 2019, which was being organised by Andrew Collier and Megan Beckett. I was selected, which was a welcome surprise for my little topic. They were so helpful when I was trying to figure out the pull request flow for submitting my presentation. I also attended my first-ever R workshop the day before (which Andrew presented), which was the first time I’d ever sat in a room with other people who love R. It was awesome!

While doing all of this, I was also following more and more people on Twitter who were tweeting about R, collecting bookmarks of packages, tutorials, and, of course, books. I was having a lot of fun. By early 2020, I wrote my first book with Veerle van Son called Twitter for R Programmers as a way to introduce others to the R community on the social platform. So by that point, you could say I’d become heavily invested in the community :).

What inspired you to start the Big Book of R?

I had been diligently collecting bookmarks of books as I was finding them. After about 2 years of doing so, I had an inkling that I must have a large collection. One day, I counted about 80. I compared it to other lists of books that I’d seen published and I had way more, so I figured this might be quite unique to have so many.

Having had written my own short book, I also appreciated how much effort it took and I felt there must be better usability for readers and discoverability for authors if books were all listed in one place and grouped by topic (I invented a library - haha!). I also hoped (still do) that it might encourage more writing too. I put all the books together, spent some time categorising them, and in August 2020 published it. It made quite a splash!

Tell us about the design choices you made to make the book inclusive and open to the community.

I opted to use bookdown and git as I already had a bit of experience using them for Twitter for R Programmers. It felt very “meta” and fitting to use bookdown, which had in turn been used for almost all of the books in the collection. I was hoping this format would allow others to submit books as well — and they’ve generously done so. I kept the collection as a plain text format in the hope that it would slightly lower the barrier to people submitting books, but in the end, many people just tag me in a tweet which is welcome :).

What has surprised you the most about this project?

I knew people would like this but I didn’t expect how much it would be appreciated. Every now and then, I get a message of thanks for creating and maintaining it that really makes me feel warm and fuzzy inside. I hope people also reach out to the authors and do the same. Their effort in writing these books is immense and a little bit of appreciation will make their day — guaranteed!

What also surprises me is how there’s very consistent spikes in views and a steady growth in daily visitors whenever someone else shares the Big Book of R. If you’re interested to see the analytics, I’ve made them publicly accessible.

What’s in store for the Big Book of R in 2022?

I’m sure the collection will keep growing :).

I’ve just remodeled how the content is generated. Instead of capturing the book entries directly into markdown, it’s now generated by reading the data from a Google sheet. This new setup gives me the flexibility to do things like alphabetize the books more easily, add additional fields and tags more easily, set up Twitter/LinkedIn bots to automatically post about books in the collection, set up scripts to detect book updates, etc. Basically, I want to open up more possibilities for further automation and discoverability. If anyone is building anything using this data, I’d love to hear about it.

If anyone has ideas of how to improve, please get in touch with me or submit an issue in the repo.

Do you have any ongoing or upcoming projects you’d like the R Community to know about?

If things work out, there’s a chance I’ll be a technical reviewer on two R books being worked on in 2022, so that’ll be a new experience for me that I’m looking forward to. I’m going to keep writing useful articles about R, data and data careers over on my blog (and releasing fun R-related products here and there). The best way to be notified of those is to sign up to my newsletter. I’m also looking to write some more on the topic of Project Management to build upon my other bit of work that I’m really proud of, Project Management Fundamentals for Data Analysts.

Closing question: what is your favorite R package right now?

I think I’d give that accolade to {dplyr}! I’m pretty sure that filter() and group_by() are my most-used functions. Nothing gives me greater pleasure than a good anti_join().

We at RStudio would like to thank Oscar for his contribution to RViews and the creation of a great resource for the R Community. Happy reading in 2022!

