gt

Slides - https://liomys.mx/posit25.html posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

For much of my career, I have seen data science teams make the critical decision of deciding whether they are going to be an “R shop” or a “Python shop”. Doing both seemed impossible. I argue that this has changed drastically, as we have built out an effective multilingual data science team at Ketchbrook, thanks to polars/dplyr, gt/great-tables, ggplot2/plotnine, arrow, duckdb, Quarto, etc. I would like to provide a walk through of our journey to developing a multilingual data science team, lessons learned, and best practices. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Although R supports comprehensive analysis workflows, creating polished, production-ready PDFs directly from R remained a challenge for our pharma teams. With teams facing looming deadlines, our R enablement team swiftly created {docorator}—an open-source R package that transforms R-based tables and figures into production-level PDFs. By adorning results with “decorations” like headers, footers, and page numbers, {docorator} produces seamless, polished documents. Powered by Quarto, it also auto-sizes {gt} tables for user ease. Attendees will learn how {docorator} became the missing piece in GSK’s R workflows and learn how focusing on quick, simple solutions can have a lasting impact. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

• Workshop GitHub Repository: https://github.com/rich-iannone/great-tables-mini-workshop
• Great Tables https://posit-dev.github.io/great-tables/articles/intro.html
• {reactable-py} https://github.com/machow/reactable-py
• Save a gt table as a file https://gt.rstudio.com/reference/gtsave.html
• {gto} Insert gt tables into Word documents https://gsk-biostatistics.github.io/gto/
• GT.save https://posit-dev.github.io/great-tables/reference/GT.save.html
• define_units https://posit-dev.github.io/great-tables/reference/define_units.html#great_tables.define_units
• Posit Tables Contest 2024 winners: https://posit.co/blog/2024-table-contest-winners/

Editor’s note: During this workshop, several interruptions from an unwanted and disruptive intruder (commonly referred to as a “Zoom bomber”) occurred. We removed those instances from the recording, however that causes a few of the workshop sections to appear disjointed. We apologize for the inconvenience.

Workshop recorded as part of the 2024 R/Pharma Workshop Series

Michael Chow, Rich Iannone

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you!

We were recently joined by Rich Iannone and Michael Chow, software engineers at Posit, to chat about their experiences building GT and Great Tables, how they drive community engagement around their packages, and their career advice for package developers.

GT and Great Tables are R and Python packages for creating static tables in R and Python. They were created to fill a need for a good, maintained solution for generating tables with different output types from data frames. They’ve gained loads of popularity, and have an active community of users!

Resources mentioned in the video and zoom chat: Great Tables Blog → https://posit-dev.github.io/great-tables/blog/ 2024 Table Contest Winners → https://posit.co/blog/2024-table-contest-winners/ Contributing to Public Transit Data Analysis and Tooling → https://posit-dev.github.io/great-tables/blog/open-transit-tools/ Tables as Powerful Representational Tools → https://www.researchgate.net/publication/363345970_Tables_as_Powerful_Representational_Tools The MockUp - 10+ Guidelines for Better Tables in R → https://themockup.blog/posts/2021-01-13-10-guidelines-for-better-tables-in-r/ Show Me the Numbers by Stephen Few → https://analyticspress.com/smtn.php Excel spreadsheets to gt package called {forgts} → https://github.com/luisDVA/forgts What They Forgot to Teach You About R → https://rstats.wtf/ David Robinson Talk called “The unreasonable effectiveness of public work” → https://www.youtube.com/watch?v=th79W4rv67g Publication quality tables in 2024 → https://posit.co/blog/what-we-did-with-publication-quality-tables-in-2024/ Great Tables Design Philosophy → https://posit-dev.github.io/great-tables/blog/design-philosophy/ gtsummary-to-excel → https://www.pipinghotdata.com/posts/2024-07-26-gtsummary-to-excel/ happy git with R → https://happygitwithr.com/ freeCodeCamp - how to contribute to open source → https://github.com/freeCodeCamp/how-to-contribute-to-open-source/

If you didn’t join live, one great discussion you missed from the zoom chat was about how people manage their personal and work GitHub accounts, and whether to have one account for all their work or separate accounts for each employer. Let us know YOUR thoughts on this topic below!

► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu

Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co

Thanks for hanging out with us!

#pythoncontent

Michael Chow, Rich Iannone

Slides: https://github.com/rich-iannone/presentations/blob/main/2024-08-13-posit_conf_adequate_tables/gt_GT_talk_posit_conf.pdf GitHub Repo: https://github.com/rich-iannone/presentations/tree/main/2024-08-13-posit_conf_adequate_tables

Links mentioned: TidyTuesday: https://github.com/rfordatascience/tidytuesday Table Contest: https://posit.co/blog/announcing-the-2024-table-contest/ Posit Conference: https://posit.co/conference/ Monthly Workflow Demos: https://www.addevent.com/event/Eg16505674 gt package: https://gt.rstudio.com/ So Good They Can’t Ignore You book recommendation: https://www.goodreads.com/book/show/13525945-so-good-they-can-t-ignore-you Community Builder Quarto Site: https://pos.it/community-builder

► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu

Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software

To join future data science hangouts, add to your calendar here: https://pos.it/dsh

We’d love to have you join us in the conversation live!

Thanks for hanging out with us!

RStudio Team

Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.#

Talk Track: Elevating your reports. Session Code: TALK-1156

Rich Iannone

• Here’s the link to the video that I mention in the talk: https://youtu.be/QU8wSya-Y9E?si=zw59OSFPl1eJSY7M
• Part 1 of this two part series can be found at https://www.youtube.com/watch?v=jX4_Dnzhl0M

Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.#

Talk Track: Compelling design for apps and reports. Session Code: TALK-1105

• R Submissions Working Group: https://rconsortium.github.io/submissions-wg/
• R Tables eBook: https://rconsortium.github.io/rtrs-wg

Abstract: Within the life sciences industry, Shiny has enabled tremendous innovations to produce web interfaces as frontends to sophisticated analyses, dynamic visualizations, and automation of clinical reporting across drug development. While industry sponsors have widely adopted Shiny as part of their analytics and reporting toolset, a relatively unexplored frontier has been the inclusion of a Shiny application inside a clinical submission package to regulatory agencies such as the FDA. The R Consortium R Submissions Working Group has continued the positive momentum of previous submission pilots to achieve substantial progress in this domain. In this talk, we will share the development journey of the working group’s Pilot 2 successful submission of a Shiny application to the FDA, along with the progress on the use of novel technologies such as Linux containers and web assembly to bundle a Shiny application into a self-contained package, facilitating a smoother process of both transferring and executing the application. The R Consortium’s R Tables for Regulatory Submissions (RTRS) Working Group has released the first edition of (Tables in Clinical Trials with R)[ https//rconsortium.github.io/rtrs-wg/] as a free and openly accessible ebook. The book contributes to the development of a theory of displaying tabular information by identifying a small number of table archetypes that may be used to generate the most common tables employed in clinical submissions. Chapters in the book demonstrate how these tables may be rendered in different R Packages including flextable, gt, rtables (with and without tern), tables, tfrmt and tidytlg. All tables are generated from CDISC-compliant data. Comparing the code showcases the robustness of R for aggregating and displaying tabular information and illuminates the flexibility and design tradeoff of the various R packages. The talk will discuss the motivation for the book, present the idea of table archetypes, show some representative tables, and make the case for R as a superb language for analyzing clinical trial data. The RTRS working group expects Tables in Clinical Trials with R to become a primary resource of clinical programming teams.

Speaker Bios:

Eric Nantz is a director within the statistical innovation center at Eli Lilly and Company, creating analytical pipelines and capabilities of advanced statistical methodologies for clinical design used in multiple phases of development. Outside of his day job, Eric is passionate about connecting with and showcasing the brilliant R community in multiple ways. You may recognize his voice from the R-Podcast that he launched in 2012. Eric is also the creator of the Shiny Developer Series where he interviews authors of Shiny-related packages and practitioners developing applications, as well as sharing his own R and Shiny adventures via livestreams on his Twitch channel. In addition, Eric is a curator for the RWeekly project and co-host of the RWeekly Highlights podcast which accompanies every issue.

