Rich Iannone

pointblank

Data validation toolkit for assessing and monitoring data quality

Quarto

Open-source scientific and technical publishing system built on Pandoc

Great Tables

Make awesome display tables using Python

gt

Easily generate information-rich, publication-quality tables from R

rmarkdown

Dynamic Documents for R

pkgdown

Generate static html documentation for an R package

Shiny

Easy interactive web applications with R

shinytableau

Extend Tableau dashboards using Shiny

blastula

Easily send great-looking HTML email messages from R

fontawesome

Easily insert FontAwesome icons into R Markdown docs and Shiny apps

flexdashboard

Easy interactive dashboards for R

cheatsheets

Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/

distill

Distill for R Markdown

ymlthis

write YAML for R Markdown, bookdown, blogdown, and more

beyond-dashboard-fatigue

Materials for the RStudio webinar 'Beyond Dashboard Fatigue'

hex-stickers

RStudio hex stickers

sass

Sass compiler package for R

shiny-examples

shinycoreci-apps

SUPERSEDED - Apps for Shiny continuous integration testing

shinyvalidate

Input validation package for the Shiny web framework

R/Medicine 2025

The R/Medicine conference provides a forum for sharing R based tools and approaches used to analyze and gain insights from health data

R/Pharma 2025

R/Pharma is a scientifically & industry oriented online conference focused on the use of R in the development of pharmaceuticals

Recreating Septa Transit Timetables in Python

Learn to build complex table layouts in Great Tables: vertical text, column spanners, and zebra striping

Michael Chow, Rich Iannone

How to use {pointblank} to understand, validate, and document your data

How to use {pointblank} to understand, validate, and document your data - Rich Iannone Abstract: This workshop will focus on the data quality and data documentation workflows that the pointblank package makes possible. We will use functions that allow us to: (1) quickly understand a new dataset (2) validate tabular data using rules that are based on our understanding of the data (3) fully document a table by describing its variables and other important details. The pointblank package was created to scale from small validation problems (“Let’s make certain this table fits my expectations before moving on”) to very large (“Let’s validate these 35 database tables every day and ensure data quality is maintained”) and we’ll delve into all sorts of data quality scenarios so you’ll be comfortable using this package in your organization. Data documentation is seemingly and unfortunately less common in organizations (maybe even less than the practice of data validation). We’ll learn all about how this doesn’t have to be a tedious chore. The pointblank package allows you to create informative and beautiful data documentation that will help others understand what’s in all those tables that are so vital to an organization. Resources mentioned in the workshop: * Workshop GitHub repository: https://github.com/rich-iannone/pointblank-workshop * pointblank documentation: https://rstudio.github.io/pointblank/

Rich Iannone

Making Things Nice in Python (Rich Iannone, Posit) | posit::conf(2025)

Making Things Nice in Python Speaker(s): Rich Iannone Abstract: When working on the Great Tables and Pointblank Python packages, we've tried to make them 'nice'. These packages give you a lot of convenient options, and a large volume of docs and examples. In the Python world, this might be received differently than it would be in R. Whether it was integrating Polars selectors in Great Tables or accepting a multitude of DataFrames and DB tables in Pointblank, these design choices can be seen as surprising things to established Python developers. However, I argue it's good to be doing this! People are benefitting from these approaches. I'll share a few of these developer stories with the takeaway being that Python packages could and should pay attention to good user experience. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Rich Iannone

Virtual Day AMA with Rich Iannone | posit::conf(2025)

Libby Heeren from the Data Science Hangout hosts an AMA (ask me anything) session with Rich Iannone, a software engineer at Posit. Rich fielded questions from participants about everything from his journey in open source development to the guitars on the wall behind him. He was asked about his very first package, talked about the importance of personal projects for career growth, and described some of the differences between CRAN and PyPI as package repositories/distributors. Rich emphasized the value of community engagement and encouraged data newcomers to contribute to open source. He also shared his interests in niche packages (and hiding easter eggs in his open source code!) and the challenges of transitioning personal projects to public ones

Rich Iannone

Great Tables `v0.18.0`: Easy Column Spanners and More!

New in Great Tables v0.18.0: automatic column spanners with tab_spanner_delim(), boolean formatting, and rotated labels

Rich Iannone

Great Tables + marimo = Interactive Tables

Make your Great Tables interactive with marimo's reactive widgets

Rich Iannone, Jerry Wu

Data Validation Libraries for Polars (2025 Edition)

Choosing a data validation library for Polars? We compare Pandera, Patito, Pointblank, Validoopsie, and Dataframely

Rich Iannone

C'mon C'mon: Let's Do a Pointblank Workshop!

Want a free Pointblank workshop for your data team? Here's how to set one up

Rich Iannone

Overhauling Pointblank's User Guide

Pointblank's revamped user guide: spiral learning, clearer examples, and full API coverage

Rich Iannone, Michael Chow

Level Up Your Data Validation with `Actions` and `FinalActions`

Automate responses to bad data with Pointblank's Actions and FinalActions

Rich Iannone

Introducing Pointblank

Get started with Pointblank for data validation using Polars, Pandas, or DuckDB

Rich Iannone

Tables in Python with Great Tables

Tables in Python with Great Tables - Rich Iannone, Michael Chow Resources mentioned in the workshop: - 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

How We Used Great Tables to Supercharge Reporting in Pointblank

See how Great Tables powers Pointblank's beautiful, shareable validation reports

Rich Iannone

We want GREAT tables! | Richard Iannone & Michael Chow | Data Science Hangout

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

Great Tables `v0.15.0`: Flags, Icons, and Other Formatting Goodies

New in Great Tables v0.15.0: country flag icons with fmt_flag(), Font Awesome icons, and accounting notation

Rich Iannone

Great Tables: Generating LaTeX Output for PDF

Create publication-ready PDF tables by exporting Great Tables to LaTeX

Rich Iannone

Great Tables `v0.13.0`: Applying styles to all table locations

Great Tables v0.13.0 brings styling to all table locations via expanded loc.*() methods

Rich Iannone, Michael Chow

Great Tables `v0.12.0`: Google Fonts and zebra stripes

New in Great Tables v0.12.0: Google Fonts via google_font() and zebra striping with opt_row_striping()

Rich Iannone

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It's nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of 'display tables' in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We'll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by "publication ready tables" 1:29 Overview of what we'll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07 .fmt_nanoplot(): Small plots within table cells 19:08 .data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

Great Tables for Scientific Publishing

Format scientific tables in Great Tables: unit notation, chemical formulas, and scientific notation

Rich Iannone

The Design Philosophy of Great Tables

Why tables matter: 5,000 years of table design and how Great Tables solves the modern dilemma

Rich Iannone, Michael Chow

Great Tables `v0.4.0`: Nanoplots and More

New in Great Tables v0.4.0: nanoplots for tiny, interactive visualizations inside table cells

Rich Iannone

Great Tables `v0.3.0`: So Many Style Options!

New in Great Tables v0.3.0: control column widths, style entire tables, and use system fonts

Rich Iannone

Great Tables `v0.2.0`: Easy Data Coloring

New in Great Tables v0.2.0: data_color() for automatic cell coloring based on values

Rich Iannone

Can I Have a Word? - posit::conf(2023)

Presented by Ellis Hughes Since its release, {gt} has won over the hearts of many due to its flexible and powerful table-generating abilities. However, in cases where office products were required by downstream users, {gt}'s potential remained untapped. That all changed in 2022 when Rich Iannone and I collaborated to add Word documents as an official output type. Now, data scientists can engage stakeholders directly, wherever they are. Join me for an upcoming talk where I'll share my excitement about the new opportunities this update presents for the R community as well as future developments we can look forward to. 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

Eric Nantz, Alex Lauer, Rich Iannone - A Pivotal Year of Milestones in R Consortium Working Groups

A Pivotal Year of Milestones: R Submissions Working Group and R Tables for Regulatory Submissions updates - Eric Nantz, Alexandra Lauer, and Rich Iannone Resources mentioned in the presentation: - 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

Rich Iannone | What's new and exciting in gt 0.8.0 | Posit

With the gt package, anyone can make wonderful-looking tables using the R programming language. Rich Iannone, maintainer of gt, shows what's new and improved in gt 0.8.0! 00:00 Introduction 00:42 Find/Replace values with sub_values() 02:46 Find values and style them with tab_style_body() 05:00 Place a cell in your Quarto/RMarkdown doc with extract_cells() 07:13 Make numbers more readable with cols_align_decimal() 08:54 See column id info with tab_info() 11:03 Date and time formatting improvements 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

How to Use {pointblank} to Understand, Validate, and Document your Data

Workshop recorded as part of the R/Pharma Workshop Series (October 27, 2022) Instructor: Rich Iannone (Posit) Resources mentioned in the workshop: - https://github.com/rich-iannone/pointblank-workshop - https://github.com/kmasiello/pointblank_demo

Rich Iannone

Open Source Chat - {gt} with Rich Iannone

Join Rich Iannone, maintainer of the {gt} package, as he takes questions from the community about the latest in {gt} v0.7.0, and building great looking data display tables with R. 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

What's New in {gt} 0.7.0?

gt 0.7.0 was just released. Rich Iannone, maintainer of gt, dives into the 7 new features added. 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

Submitting Your Work to the Table Contest | 2022 Table Contest

Rich Iannone walks through how to submit a table to the 2022 Table Contest. He explains considerations for each field, and how to update & edit your entry afterwards. Learn more about the 2022 Table Contest at https://www.rstudio.com/blog/rstudio-table-contest-2022/

Rich Iannone

Let's Create a Quarto Doc & Share it on Quarto.Pub | Table Contest 2024

Rich Iannone creates a gt table with Quarto, and publishes it on quarto.pub. You'll see how to set up your .qmd file in the RStuido IDE, preview your table, publishing it to Quarto Pub, some admin settings, and how to share your table made in Quarto to the world

Rich Iannone

Rich Iannone || New features in {gt} 0.6.0! || RStudio

00:00 Introduction 00:18 sub_missing() 03:51 Markdown formatting in sub_missing() 04:51 sub_zero() 07:34 sub_small_vals() 13:08 sub_large_vals() 16:25 final thoughts A new version of the R package {gt} has been released! We are now at version `0.6.0` and there are now even more features that'll make your display/summary tables look and work much, much better. Let's run through some of the bigger changes and see the benefits they can bring! New functions for substituting cell data We now have four new functions that allow you to make precise substitutions of cell values with perhaps something more meaningful. They all begin with `sub_` and that's short for substitution! sub_missing() (formerly known as fmt_missing()) Here's something that's both old and new. The sub_missing() function (for replacing NAs with... something) is new, but it's essentially replacing a function that is old (fmt_missing()). The missing_text replacement of "---" is actually an em dash (the longest of the dash family). This can be downgraded to an en dash with "--" or we can go further with "-", giving us a hyphen replacement. Or, you can use another piece of text. If you're using and loving fmt_missing(), it's okay! You'll probably receive a warning about it when you upgrade to {gt} 0.6.0 though. Best to just substitute fmt_missing() with sub_missing() anyway! sub_zero() The sub_zero() function allows for substituting zero values in the table body. sub_small_vals() Next up is the sub_small_vals() function. Ever have really, really small values and really just want to say they are small? With sub_small_vals() we can reformat smaller numbers using the default threshold of 0.01. Small and negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the small, negative values. You don't have to settle with the default threshold value or the default replacement pattern (in small_pattern). This can be changed and the "x" in small_pattern (which uses the threshold value) can even be omitted. sub_large_vals() Okay, there's one more substitution function to cover, and this one's for all the large values in your table: sub_large_vals(). With this you can substitute what you might consider as too large values in the table body. Large negative values can also be handled but they are handled specially by the sign parameter. Setting that to "-" will format only the large values that are negative. You don't have to settle with the default threshold value or the default replacement pattern (in large_pattern). This can be changed and the "x" in large_pattern (which uses the threshold value) can even be omitted. Final thoughts We are always trying to improve the gt package with a mix of big features (some examples: improving rendering, adding new families of functions) and numerous tiny features (like improving existing functions, clarifying documentation, etc.). It's hoped that the things delivered in gt 0.6.0 lead to improvements in how you create and present summary tables in R. If there are features you *really* want, always feel free to: 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

{gt} Table Battles || Eurovision || RStudio

00:00 Introduction 00:07 Jesse's gt table, with a focus on flag emoji and interactivity via a Shiny app 09:50 Rich's gt table, with a focus on CSS and embedded animations 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

{gt} Table Battles || Crosswords || RStudio

00:00 Introduction 00:34 Rich's gt table, with a focus on creating audio within a table 07:28 Jesse's gt table, with a focus on sentiment analysis 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

{gt} Table Battles || Digital Publications || RStudio

00:00 Introduction 00:32 Jesse's gt table, with a focus on changing background cell color 07:11 Rich's gt table, which uses three different tables to create a fixed-size scrollable gt table 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

Rich Iannone || {gt} Intendo Game Data Project Walkthrough || RStudio

00:00 Introduction 00:11 Setting up our environment 01:21 Importing data 01:56 Data preparation using the tidyverse 14:12 Basic gt table 16:25 Specifying row order with row_group_order() 17:20 Formatting currency with fmt_currency() 18:10 Formatting missing values with fmt_missing() 18:55 Creating row groups with tab_options() 19:50 Relabel column names with cols_label() 20:41 Creating tab spanners with tab_spanner() 23:00 Creating a table title and subtitle with tab_header() 24:40 Aligning table title and subtitle with opt_align_table_header() 25:16 Creating a stubhead label with tab_stubhead() 26:00 Format all table cell text using tab_style() 27:25 Automatically format data color based on value using data_color() 30:45 Creating Markdown-friendly source notes using tab_source_note() 32:45 Creating Markdown-friendly footnotes using tab_footnote() 39:28 Adjust table column width using cols_width() 40:55 Adjust cell padding using opt_horizontal_padding() and opt_vertical_padding() 42:22 Change row group headers using tab_style() 43:40 Convert all table text to small caps using opt_all_caps() 43:58 Change all table text font using opt_table_font() 44:28 Changing table, table heading, footnotes, and source notes background color using tab_options() 46:41 Add a table "cap" at the top and bottom using table.border.top.width() and table.border.bottom.width() 47:23 Use multiline formatting with footnotes using footnotes.multiline() 47:34 Change line style using table_body.hlines.style() 47:55 Change table title and subtitle font sizes using heading.title.font.size() and heading.subtitle.font.size() 48:11 Checking out our final table! 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

Rich Iannone || Making Beautiful Tables with {gt} || RStudio

00:00 Introduction 00:37 Adding a title with tab_header() (using Markdown!) 01:47 Adding a subtitle 02:48 Aligning table headers with opt_align_table_header() 03:48 Using {dplyr} with {gt} 06:03 Create a table stub with rowname_col() 07:35 Customizing column labels with col_label() 09:45 Formatting table numbers with fmt_number() 12:10 Adjusting column width with cols_width() 15:39 Adding source notes with tab_source_note() 16:55 Adding footnotes with tab_footnote() 18:55 Customizing footnote marks with opt_footnote_marks() 19:10 Demo of how easy managing multiple footnotes is with {gt} 23:41 Customizing cell styles with tab_style() 27:07 Adding label text to the stubhead with tab_stubhead() 28:15 Changing table font with opt_table_font() 29:25 Automatically scaling cell color based on value using data_color() With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs. 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

R/Pharma 2021 Day 1. Rich Iannone. The gt Package: Past, Present, and Future

R/Pharma 2021 Day 1 Rich Iannone (RStudio) The gt Package: Past, Present, and Future

Rich Iannone

Webinar Summary | Avoid Dashboard Fatigue | RStudio (2020)

0:00 Introduction 0:07 The Problem 1:05 The Solution 3:20 Real Life Success Stories 5:27 Demo (with code) Don't have an hour to watch a webinar? We've made a summary video that covers the main points of our "Avoid Dashboard Fatigue" webinar from Sean Lopp and Rich Iannone. The full webinar covered: Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. 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

Sean Lopp & Rich Iannone | Avoid Dashboard Fatigue | RStudio (2020)

Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. 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

Rich Iannone | Introducing the gt package | RStudio (2019)

With the gt package, anyone can make great-looking display tables. Though the package is still early in development, you can do some really great things with it right now! I'll walk through a few examples that touch upon the more common table-making use cases. These will include features like adding table parts, integrating footnotes, styling/transforming table cells, using tables in R Markdown documents, and even including gt tables in email messages. 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