R Views Call for Documentation

Wed, 04 Aug 2021 00:00:00 +0000

We at R Views believe that the R ecosystem, centered around CRAN and Bioconductor, is the world’s largest open repository of statistical knowledge. R packages provide implementations and examples for a tremendous number of statistical methods, procedures, and algorithms. Yet, the virtual library of the R ecosystem is far from being complete. There is plenty of room for examples that explore some little travelled path of computational statistics or illuminate a familiar field with clarity. We invite you to contribute to expanding the knowledge available in the R ecosystem through blogging.

Documentation for R packages begins with the package pdf file which provides a detailed description for each individual function, indicates relationships among functions and often includes references to the statistical and scientific literature. Going down an organizational level you can view the source code for each function. Moving up a level, README files and package vignettes provide a coherent overview of the capabilities of the package as a whole and often include example and elaborate use cases.

While the burden to explain what R packages do falls mainly on package authors, all of us data scientists, statisticians, researchers, students and “ordinary” R users can add to the knowledge encoded in R by elaborating on the capabilities of R functions and packages, developing additional examples and contributing statistical analyses in areas where we may have developed some expertise.

^{xkcd 1805 Unpublished Discoveries}

We are not talking about Nobel Laureate work here. But nearly everyone who regularly uses R has some gem stashed away somewhere. We are looking to mine those gems and organize them in a way that can be shared with the R Community.

We are asking you to please dig up your gem and organize it into a blog post. Tell us what your gem does, how it works, and why it is of some value. Some of the most popular R Views posts introduce technical topics by providing R based tutorials. Others show how to efficiently accomplish everyday tasks or present an elegant calculation or visualization. Think in terms of a well explained example, or if you are really ambitious, you could write that package vignette that you wish existed. Look below for some guidance on more elaborate ideas.

Call for Posts

Please submit you posts using the following form: rstd.io/rviews-2021. All submissions will appear on the Call for Docs page on the RStudio Community Site, and you will need an RStudio Community profile.

The deadline for submission is Friday, September 23, 2021. We planning awards of a sort to the best entries and would like to announce these by November 1, 2021.

Awards

The award categories and the associated prizes are as follows:

Honorable Mentions:
- One year free RStudio Cloud Premium
- A bunch of hex stickers of RStudio packages
Runners Up:
- All prizes listed above, plus
- Some number of RStudio t-shirts, books, and mugs (worth up to $200)
Grand Prizes:
- All prizes listed above, plus
- Special & persistent recognition by RStudio in the form of a winners page, and a badge that will be publicly visible on your RStudio Community profile
- A selected group of submissions will be invited to appear on R Views

^{* Please note that we may not be able to send t-shirts, books, or other larger items to non-US addresses.}
^{* Please note that all articles on R Views will go through an editorial review before being published there.}
^{* With the author’s permission, some posts may appear on R Views before the awards announcements.}.

Announcement

The names and work of all winners will be highlighted in an announcement on R Views, and we will announce them on RStudio’s social platforms, including RStudio Community (unless the winner prefers not to be mentioned).

Need inspiration?

Edgar Ruiz Gentle Introduction to tidymodels is a good example of a homemade package vignette.
Jonathan Regenstein’s series on Reproducible Finance with R presents several masterful examples of Financial use cases.

Guidance

Posts should be well-written, contain enough code to support the main argument and have a few plots and/or tables.
Novelty - We are looking for original content. That is, your article should not have appeared on blogging platforms, other than your personal website or blog.
Contribution - Your article should provide novel insight and utility. For example, an exploration of a package or use case that is currently poorly documented will be better received than one which already has excellent documentation and vignettes either on CRAN or in the R community.
Reproducible - Your submission will need to link to a repo which includes an .Rmd file that reproduces all code, images, and other output in the submitted article. If there is code that requires special resources, such as access to a private database, it is fine to simply refer to these in your repo.
Please confine your posts to less than 1,000 words, excluding code and image captions. Your article should be self-contained, but may refer to additional content on your repo or personal website.