Alexandra Lauer is a Senior Principal Statistical Analyst at Merck KGaA, Darmstadt, Germany, with a background in mathematics. She specializes in bridging psychometrics and biostatistics, with a primary focus on Health-Related Quality of Life evaluations. Alex is an R enthusiast, co-leading the Merck-internal R User Group.

Rich Iannone: My background is in programming, data analysis, and data visualization. Much of my current software engineering work on R packages is intended to make working with data easier. I truly believe that with the right approach, tools like these can be both powerful and easy to use.

Presented at the 2023 R/Pharma Conference (October 26, 2023)

Rich Iannone

Presentation slides available at https://github.com/rich-iannone/presentations/tree/main/2023-10-23-rpharma_gt_2023

Bio: My background is in programming, data analysis, and data visualization. Much of my current software engineering work on R packages is intended to make working with data easier. I truly believe that with the right approach, tools like these can be both powerful and easy to use.

Presented at the 2023 R/Pharma Conference (October 24, 2023)

Live Q&A recording: https://youtu.be/JUgChPCa3vs

Follow-up links:

• Posit Team: https://posit.co/products/enterprise/team/
• Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo
• Follow-along blog post: https://posit.co/blog/scheduling-a-quarto-doc-on-posit-connect/
• Source code for example: https://github.com/ryjohnson09/quarto-job-scheduling
• Posit Team demo resources: pos.it/demo-resources

Timestamps: 1:45 - What is Posit Team? 3:31 - The data we are analyzing: R package download data from within Posit Package Manager (via experimental API) 4:44 - What is Quarto? (we are creating two Quarto docs today) 8:06 - Create a new session in Posit Workbench 8:56 - Create a new project within the RStudio IDE 10:13 - Create the first Quarto document (ETL: extra, transform, load workflow) 13:55 - Publish the first Quarto doc to Posit Connect 16:50 - Take the package download results and pin it to Posit Connect (overview of pins) 18:30 - Schedule the first Quarto doc to run every day at 7am on Posit Connect 20:49 - Create the second Quarto document (report for stakeholders) 23:41 - View the first “boring report” 24:54 - Using a custom Posit format for the report 27:39 - First look of the themed report without modifications 29:00 - Adding Posit themed colors to the gt table 31:35 - Apply code chunk options to hide code from output 32:44 - Publish the second Quarto document to Posit Connect 34:28 - View finished custom branded Quarto document 34:44 - Define specific users who have access to the Quarto doc on Posit Connect 35:10 - Schedule the second Quarto doc to read in the pinned data from the first Quarto doc 36:30 - Example emailed report from the scheduled Quarto report

On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar.

Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience!

We will discuss topics that will speak to:

• Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain,
• Teams searching for a new analytic platform built to support open-source data science,
• And, those that are just curious about Posit Team!

What you can expect from the monthly Posit Team demo:

During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features!

While each session’s content will vary slightly, here are a few core topics we will address each month:

• Open Source Analytics: The future of data science is open source. We’ll discuss methods for leveraging open-source tools and packages in a secure and scalable way!
• Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more!
• Data Access: Data comes in various forms and is stored in various ways. We’ll discuss best practices for accessing, reading, and writing data!
• Job Scheduling: Do you have recurring data science jobs? We’ll show you how to automate these processes using Posit Connect.

What is Posit Team?

Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages.