COVID-19 Data Forum: Data Journalism

Tue, 06 Apr 2021 00:00:00 +0000

The COVID-19 Data Forum, a joint project of the Stanford Data Science Institute and the R Consortium, is an ongoing series of multidisciplinary webinars where topic experts discuss data-related aspects of the scientific response to the pandemic. The most recent event, held on March 18, 2021, explored the role of data journalism in the pandemic. This was a bit of a departure from previous forum events¹ because it focused on issues relating to using and interpreting COVID-19 data, and not on the particular kinds of COVID-19 related data that are available.

I think you will find the webinar video worth watching. If you are a statistician or epidemiologist working on COVID-19, you may find the data journalists’ accounts of difficulties they faced working with COVID data and statistical models instructive. But, even if you are not directly working on COVID, you may find that listening to the journalists fills in some gaps between what you know about statistics and data visualizations and what you see in the news.

The data journalism event was moderated by Dr. Irena Hwang, a data reporter at ProPublica. Speakers included Dr. Mark Hansen, David and Helen Gurley Brown Professor of Journalism and Innovation at Columbia University; Ana Carolina Moreno, a senior data journalist at TV Globo in São Paulo, Brazil; and Meghan Hoyer, Director of Data Reporting at the Washington Post.

The video of the data journalism event is available here. The following short time map and the times referenced in my comments below should be helpful for browsing the ninety minute event.

2:37 Irena Hwang introduces Mark Hansen
3:50 Start of Mark’s talk
19:30 Irena introduces Ana Carolina Moreno (Carol)
21:10 Start of Carol’s talk
39:20 Irena introduces Meghan Hoyer
40:00 Start of Meghan’s talk
1:01:40 Start of discussion

Mark Hansen

In his talk, Mark offers an overview of the profession of data journalism that provides some historical context and emphasizes the hybrid nature of the practice which blends a hard nose detective’s drive to uncover facts with the empathy to tell stories “about who we are and how we live”.

7:00 Mark introduces Joseph Pulitzer’s 1904 paper The College of Journalism in which Pulitzer includes Statistics as a subject journalists should study. On page 673, Pulitzer writes:

You want statistics to tell you the truth. You can find truth there if you know how to get at it, and romance, human interest, humor and fascinating revelations as well.

10:19 Mark describes a piece, An ode to reporter’s notebooks, published by Philip Eil in the Columbia Journalism Review that offers a personal account of reporting: Eil writes:

To report is to be alert and alive at a particular time and place.

11:00 Mark remarks:

when we’re thinking about bringing computation to journalism we are taking that basic curiosity that we are cultivating in our students minds … and adding computational lines of inquiry to that habit of mind, that questioning why things look the way they do…

12:08 Mark calls attention to the report by Charles Berret and Cheryl Phillips Teaching Data And Computational Journalism and describes some recent activities of the Brown Institute at the Columbia School of Journalism.

Ana Carolina Moreno

22:32 Carol introduces Brazil’s universal healthcare system and shows a schematic of the available official and unofficial COVID-19 data sources.

26:00 Carol notes that a platform originally built to track SARS data was adapted to track COVID.

27:38 Carol explains that, in practice, there are many obstacles making it difficult to obtain the data necessary to understand how the pandemic is developing. Some of these are called out in the following slide:

30:37 Carol remarks that hospital data seems to be the most reliable.

31:06 Carol describes how the government changed its policy for reporting deaths. The new scheme of only reporting deaths that have been confirmed in the past twenty-four hours vastly undercounts the current death rate.

31:57 In an effort to obtain more reliable data, a consortium of competing journalists at local news organizations began cooperating by sharing information directly obtained from hospitals every day.

32:35 Carol provides a view of day-to-day journalism at the local news organizations and describes how the data journalist scrape data on a daily basis to populate dashboards showing rolling averages and daily indicators. By focusing on the more reliable hospitalization data journalists are doing their best to track the spread of the pandemic an expose inequities in the health care system.

Meghan Hoyer

40:07 Meghan begins her walk through of what last year was like for data journalists who were trying to tell the story of the pandemic in real time as it was happening.

41:22 Meghan recounts her experiences trying to make sense of COVID-19 models and expresses the frustration she and other data journalists felt with the multitude of contradictory predictive models.

44:03 In a memorable quote, Meghan remarks:

Models were inherently problematic and yet they were being forced upon us by society…

Consequently journalists at the AP agreed and decided that they were not going to base stories on models.

In absence of reliable case data, and wanting nothing to do with the models, Meghan explains that data journalists turned to whatever data they could get their hands on to quantify the story of the pandemic.

46:00 Meghan recounts how journalists used garbage pickup data as a proxy for population density to estimate where people were living in NYC and correlate it with case data.

47:30 Journalists struggled to find data to verify the anecdotal stories they were hearing about the the disparities in who was being affected by virus. Finding that one quarter to one third of the COVID case data was missing information on race, data journalists “hand collected” data by looking city by city to find the missing data.

50:30 Meghan recounts how they turned to age adjusted data to determine the impact of the virus on communities of color.

52:10 Data journalists find that excess deaths is a reliable metric for determining the impact of what is happening on the ground.

54:18 Journalists developed a survey which was returned by seven hundred schools to investigate how going back to school might be affecting students. Among their findings was that districts serving students of color were more likely to start online.

56:35 Meghan discusses the COVID-19 Tracking Project and the effort to sort out the impact of test positivity rates. She reports that because not all states measure the number of people who test in the same way, correctly comparing test positivity rates among states remains an unsolved problem.

58:33 Meghan shares the need to “flip the numbers” to help people understand the meaning of statistics stated in terms of very large numbers. For example, saying that “Since January of last year at least 1 in 15 people who live in Alexandria, Virginia have been infected by the virus” is easier for people to understand than something like: “On March 17th there were 14 cases per 100,000 in Alexandria”.

59:53 Vaccination tracking is another problematic data reporting area. Not only are vaccinations reported differently from state-to-state, but the data that is reported is changing from day-to-day. The CDC is apparently still adding new fields to the vaccination data sets.

The Q & A Discussion

1:02 The question and answer discussion begins.

1:02:56 Mark talks about how visualizations evolved over the course of the pandemic.

1:06:08 Carol and then Meghan talk how the lessons the pandemic taught data journalists about competition and collaboration.

1:10:04 Meghan describes how during the pandemic data journalists became advocates for public data.

1:11:21 Carol answers a question about the opportunities for data journalism in Brazil.

1:15:50 Answers a question of how academia is supporting data journalism during the pandemic and mentions an effort to have statistical and scientific experts collaborate with data journalists.

1:20:19 Meghan responds to a question about technical and social challenges for data journalists during the pandemic.

1:23:10 Carol talks about the difference between reporting online news and television news.

1:26:01 Mark answers a question about communicating emotional impact in COVID reporting and ends with emphasizing the importance of communicating honestly about what we do, and do not know.

¹The first forum on May 14, 2020 focused on the data needs and challenges of modeling and controlling the spread of COVID-19, The second forum on August 13, 2020 explored what was being done to make clinical data available and useful. The third forum on December 10, 2020 discussed the role of mobility data.

Party with R: How the Community Enabled Us to Write a Book

Mon, 03 Aug 2020 00:00:00 +0000

Isabella C. Velásquez, MS, is a data analyst committed to nonprofit work with the aim of reducing racial and socioeconomic inequities.

“It is like a party all the time; nobody has to worry about giving one or being invited; it is going on every day in the street and you can go down or be part of it from your window.” - Eleanor Clark. Without the R community, Data Science in Education Using R would never have happened. Most evidently, we wouldn’t have met each other without the strong R presence on Twitter that sparked a conversation about data use in education. More importantly though, are the aspects of the R community that inspired that initial discussion and enabled us to complete a book for a broad and complex field.