Registration is not required. The event will be streamed through YouTube Premiere

For more details: • Demo script in this video: https://pos.it/gt8 • Read the blog post on gt 0.8.0: https://posit.co/blog/new-features-upgrades-in-gt-0-8-0/ • Learn more at https://gt.rstudio.com/ • See a full list of new features and improvements at https://gt.rstudio.com/news/index.html#gt-080

Rich Iannone

Key Resources: ⬡ Get started with {gt} - https://gt.rstudio.com

Reach out: 38:48 - How do I ask Rich about {gt}, feature requests, bug reports, how to solve a problem via {gt}? Rich and the {gt} team would love to hear from you. ⬡ Feature requests & bug reports with GitHub Issues, https://github.com/rstudio/gt/issues ⬡ GitHub Discussions, https://github.com/rstudio/gt/discussions ⬡ Ask the community a question, https://community.rstudio.com/tag/gt ⬡ Follow {gt} on Twitter, feel free to reach out and ask questions, https://twitter.com/gt_package

Timestamps Rich Iannone Introduction. 03:52 - Why {gt}? - What does {gt} bring to the table? Why so much effort into static, data display tables? 05:50 - Why open source? Why is {gt} open source and why have you dedicated your career to develop open source software? 08:30 - {gt} v0.7.0, Tell us about those new vector formatting functions in {gt}. Why did you include them? Could you show us some examples? {gt}’s vector formatting functions help you customize the styling, look and feel of your values. Converting the output values R gives you, and making them look exactly the way you want them to can be tricky. A lot of work was put into {gt} to give nice value formatting options. You can now access all these outside of a gt table; e.g. in text, in a plot, etc. 22:35 - Could you provide an example or two with the new styling function called opt_stylize()? What kinds of tables can you make with that? Can you extend that with your own tweaks? 28:15 - Can you make your own themes and share them? “How do I create my own custom theme for my table? A theme I can share with the rest of my organization?” 31:58 - What is the distinction between tab_options and the opt_* functions? Why would a function be in opt_* and not tab_options? 34:00 - sub_values() function, to find and replace certain values in your table. 36:50 - What is the current support for latex in {gt} at the moment? “Personally, I much prefer HTML, but for scientific publications, we are asked to provide a LaTeX file.” 42:50 - “In my work, I often produce A4 output in PDF, mainly with ggplot2 content. It would be nice to be able to combine ggplot + gt tables in a similar way {patchwork} works. Having the plot and the table next to it is very useful sometimes.” 44:30 - Interactive Tables with {gt}? 47:45 - “Any plans to make applying of same style to several columns easier? Unless I’m mistaken, the locations argument of tab_style requires one to specify an individual column. See here: https://gt.rstudio.com/reference/tab_style.html#examples." Yes, supply a vector of columns or use tidyselect functions. 49:15 - “Excel output with {gt}? Would be a huge improvement. I often have to produce tabular output that can be easily reused. Usually it means Excel tables. So far I have mainly done this with Python and openpyxl or PyWin32 (through COM). A simple solution in R would be great.” 50:20 - Support for additional output formats with {gt}? Excel, PowerPoint, etc.? 50:25 - {pointplank}, a package to methodically validate your data whether in the form of data frames or as database tables., https://rich-iannone.github.io/pointblank/ . Check out the workshop materials at https://github.com/rich-iannone/pointblank-workshop 55:50 - “Are there ways to have grouped rows? I mean when repeated rows have same characters can we merge them to one?” 58:00 - “Is there an ability to add ‘battleship coordinates’ (e.g. column letters & row numbers) to a gt object? This is a standard for table across my org and I’ve been trying to figure out how to implement it.” 59:59 “Do you have suggestions or examples of building out & applying corporate formatting to gt tables (e.g. adding a company logo, company colors, etc.)?” 01:04:30 - “With PDF/LaTeX output for wide tables, it does not shrink the table.”

Rich Iannone

For more details, ⬢ Read the blog post on gt 0.7 https://www.rstudio.com/blog/all-new-things-in-gt-0-7-0/ . ⬢ Learn more about gt at https://gt.rstudio.com/ . ⬢ Follow the gt twitter account, https://twitter.com/gt_package .

00:07 The new Word table output format, .docx output. 00:34 A whole new family of vector formatting functions (vec_fmt_*()) has been added. 01:03 Table presets/themes styling with the new opt_stylize() function. 01:50 The new tab_stub_indent() for superfine control over row label indentation (in the stub) 02:26 The new fmt_duration() function for formatting of time duration values. 03:32 An upgraded gtsave() that uses {webshot2}, .png output looks better. 04:14 Accessibility enhancements for HTML table outputs

Rich Iannone

Content website: https://jthomasmock.github.io/quarto-2hr-webinar/ FULL Workshop Materials (this was from a 2-day workshop): rstd.io/get-started-quarto Other upcoming live events: rstd.io/community-events

Double-check: Are you on the latest version of RStudio i.e. v2022.07.1 or later?

Packages used: tidyverse, gt, gtExtras, reactable, ggiraph, here, quarto, rmarkdown, gtsummary, palmerpenguins, fs, skimr

️ Pre-built RStudio Cloud with workshop materials already installed: https://rstudio.cloud/content/4332583

For follow-up questions, please use: community.rstudio.com/tag/quarto

Timestamps: 7:16 - What is Quarto? 8:28 - How does R Markdown work? 9:40: Quarto, more than just knitr 13:56 - Quarto can support htmlwidgets in R and Jupyter widgets for Python/Julia 14:18 - Native support for Observable Javascript 19:28 - Quarto in your own workspace (Jupyter Lab, VSCode, RStudio) 20:26 - RStudio Visual Editor mode 23:30 - VS Code YAML 26:02 - Quarto for collaboration 26:55 - How do you publish Quarto? (Quarto Pub, GitHub Pages, RStudio Connect, Netlify) 28:44 - What about Data Science at Work? 29:59 - Formats baked into Quarto (basic formats, beamer, ppt, html slides, advanced layout, cross references, websites, blogs, books, interactivity) 32:13 - What to do with my existing .Rmd or .ipynb? 33:16 - Why Quarto, instead of R Markdown? 40:50 - Text Formatting 41:30 - Headings 41:51 - Code (also merging R and Python in one document) 43:29 - What about the CLI? 44:55 - Navigating in the terminal 57:56 - PART 2: Authoring Quarto 1:00:22 - Output options 1:04:46 - Quarto workflow 1:12:06 - Quarto YAML intelligence 1:13:20 - Divs and Spans 1:22:13 - Figure layout 1:34:40 - Code chunk options 1:41:00 - Quarto and R Markdown (converting R Markdown to Quarto)

This 2-hour virtual session is designed for those who have no or little prior experience with R Markdown and who want to learn Quarto.

Want to get started with Quarto?

• Install RStudio v2022.07.1 from https://www.rstudio.com/products/rstudio/download/#download - this will come with a working version of Quarto!
• Webinar materials/slides: https://jthomasmock.github.io/quarto-2hr-webinar/
• Workshop materials on RStudio Cloud: https://rstudio.cloud/content/4332583

What is Quarto?

Quarto is the next generation of R Markdown for publishing, including dynamic and static documents and multi-lingual programming language support. With Quarto you can create documents, books, presentations, blogs or other online resources.

Should I take this?

As with all the community meetups, everyone is welcome. This will be especially interesting to you if you have experience programming in R and want to learn how to take advantage of Quarto for literate data science programming in academia, science, and industry.

This workshop will be appropriate for attendees who answer yes to these questions:

Have you programmed in R and want to better encapsulate your code, documentation, and outputs in a cohesive “data product”? Do you want to learn about the next generation of R Markdown for data science? Do you want to have a better interactive experience when writing technical or scientific documents with literate programming?

For more info on Quarto: quarto.org

File an issue: https://github.com/rstudio/gt/issues ) Talk about your ideas on the Discussions page: https://github.com/rstudio/gt/discussions

Learn more about the gt package here: https://gt.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

Code: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles

Learn more about the gt package here: https://gt.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Gemeni City by Blue Dot Sessions https://app.sessions.blue/browse/track/113567

Rich Iannone

Learn more about the gt package here: https://gt.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

You can find the code for each table here: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles/01_round-01_digital-publications

Learn more about the gt package here: https://gt.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

Code to recreate the table from the video: https://github.com/kierisi/rstudio_videos/blob/main/gt/rich-intendo-project-walkthrough/intendo-30032022.R

Learn more about the gt package here: https://gt.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) Motion design and editing: Jesse Mostipak (@kierisi) Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