In the first chapter of our book, we invite data practitioners in education to the party but the R community invited us to the party first. Like any successful party, certain elements had to exist for us to join in and end up having an amazing time.

The Invitation 📩

As someone who started following the R community on Twitter after it was already well established and popular, I never felt the apprehension about having to ask how to get involved or interact with others. First, there are so many avenues. The R community offers many ways to let you in, whether it be replying to tweets, posting a question on community.rstudio.com, or sharing a blog post on R Weekly. Second, the R community welcomes users no matter their level. Whether it is a code snippet with a function you found cool, a blog post, or a personal side project, there are ways to engage that appeal to everybody. Members of the R community can interact how they want and as often as they want.

For us, it was exciting to meet on Twitter, talk about collaborating on an education data project, and then just get started on it. We felt welcome and encouraged to do so. Because people in the R community meet other users virtually and begin side projects all the time, we didn’t have to worry about whether something like this was possible: The invitation was already there.

An Open Door 🚪

In our first blog post, we described what it’s like to learn by seeing someone do the thing you want to learn. One of the best things about the R community is you constantly get to see this in action. The R community not only holds open principles but actually exhibits them whenever possible. Users post their code, projects, and drafts constantly. Just by scanning the #rstats hashtag, one can discover something new.

Because others were open, we knew that we wanted to be open as well. We wrote our book on Github, showing all the many changes it went through until its final completion. By having it freely available on a website, we hope that it opens the door to others who’d like to learn what we did and work on their own open project as well.

Food (for Thought) 🍕

The R community offers so many opportunities to get feedback, advice, and information from a wide variety of users. Early on in the book development, we had a lot of questions to nail down: who is the audience? “Data is” or “data are”? How do we describe “people who work in the education field and use data and want to get more effective at it”? Finding a common language was difficult, but we were able to do this by engaging others in the wider R community. We listened to the stories of many data scientists who work in education, then found common experiences we could describe in our writing.

As an example, we learned we weren’t the only ones challenged by learning a programming language while attending to full time jobs and personal lives. Knowing this, we made sure to discuss these challenges in our book and offer various ways of engaging with the material based on the reader’s needs.

Socializing 💬

Throughout the process of writing DSIEUR, we asked the R community several times for other types of feedback and suggestions. We also ran into some technical issues, especially when it came to preparing our manuscript with {bookdown} to meet our publisher’s specifications, and were able to get ideas on how to resolve them. We know that the R community is a safe and encouraging place to ask questions, and this enabled us to write a stronger book.

The Next Day ☀️

The community participation throughout the DSIEUR process helped us define our goals and get feedback. Another wonderful aspect is that the R community engaged us back. Writing this R Views series is an example: Someone reached out to us and wanted us to reflect on what we discovered and share it with all of you. This type of engagement reminds us of what an inclusive and encouraging place the R community is and helps us come up with new ways of making sure others see the invitation as well.

Thank you for reading! 🎉 We’ll be back with the fourth post on “One Writer, Five Authors” in about two weeks. Until then, we’d love to know how else the R paRty has encouraged your work, both personally and professionally. You can reach us on Twitter: Emily @ebovee09, Jesse @kierisi, Joshua @jrosenberg6432, Ryan @RyanEs, and me @ivelasq3.

Some 2020 R Conferences

Wed, 05 Feb 2020 00:00:00 +0000

rstudio::conf kicked off the 2020 season for R conferences last week with record attendance somewhere north of twenty-one hundred. Session topics ranged from business to science, marketing to medicine and attracted R users with very varied backgrounds including DevOps professionals, data scientists, journalists, physicians, statisticians, R package developers, Shiny developers and more. Although it is true that the San Francisco Bay Area is home to a large R Community, and that a great deal of planning and promotion went into making rstudio::conf a success, I don’t think that the enthusiasm and energy that permeated the conference was a local phenomenon. I expect 2020 to be a good year for R conferences worldwide. Here is my short, somewhat eclectic, and by no means complete list of upcoming 2020 R events.

While not an R specific conference, the Conference on Statistical Practice (Sacramento, February 20 - 22) coming up soon will have some interesting R content. I am particularly looking forward to the talk by Songtao Wang on Thinking Statistically in Social Science and Humanities.
The NICAR Confererence produced by the nonprofit Investigative Reporters and Editors Inc. (New Orleans, March 5 - 8) is a gathering of R and Python savvy journalists using R as an every day tool. Last year, I found the workshops and R training sessions to be outstanding.
The PAA Annual Meeting (Washington D.C., April 22 - 25) is an opportunity to meet demographers, economists public health professionals and sociologists using R.
The R Conference New York (May, 7 - 9), the re-branded NY R Conference of previous years, promises to be the major East Coast R event of the year with a diverse and talented roster of speakers.
The European R Users meeting, eRUM (May, 27 - 30) will be the first major European R conference of the season. Note that Sharon Machlis, Director of Data & Analytics at IDG Communications will be one of the keynote speakers. 2020 may be the year that journalism captures the attention of the R Community.
The Symposium on Data Science & Statistics, SDSS, (Pittsburgh, June 3 - 6) has become one of my favorite statistics conferences. Smaller and more manageable than the JSM, talks are sure to be infused with R content. Note that there will be a session on Data Journalism and Visualization.
Now in its twelfth year, R / Finance (Chicago, June 5 - 6) has set the bar for small, single-session, collegial, technical R conferences. Focusing on Financial applications with a serious dose of advanced time series applications, R / Finance provides the opportunity to interact with professionals who put their money on R.
Always the pivotal event of the R Community, useR! 2020 (St. Louis, July 7 -10) is sure to be a great event and a good time. The final program has not been published (Note that the Call for papers is still open.), but the tutorial sessions alone indicate that useR! 2020 be an outstanding educational opportunity.
The useR! 2020 European Hub Conference (Munich, July 7 - 10) will feature live talks as well as video streaming sessions from useR! 2020 in St. Louis. This innovative format promises to be an important event in its own right and a satisfying community experience.
The JSM (Philadelphia, August 1 - 6), the mother of all statistical conferences, is expecting more than 6,500 attendees from 52 countries. This is an event you have to train for, but with some preparation and a little planning the JSM can be an opportunity to interact with statisticians who depend on the deep statistical knowledge embedded in R.
The Bioconductor Conference, BioC 2020, the event where Software and Biology Connect (Boston, July 29 - 30) is the premier conference for R and Genomics. If you have an interest in learning about cutting edge statistical applications using the big data of modern Biology, this the conference to attend.
The Why R? 2020 Conference (Warsaw, August 27 - 30) is not only positioned to be the third significant European R conference of the year, the organizers have developed an ambitious and innovative strategy of supporting a number of satellite pre-meetings that stretch from Warsaw to Limerick and South Africa. I think this is a fantastic community initiative that represents a commitment to build the R Community in under served areas.

There is no website yet, but it is breaking news that R / Medicine 2020 will be held in Philadelphia from August 27th to 29th. In its third year, R / Medicine is establishing itself as the R conference for physicians seeking to advance clinical practice with R fueled data science.
The LatinR 2020 Conference (Montevideo, October 7 - 9) will be a major South American event. The Call for papers is open. Look here for previous programs.
The BiocAsia 2020 Conference (Beijing, October 17 - 18), the yearly Asian Bioconductor event, has confirmed that Robert Gentleman will be speaking.

Other conferences on my radar are:
* The R Conference Dublin (June)
* The CascadiaRConf (Eugene, OR)
* R / Pharma which will most likely be held at Harvard University in August.
* BioCEurope which will likely be held in December.

Please let me know what upcoming conferences I may have missed by adding them to the comments section of this post.