Links shared at the meetup! George’s GitHub/ Presentation Resources: https://github.com/stringfestdata/rstudio-mar-2022

Packages? Where to find them & recommendations: CRAN Task Views: https://cran.r-project.org/web/views/ Mark shared: for folks who primarily use excel to present formatted tables, the gt package is a great way to start doing this programmatically in R: https://gt.rstudio.com/ Ivan shared: In addition to regular Google, I’d recommend https://rseek.org/ , given that the character ‘R’ is sometimes not search friendly :) Jeff shared: Fpp2 is great for forecasting and time series analysis - https://otexts.com/fpp2/ Floris shared: https://otexts.com/fpp3/ Ivan shared: If you’re into tidyverse, there’s an equivalent for time-series: https://tidyverts.org/ George shared: https://dplyr.tidyverse.org/ Ryan shared: This can be a helpful package for dynamically editing tables, like in excel https://github.com/DillonHammill/DataEditR Ryan shared: This is a great package for making and learning ggplot visualizations: https://cran.r-project.org/web/packages/esquisse/vignettes/get-started.html

Other resources: Monaly shared: There is a R help group: r-help@r-project.org George shared: Helpful book/site on statistics: https://moderndive.com/ Ryan shared:Harvard has a good online source (free options) that has a number of classes, the following for stats: https://www.edx.org/professional-certificate/harvardx-data-science George shared: R for Data Science free book: https://r4ds.had.co.nz/ Fernando shared: big book of R https://www.bigbookofr.com/index.html Floris shared: Advanced R Book: https://adv-r.hadley.nz/ Pedro shared: The R for Data Science Slack channel is a great learning resource! r4ds.io/join (we just made a channel there called #chat-excel_to_r Ivan shared: For teams who are deeply entrenched in Excel (like my old team), this tool may be useful - https://bert-toolkit.com/ . It allows running R code in .xls, so you can learn R while doing .xls :)

Re: Glossary of terms: Ivan shared: inner_join() is like VLOOKUP in .xls. Dan shared: Here’s one cheat sheet (glossary of Excel to R) that I just found; https://paulvanderlaken.com/2018/07/31/transitioning-from-excel-to-r-dictionary-of-common-functions/

Extra Meetup Links Feedback: rstd.io/meetup-feedback Talk submission: rstd.io/meetup-speaker-form If you’d like to find out about upcoming events you can also add this calendar: rstd.io/community-events RStudio conference/submit a talk: https://www.rstudio.com/conference/ Recordings of all meetups: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Alok’s Slides: https://lnkd.in/gxQUf8nV

Packages shared: sportsdataverse: https://lnkd.in/g8UKAJgc wehoop: https://lnkd.in/grBgmc33 hoopr: https://lnkd.in/gRHNWV4j glmnet: https://lnkd.in/g4cuxYzs googleAuthRverse: https://lnkd.in/gf5fRgcC All of Mark Edmondson’s Google packages: https://lnkd.in/gmTpvMsY

Other resources shared: Sports data: https://www.spotrac.com/ Analyzing NCAA Basketball with GCP: https://lnkd.in/gT-4vWwa 2020 Google Cloud March Madness Insights: https://lnkd.in/gEWd9xtu Alok’s presentation on Innovating the MLB Fan Experience through Data: https://lnkd.in/g9XmFKsy NFL Player Tracking Data Meetup recording: Analyzing Soccer Data with BigQuery: https://lnkd.in/gbbCGKaK Sports channel on the R for Data Science Online Learning Community Slack: r4ds.io/join # chat-sports_analytics

You can read more about gt here: https://gt.rstudio.com/articles/intro-creating-gt-tables.html And you can learn more about Shiny here: https://shiny.rstudio.com/

Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/

Content: Rich Iannone (@riannone) Design & editing: Jesse Mostipak (@kierisi)

Rich Iannone

Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/

About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native.

About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/

About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native.

About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

VIEW MATERIALS https://github.com/rich-iannone/presentations/tree/master/2019_01-19-rstudio_conf_gt

About the Author Rich Iannone My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone