Using the CSV listed in https://twitter.com/hadleywickham/status/1351298997483425792?s=20
ICS file created on 2021-01-18 (PET)
Created
January 19, 2021 03:24
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Save jmcastagnetto/8bc90ed6f5988f00d27c90a9bc7a2c8e to your computer and use it in GitHub Desktop.
RStudio Global 2021 events
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| library(tidyverse) | |
| library(lubridate) | |
| local_tz <- "America/Lima" | |
| rstudio2021_df <- read_csv( | |
| "https://rstudio-global-2021.s3.amazonaws.com/schedule.csv", | |
| col_types = cols( | |
| .default = col_character(), | |
| time_gmt = col_datetime() | |
| ) | |
| ) %>% | |
| mutate( | |
| time_local = lubridate::with_tz(time_gmt, tzone = local_tz) | |
| ) | |
| # temp fix for the year | |
| year(rstudio2021_df$time_gmt) <- 2021 | |
| year(rstudio2021_df$time_local) <- 2021 | |
| ics <- rstudio2021_df %>% | |
| mutate( | |
| name = replace_na(name, "*"), | |
| url = replace_na(url, ""), | |
| summary = glue::glue("{topic}: {title_text} ({name})"), | |
| description = glue::glue("{abstract_text}"), | |
| start_time = paste0("DTSTART:", | |
| format(time_gmt, "%Y%m%dT%H%M%SZ")), | |
| end_time = paste0("DTEND:", | |
| format(time_gmt + | |
| seconds(as.numeric(duration)), | |
| "%Y%m%dT%H%M%SZ")) | |
| ) %>% | |
| rename( | |
| location = url | |
| ) %>% | |
| rowwise() %>% | |
| mutate( | |
| uid = digest::digest(paste0(time_gmt, duration, summary), 'md5'), | |
| event = glue::glue( | |
| " | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:{uid} | |
| {start_time} | |
| {end_time} | |
| SUMMARY:{str_trim(summary)} | |
| DESCRIPTION:{str_trim(description) %>% str_replace_all('\n', '')} | |
| LOCATION:{location} | |
| END:VEVENT | |
| " | |
| ) | |
| ) %>% | |
| pull( | |
| event | |
| ) | |
| write_lines( | |
| c( | |
| "BEGIN:VCALENDAR", | |
| "VERSION:2.0", | |
| "PRODID:RStudio-Global-2021", | |
| ics, | |
| "END:VCALENDAR" | |
| ), | |
| "schedule.ics", | |
| sep = "\r\n" | |
| ) | |
| saveRDS( | |
| rstudio2021_df, | |
| "schedule.rds" | |
| ) |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| BEGIN:VCALENDAR | |
| VERSION:2.0 | |
| PRODID:RStudio-Global-2021 | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:700c2c7187197329ab9562dc55df1349 | |
| DTSTART:20210121T160000Z | |
| DTEND:20210121T163300Z | |
| SUMMARY:Keynote: Maintaining the house the tidyverse built (Hadley Wickham) | |
| DESCRIPTION:Hadley will talk about how the tidyverse has evolved since its creation (just five years ago!). You'll learn about our greatest successes, learn from our biggest failures, and get some hints of what's coming down the pipeline for the future. | |
| LOCATION:https://global.rstudio.com/student/page/40521 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9f1d05045aa8aef4807ce15e94027348 | |
| DTSTART:20210121T163300Z | |
| DTEND:20210121T172000Z | |
| SUMMARY:Keynote: Q&A: Hadley Wickham (*) | |
| DESCRIPTION:Join Jenny Bryan for audience Q&A with keynote speaker Hadley Wickham. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:61fc173deb1c3ead7c2e2f3186f6f478 | |
| DTSTART:20210121T180000Z | |
| DTEND:20210121T182156Z | |
| SUMMARY:Learning: Always look on the bright side of plots (Kara Woo) | |
| DESCRIPTION:Everyone who creates visualizations in R is bound to make mistakes that prevent their plots from looking as they should. Sometimes, these mistakes create beautiful "accidental aRt", though other times they're just plain frustrating. Either way, however, there's something to be learned. This talk will draw on years of watching both the ggplot2 issue tracker and the @accidental__aRt twitter account to highlight some common plot foibles and explain what they can teach us about how ggplot2 works. | |
| LOCATION:https://global.rstudio.com/student/page/40618 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:61648a57352dcf6635fbb9ba2976a64d | |
| DTSTART:20210121T182156Z | |
| DTEND:20210121T182902Z | |
| SUMMARY:Learning: Starting an R Book Club: Cooking Up Friendships in Isolation (Maya Gans) | |
| DESCRIPTION:Amidst a global pandemic there’s been one consistency in my life: every Tuesday a group of friends meet to discuss Hadley Wickham’s Advanced R. I crowdsourced interest using the R4DS Slack and the results were magical: a group of incredibly curious and generous people motivated to learn and teach one another emerged. The meetings evolved from a group of strangers giving timid presentations to a safe space where we share and improve upon personal applications. The 1 club has grown to 3 regional cohorts, and became a model for discussing other books too. This talk will go over the structure of our meetings in hopes of empowering others to start their own book clubs, showcasing a different way people can create and engage in communities. | |
| LOCATION:https://global.rstudio.com/student/page/40626 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:0a944d771c17bb059114150809d5c50f | |
| DTSTART:20210121T182902Z | |
| DTEND:20210121T190000Z | |
| SUMMARY:Learning: Discussion: Learning 1 (*) | |
| DESCRIPTION:Join Jesse Mostipak and Ryan Estrellado for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9d85fbf1efc52b4eacd0d387e8f1dceb | |
| DTSTART:20210121T190000Z | |
| DTEND:20210121T191932Z | |
| SUMMARY:Learning: Art Lessons: One Year as RStudio’s Artist-in-Residence (Allison Horst) | |
| DESCRIPTION:Art can be a welcoming bridge for learners and users to engage with and learn tools and skills in R. As RStudio’s first Artist-in-Residence, my goal has been to make the R landscape more welcoming for a broader community of users through engaging, didactic artwork. In this R, art, and heart-filled talk, I’ll share the motivation behind my R artwork and some lessons learned over the past year as Artist-in-Residence, including: - Learning to embrace cute and credible artwork - Art to help students engage with, learn and remember R skills - Art for community building and supportI hope this talk inspires viewers to use, create and share more artwork, so that together we can make the R landscape feel even brighter. | |
| LOCATION:https://global.rstudio.com/student/page/40597 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:401d632bf9f650ec92e9aa8b1b154d29 | |
| DTSTART:20210121T191932Z | |
| DTEND:20210121T193818Z | |
| SUMMARY:Learning: Aesthetically automated figure production (Megan Beckett) | |
| DESCRIPTION:Automation, reproducibility, data driven. These are not normally concepts one would associate with the traditional publishing industry, where designers normally manually produce every artefact in proprietary software. And, when you have 1000s of figures to produce and update for a single textbook, this becomes an insurmountable task, meaning our textbooks quickly become outdated, especially in our rapidly advancing world.With R and the tidyverse in our back pocket, we rose to the challenge to revolutionize this workflow. I will explain how we collaborated with a publishing group to develop a system to aesthetically automate the production of figures for a textbook including translations into several languages.I think you’ll find this talk interesting as it shows how we applied tools that are familiar to us, but in an unconventional way to fundamentally transform a conventional process. | |
| LOCATION:https://global.rstudio.com/student/page/40627 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:95a8b1a7e4dd48f071e56c9889345086 | |
| DTSTART:20210121T193818Z | |
| DTEND:20210121T200000Z | |
| SUMMARY:Learning: Discussion: Learning 2 (*) | |
| DESCRIPTION:Join Jesse Mostipak and Ryan Estrellado for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:776c7ad6c4fa0c4b1402fbf27e7a2cd4 | |
| DTSTART:20210121T180000Z | |
| DTEND:20210121T181917Z | |
| SUMMARY:Language interop: Bringing the Tidyverse to Python with Siuba (Michael Chow) | |
| DESCRIPTION:Last January I left my job to spend a year developing siuba, a python port of dplyr. At its core, this decision was driven by a decade of watching python and R users produce similar analyses, but in very different ways.In this talk, I'll discuss 3 ways siuba enables R users to transfer their hard-earned programming knowledge to python: (1) leveraging the power of dplyr syntax, (2) options to generate SQL code, and (3) working with the plotnine plotting library.Looking back, I'll consider two critical pieces that have helped me develop siuba: using it to livecode TidyTuesday analyses, and building an interactive tutorial for absolute beginners. | |
| LOCATION:https://global.rstudio.com/student/page/40629 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:176381e585cf0c0bd65ad9aeb2ad4263 | |
| DTSTART:20210121T181917Z | |
| DTEND:20210121T183432Z | |
| SUMMARY:Language interop: R & Python: Going Steady (Sean Lopp) | |
| DESCRIPTION:While there has been a lot of excitement about the R and Python love story, there are still misconceptions that individuals, teams, or organizations must pick between R or Python. This talk will explain why this false choice exists, debunk the myths that cause teams to be stuck with only one tool, and clarify how data scientists can use both languages to be more effective. We will explore this love story's blossoming relationship by looking at updates to RStudio's packages and products that make it easier to develop and collaborate in R and Python. This talk is for individuals who want to uncover the benefits of multilingual data science, IT professionals who are skeptical their life can get better by supporting more languages, and data science managers interested in enabling their teams instead of forcing their data superheros to be subservient to particular tools. | |
| LOCATION:https://global.rstudio.com/student/page/40638 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1e5dac27d9017984b9332641ff79a833 | |
| DTSTART:20210121T183432Z | |
| DTEND:20210121T190000Z | |
| SUMMARY:Language interop: Discussion: Language interop 1 (*) | |
| DESCRIPTION:Join Amanda Gadrow and Teon Brooks for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:12b2ff61e9529f39e9307a3493ca95f3 | |
| DTSTART:20210121T190000Z | |
| DTEND:20210121T191924Z | |
| SUMMARY:Language interop: Using pins with Python and JavaScript (Javier Luraschi) | |
| DESCRIPTION:Last year, pins got released as a brand new R package to pin, discover and cache remote resources for R users. This package has matured to support many use cases; from caching remote URLs, and easily sharing datasets with other R users, to building automated pipelines.However, in order to truly collaborate in multi-disciplinary data-driven teams, one needs to consider how to collaborate beyond R? How can we share resources with designers and machine learning experts who happen to use different programming languages like Python and JavaScript?This talk will introduce the pinsjs project, a cross-language community project which has the goal of bringing pins to the broader open source community to enable rich workflows across larger data-driven teams. | |
| LOCATION:https://global.rstudio.com/student/page/40613 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:6bfbc2a35f9c4f8f7c0ae41777145d13 | |
| DTSTART:20210121T191924Z | |
| DTEND:20210121T193723Z | |
| SUMMARY:Language interop: The dynamic duo: SQL & R (Irene Steves) | |
| DESCRIPTION:There's a point in every data wranglers' career in which their full dataset can no longer fit into just CSV files, and the journey to database-world begins. I reached this point about two years ago, when I transitioned from ecological research to the world of eCommerce fraud prevention. My calls to read_csv became scarcer as I came to rely more and more on databases. In this talk, I'll demonstrate how I use R and SQL to access database tables, and how I incorporate both into my daily workflow, aided by features in RStudio IDE. I'll also discuss our company’s "riskiconn" package for handling database connections and queries, which includes customizations to simplify day-to-day data querying. | |
| LOCATION:https://global.rstudio.com/student/page/40612 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c687b17bddd3231bd1495cfacda9984d | |
| DTSTART:20210121T193723Z | |
| DTEND:20210121T200000Z | |
| SUMMARY:Language interop: Discussion: Language interop 2 (*) | |
| DESCRIPTION:Join Amanda Gadrow and Teon Brooks for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:560e65565a4ba3e91d8379853a60eb9a | |
| DTSTART:20210121T180000Z | |
| DTEND:20210121T181934Z | |
| SUMMARY:Visualisation: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages (JooYoung Seo) | |
| DESCRIPTION:Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models.However, data science itself should require insight and computational thinking beyond what is just seen by eyes.JooYoung Seo, who is a blind data scientist and who was working for RStudio's accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data.If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking. | |
| LOCATION:https://global.rstudio.com/student/page/40617 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:3ddc23e5a34db6c4f734b999912939b0 | |
| DTSTART:20210121T181934Z | |
| DTEND:20210121T183342Z | |
| SUMMARY:Visualisation: A New Paradigm for Multifigure, Coordinate-Based Plotting in R (Nicole Kramer) | |
| DESCRIPTION:R is unparalleled in its ability to transform raw data into a wide array of beautiful graphics, all within the same environment. However, when it comes to complex, multi-paneled plots, users rely on 3rd party graphic design software to arrange plots. Here I present the new world of programmatic, coordinate-based multi-figure plotting in R. Employing grid Graphics and drawing from the paradigms of base plotting and ggplot2, I am developing a package that will revolutionize the way plots are laid out in R. Not only will individual plots be aesthetically customizable and tailored for speed, users will also be offered exquisite control over all aspects of page layout, plot placement, and arrangements. Come join me in changing how we plot in R! | |
| LOCATION:https://global.rstudio.com/student/page/40633 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:13d2ca0cb0209dcf3f2d648561a8450a | |
| DTSTART:20210121T183342Z | |
| DTEND:20210121T190000Z | |
| SUMMARY:Visualisation: Discussion: Visualisation 1 (*) | |
| DESCRIPTION:Join Thomas Pedersen and Danielle Navarro for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:08126e10a2e52192b06f85e57d3b3a65 | |
| DTSTART:20210121T190000Z | |
| DTEND:20210121T191600Z | |
| SUMMARY:Visualisation: Trial and Error in Data Viz at the ACLU (Sophie Beiers) | |
| DESCRIPTION:Visualizing data the “right” way requires many considerations — the topic, the quality of your data, your audience, your time frame, and the various channels of (sometimes conflicting) feedback you received. In this presentation, I’ll introduce some reflections on these considerations and ways I’ve incorporated feedback (or not) into my work as Data Journalist at the ACLU. Lastly, I’ll present some of the sillier trials and errors I’ve made that were arguably necessary to my process in creating effective data visualizations using R. | |
| LOCATION:https://global.rstudio.com/student/page/40642 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7e6a58ae14b4f55cb4699713cd443c62 | |
| DTSTART:20210121T191600Z | |
| DTEND:20210121T192045Z | |
| SUMMARY:Visualisation: Racial Equity Dashboard: Unpacking Systemic Inequity (Nicholas Pylypiw) | |
| DESCRIPTION:At Cape Fear Collective, we’re redefining what a town square looks like in our community, serving as a place where all people, organizations, and ideas can come together to effect real, lasting, and systemic change. By merging cutting edge data science with an emphasis on equity and the lived experience of our most marginalized communities, Cape Fear Collective supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. This talk is about how we bring that model to life through our Racial Equity Dashboard, from data sourcing, to modeling and, ultimately, action. | |
| LOCATION:https://global.rstudio.com/student/page/40632 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:233f4cce583f2a2225648d072b7ea357 | |
| DTSTART:20210121T192045Z | |
| DTEND:20210121T192540Z | |
| SUMMARY:Visualisation: Your R is My R too: Reflections on creating the Mi-R community (Danielle Smalls-Perkins | |
| Dorris Scott) | |
| DESCRIPTION:While the R community has made strides in increasing the representation and participation for women and users from underrepresented regions, there are still members of the R community that have expressed desires for a more inclusive space in addition to these strides. In addition, there are unique challenges that underrepresented R users experience in their respective workspaces or academic environments. In late February of 2020, Danielle Smalls-Perkins and Dorris Scott created Mi-R (Minorities in R) as a result of their various experiences both in and outside the R community. The purpose of this talk is to reflect on the challenges, highlights, and future directions of the first six months since the creation of Mi-R. | |
| LOCATION:https://global.rstudio.com/student/page/40605 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:45c7530deeea850a20e2d479ffe55d77 | |
| DTSTART:20210121T192540Z | |
| DTEND:20210121T193040Z | |
| SUMMARY:Visualisation: An easy and friendly way to build your multilingual website (Pamela E. Pairo) | |
| DESCRIPTION:Having a personal website is a great way to share our experiences with other people, that also allows us to improve our communication skills and expand our networking groups. Besides, if the website is multilingual, the scope will be extended considerably by facilitating the exchange of ideas. I will give the key steps, some tips, and important considerations to bear in mind when creating a multilingual website using Blogdown, Hugo, and Netlify. Although having a multilingual website demands more effort, R enables us to build a website easily and keep it updated. I aim to help and encourage others to build their website to promote exchange experiences among people from different native languages. | |
| LOCATION:https://global.rstudio.com/student/page/40634 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:09bf429efc79cd862f104eed5a1e3f16 | |
| DTSTART:20210121T193040Z | |
| DTEND:20210121T193522Z | |
| SUMMARY:Visualisation: Making .pot-ery with R: Translations in R Packages (Michael Chirico) | |
| DESCRIPTION:The R community is globally distributed and R itself is available with messages in 14 languages. Adding translations for non-native English-speaking users of your package can ease their experience and empower them to build better things with less frustration (though please note that "object of type 'closure' is not subsettable" is equally inscrutable in all human languages).In this talk, I will cover translations in R packages -- how to implement them, why to do so, and how to maintain them. This will summarize and extend learnings based on our experience adding Mandarin translations to data.table and culminating in the potools package. | |
| LOCATION:https://global.rstudio.com/student/page/40628 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9afe4b815c452cc943469b878ae7b8a8 | |
| DTSTART:20210121T193522Z | |
| DTEND:20210121T200000Z | |
| SUMMARY:Visualisation: Discussion: Visualisation 2 (*) | |
| DESCRIPTION:Join Thomas Pedersen and Danielle Navarro for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7b9d8f90b5c05a9e237d31ec90fa4ae2 | |
| DTSTART:20210121T200000Z | |
| DTEND:20210121T205011Z | |
| SUMMARY:Keynote: Reporting on and visualising the pandemic (John Burn-Murdoch) | |
| DESCRIPTION:John will discuss the lessons he's learned reporting on and visualising the pandemic, including the world of difference between making charts for a technical audience and making charts for a mass audience. You'll learn from his experience navigating the highly personal and political context within which people consume and evaluate graphics and data, and how that can help us better design and communicate with visualisations down the pipeline for the future. | |
| LOCATION:https://global.rstudio.com/student/page/40615 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c2d5f4aeadca7302445118879ed7ee02 | |
| DTSTART:20210121T205011Z | |
| DTEND:20210121T212000Z | |
| SUMMARY:Keynote: Q&A: John Burn-Murdoch (*) | |
| DESCRIPTION:Join Jenny Bryan for audience Q&A with keynote speaker John Burn-Murdoch. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:a4adc4e2d6da598c1d17d1c676dfc760 | |
| DTSTART:20210121T220000Z | |
| DTEND:20210121T222228Z | |
| SUMMARY:Teaching: Making the jump from learning to applying: R training and documentation for different levels of expertise (Kate Hertweck) | |
| DESCRIPTION:How does someone make the leap from learning R to actively applying R in professional work?At what point (if ever!) do we get to call ourselves "experts" in R?This talk explores what differentiates novice, practitioner, and expert R programmers,and how transitionsbetween these stages occur.I'll discuss the type of support required for R users to move from one level of expertise to the next,and how different types of training and documentation can support R users at each level.Understanding variable levels of education among R practitioners supports our own professional work,from collaborative coding to package development,and helps build a bigger, more inclusive R community. | |
| LOCATION:https://global.rstudio.com/student/page/40619 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:b811ec7fe2e3e4375796bceab374c225 | |
| DTSTART:20210121T222228Z | |
| DTEND:20210121T223948Z | |
| SUMMARY:Teaching: On programming, teaching, and building interactive tutorials with learnr:: (Yanina Bellini Saibene) | |
| DESCRIPTION:Teaching R is part of my activities as a community organizer, an RStudio Certified Instructor, a conference chair, and a researcher. Since 2019, I use the learnr package to generate interactive tutorials to teach R synchronously and asynchronously. The addition of the Tutorials panel in RStudio IDE and the need for virtual classes made the use of this package even more interesting. In this talk, I will tell you how to generate interactive tutorials, how to add pedagogical tools to them, what other packages you can use with {learnr} and show multilingual examples. | |
| LOCATION:https://global.rstudio.com/student/page/40646 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:b362dadda174bb626e8b17e789ec9d2b | |
| DTSTART:20210121T223948Z | |
| DTEND:20210121T230000Z | |
| SUMMARY:Teaching: Discussion: Teaching 1 (*) | |
| DESCRIPTION:Join Desirée De Leon and Hasse Walum for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:d0b31a429df823531720369fdad5399b | |
| DTSTART:20210121T230000Z | |
| DTEND:20210121T232006Z | |
| SUMMARY:Teaching: Feedback at scale (Mine Çetinkaya-Rundel) | |
| DESCRIPTION:As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge -- providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash. | |
| LOCATION:https://global.rstudio.com/student/page/40630 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:33dea71fe5502505391389ee07058bca | |
| DTSTART:20210121T232006Z | |
| DTEND:20210121T233706Z | |
| SUMMARY:Teaching: How to do things with words: learning to program in R with a "communicative approach" (Riva Quiroga) | |
| DESCRIPTION:Textbooks for learning a new language always start the same: you learn to say hello, to introduce yourself, and some simple and useful sentences to communicate with others. In language teaching, this is called a “communicative approach”, and is based on the idea that learning a language successfully comes through having to communicate real meaning to real people. This is what I expected to find when I first tried to learn R seven years ago. Sadly, I got stuck in resources that started with definitions of abstract concepts and no real examples of how to say things with data. In this talk I will discuss the benefits of adopting a communicative approach and how to implement it when teaching/learning R, writing documentation, and writing code that will be read by other human beings. | |
| LOCATION:https://global.rstudio.com/student/page/40637 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:cb5546ecc59b93dafd481f35d8c8ba37 | |
| DTSTART:20210121T233706Z | |
| DTEND:20210122T000000Z | |
| SUMMARY:Teaching: Discussion: Teaching 2 (*) | |
| DESCRIPTION:Join Desirée De Leon and Hasse Walum for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9ab0bdd196c66652211d5498d7fb6367 | |
| DTSTART:20210121T220000Z | |
| DTEND:20210121T221940Z | |
| SUMMARY:Package dev: oRganization: How to make internal R packages part of your team (Emily Riederer) | |
| DESCRIPTION:Many case studies demonstrate the benefits of organizations developing internal R packages. But how do you move your organization from individual internal packages to a coherent internal ecosystem?This talk applies the jobs-to-be-done framework to consider the different roles that internal tools can play, from unblocking IT challenges to democratizing tribal knowledge. Beyond technical functionality, we will explore design principles and practice that make internal packages good teammates and consider how these deviate from open-source standards.Finally, we will consider how to exploit the unique challenges and opportunities of developing within an organization to make packages that collaborate well -- both with other packages and their human teammates. | |
| LOCATION:https://global.rstudio.com/student/page/40607 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:af7d198c4ad5f8882412248944a58c19 | |
| DTSTART:20210121T221940Z | |
| DTEND:20210121T223453Z | |
| SUMMARY:Package dev: You're Already Ready: Zen and the Art of R Package Development (Malcolm Barrett) | |
| DESCRIPTION:R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. When you write R packages, you also unlock a whole ecosystem of tools that will make it easier to test, document, and share your code. Despite these benefits, many believe package development is too advanced for them or that they have nothing to offer. A fundamental belief in Zen is that you are already complete, that you already have everything you need. I’ll talk about why your project is already an R package, why you’re already an R package developer, and why you already have the skills to walk the path of development. | |
| LOCATION:https://global.rstudio.com/student/page/40621 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:696364d3bc0d8544857e64cf81e7dcd7 | |
| DTSTART:20210121T223453Z | |
| DTEND:20210121T230000Z | |
| SUMMARY:Package dev: Discussion: Package dev 1 (*) | |
| DESCRIPTION:Join Rich Iannone and Daniel Sjoberg for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:395af7b407048666b2848352df76f9d5 | |
| DTSTART:20210121T230000Z | |
| DTEND:20210121T232020Z | |
| SUMMARY:Package dev: Monitoring health and impact of open-source projects (Jeroen Ooms) | |
| DESCRIPTION:At rOpenSci, we have come to realize that in order to help researchers get the most out of R, we need better tooling to monitor the quality, health, and impact of R packages. This applies both to our internal projects, as well as other packages in the R ecosystem. But what exactly makes a good R package?In this talk we discuss various aspects of open-source software that are not always immediately obvious, and that you may want to consider when depending on an R package. We identify several categories of indicators you could look for, ranging from the role in the dependency network, to expectations around maintenance and participation.Finally we introduce an ambitious new rOpenSci project called R-universe: an open platform, where we will experiment with showing metrics and other background information about packages, that may reveal something about the health and the impact of the project, and also facilitate discovery of other software. | |
| LOCATION:https://global.rstudio.com/student/page/40614 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:3928313709e2a17eae51468a3e9fe750 | |
| DTSTART:20210121T232020Z | |
| DTEND:20210121T232525Z | |
| SUMMARY:Package dev: Make a package - Make some friends (Athanasia M. Mowinckel) | |
| DESCRIPTION:In 2017, I had never exposed my code to anyone other than a select few before, and I was terrified. I had some functions made from a colleagues script that I thought might be useful for others, and dared myself to make a package and push it to github.In stead of the dreaded ridiculing of poor code and development, people embraced the package and helped us make it better. Within just a couple of days, pull requests came from others to help us improve the code, implement tests, and improve documentation. I learned so much just by looking through the PRs and seeing how others worked.Rather than make me shy off development, the R neuro community's positive feedback has helped me find a new interest and joy in developing tools. | |
| LOCATION:https://global.rstudio.com/student/page/40599 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:b1aea4056d3957e1304d722e7b484bf7 | |
| DTSTART:20210121T232525Z | |
| DTEND:20210121T233037Z | |
| SUMMARY:Package dev: Using formr to create R-powered surveys with individualized feedback (John Helveston) | |
| DESCRIPTION:This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students. | |
| LOCATION:https://global.rstudio.com/student/page/40616 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9bdddf1b09fb4341ffead98c284ad516 | |
| DTSTART:20210121T233037Z | |
| DTEND:20210121T233550Z | |
| SUMMARY:Package dev: Towards an integrated {verse}: lessons learned developing a library of validated packages (Marie Vendettuoli) | |
| DESCRIPTION:Developing R packages as a unified {verse} – a set of packages that work well together but with each focusing on individual tasks – is an efficient strategy to structure support for complex workflows. The ongoing challenge becomes managing the growth of related packages in a holistic manner. This is especially problematic in industries with a heavy emphasis on stability, for example if packages need to be validated prior to use in production. In this talk, I will discuss a paradigm for developing and maintaining validated R packages, emphasizing the following areas: (1) strategies for organizing packages to prevent excessive re-work (2) facilitating responsive, iterative development and (3) empathy for developer and user experiences. | |
| LOCATION:https://global.rstudio.com/student/page/40623 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:d57671b263450e369df513c1ccae374b | |
| DTSTART:20210121T233550Z | |
| DTEND:20210121T234052Z | |
| SUMMARY:Package dev: The Power of Great Datasets (Alex Cookson) | |
| DESCRIPTION:There are a few classic datasets, like mtcars, nycflights, or Titanic passengers. They're okay, but they leave something to be desired for folks learning R: they're kind of boring.There's a big difference between "Okay Datasets" and "Great Datasets". Great Datasets prompt you to exclaim, "That's so cool!" They get your blood pumping and mind racing with questions you want answered. They give tremendous motivation to answer those questions. And in answering those questions, you'll probably learn some R.I want you to curate Great Datasets. You'll contribute to the richness of our community, you'll learn some R yourself, and you'll feel fantastic when someone finds your Great Dataset and exclaims, "That's so cool!" | |
| LOCATION:https://global.rstudio.com/student/page/40596 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:a1fafbf3c15e15cafe25da6c590d792f | |
| DTSTART:20210121T234052Z | |
| DTEND:20210122T000000Z | |
| SUMMARY:Package dev: Discussion: Package dev 2 (*) | |
| DESCRIPTION:Join Rich Iannone and Daniel Sjoberg for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5d7017cb9c4bf83bca3dbf5a14d974c2 | |
| DTSTART:20210121T220000Z | |
| DTEND:20210121T221622Z | |
| SUMMARY:Modelling: What's new in tidymodels? (Max Kuhn) | |
| DESCRIPTION:tidymodels is a collection of packages for modeling using a tidy interface. In the last year there have been numerous improvements and extensions. This talk gives an overview of additional tuning methods, new extension packages for models and recipes, and other features. | |
| LOCATION:https://global.rstudio.com/student/page/40625 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:4a219c409af13a76010d5c77221a6dfb | |
| DTSTART:20210121T221622Z | |
| DTEND:20210121T222909Z | |
| SUMMARY:Modelling: Using R to Up Your Experimentation Game (Shirbi Ish-Shalom) | |
| DESCRIPTION:Have you ever cut an A/B test short? Maybe because of traffic constraints, your antsy boss, or early successful results. In reality, cutting your test short can be catastrophic, making your business decision no better than a coin flip. Learn some R-driven tips & tricks to get meaningful results quickly with a statistically rigorous methodology called sequential testing, an A/B testing enhancement my team employs at Intuit.Key Takeaways.1. What is sequential testing and how to use it.2. How to learn (and fail!) quickly by taking big metric swings3. How I used R to share my learnings & make them useful for anyone (even non-data scientists!) at my company. | |
| LOCATION:https://global.rstudio.com/student/page/40640 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:b4ce064cdae4cbe883d32c3d99f74432 | |
| DTSTART:20210121T222909Z | |
| DTEND:20210121T230000Z | |
| SUMMARY:Modelling: Discussion: Modelling 1 (*) | |
| DESCRIPTION:Join Julia Silge and Erin Ledell for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:128e9a0224712e9819779e5394342a04 | |
| DTSTART:20210121T230000Z | |
| DTEND:20210121T232349Z | |
| SUMMARY:Modelling: Fairness and Data Science: Failures, Factors, and Futures (Grant Fleming) | |
| DESCRIPTION:In recent years, numerous highly publicized failures in data science have made evident that biases or issues of fairness in training data can sneak into, and be magnified by, our models, leading to harmful, incorrect predictions being made once the models are deployed into the real world. But what actually constitutes an unfiar or biased model, and how can we diagnose and address these issues within our own work? In this talk, I will present a framework for better understanding how issues of fairness overlap with data science as well as how we can improve our modeling pipelines to make them more interpretable, reproducible, and fair to the groups that they are intended to serve. We will explore this new framework together through an analysis of ProPublica's COMPAS recidivism dataset using the tidymodels, drake, and iml packages. | |
| LOCATION:https://global.rstudio.com/student/page/40610 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f32984c65a6648afe7ac77ff56c61577 | |
| DTSTART:20210121T232349Z | |
| DTEND:20210121T232803Z | |
| SUMMARY:Modelling: tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling (Simon Couch) | |
| DESCRIPTION:Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. | |
| LOCATION:https://global.rstudio.com/student/page/40641 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c0bcec8be76e0070b50b95cd409fa53c | |
| DTSTART:20210121T232803Z | |
| DTEND:20210121T233314Z | |
| SUMMARY:Modelling: Using Guided Simulation Exercises to Teach Data Science with R (Chelsea Parlett-Pelleriti) | |
| DESCRIPTION:With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable. Guided simulation exercises in R allow learners to explore concepts deeply, on their own time, and with others. They can also experiment with the simulations, try out edge cases, and challenge their assumptions, leading to more fruitful discussions. The comparison between coefficient estimates in regular, LASSO, and RIDGE regression, or how PCA performs when data are related are great examples of concepts where guided simulations can encourage learners to build intuitive knowledge. This talk explores how to use simulation exercises in R to help learners explore DS concepts and provides examples. | |
| LOCATION:https://global.rstudio.com/student/page/40602 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:38c2fec5655fc3819525a91c70e56b4f | |
| DTSTART:20210121T233314Z | |
| DTEND:20210121T233820Z | |
| SUMMARY:Modelling: How I became a Data Composer – examples of simulated datasets that bring value to a data-driven company (Richard Vogg) | |
| DESCRIPTION:How can I get the buy-in from business partners to use more advanced techniques? What can I do to make a data project involving several teams more efficient? And how can I train analysts who do not (yet) have access to sensitive data?A good data composer is skilled at creating suitable data quickly and efficiently. R has many functions and packages that help with simulating independent variables and composing those in a meaningful way.In this talk, I will share how I started creating data and how this skill helped me with solving some of the issues described above. Showing a few examples – of small, medium-sized, and large data composition – I want to encourage attendees to simulate data and enrich their data skillset. | |
| LOCATION:https://global.rstudio.com/student/page/40635 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:8fcf982dc1ff200d483126e94d25b9f5 | |
| DTSTART:20210121T233820Z | |
| DTEND:20210121T234303Z | |
| SUMMARY:Modelling: Categorical Embeddings: New Ways to Simplify Complex Data (Alan Feder) | |
| DESCRIPTION:When building a predictive model in R, many of the functions (such as lm(), glm(), randomForest, xgboost, or neural networks in keras) require that all input variables are numeric. If your data has categorical variables, you may have to choose between ignoring some of your data and too many new columns.Categorical embeddings are a relative new method, utilizing methods popularized in Natural Language Processing that help models solve this problem and can help you understand more about the categories themselves.While there are a number of online tutorials on how to use Keras (usually in Python) to create these embeddings, this talk will use embed::step_embed(), an extension of the recipes package, to create the embeddings. | |
| LOCATION:https://global.rstudio.com/student/page/40595 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:966b596384988edeaca9acaf45384d24 | |
| DTSTART:20210121T234303Z | |
| DTEND:20210122T000000Z | |
| SUMMARY:Modelling: Discussion: Modelling 2 (*) | |
| DESCRIPTION:Join Julia Silge and Erin Ledell for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:b58644aae50a326329af1031e70b6f1e | |
| DTSTART:20210122T000000Z | |
| DTEND:20210122T002414Z | |
| SUMMARY:Keynote: Your public garden (Vicki Boykis) | |
| DESCRIPTION:Vicky will discuss how that as people who can write code and analyze data, we have a lot of input and power over what our digital and work worlds looks like, and therefore can act as agents of change and repair. | |
| LOCATION:https://global.rstudio.com/student/page/40643 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:402d6ef5b739868cc7933011baf84798 | |
| DTSTART:20210122T002414Z | |
| DTEND:20210122T012000Z | |
| SUMMARY:Keynote: Q&A: Vicki Boykis (*) | |
| DESCRIPTION:Join Jenny Bryan for audience Q&A with keynote speaker Vicki Boykis. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:dfd4495c4c703adcb31526b0fac5db70 | |
| DTSTART:20210122T020000Z | |
| DTEND:20210122T021845Z | |
| SUMMARY:Data for good: The Opioid Files: Turning big pharmacy data over to the public (Andrew Ba Tran) | |
| DESCRIPTION:Just because data is public doesn't mean it's accessible. It takes more effort, but designing data distribution so it can be analyzed by people with differing levels of data analysis skills opens up the possibility of more stories that can be told. This talk will go over how The Washington Post used R (and Python) to analyze hundreds of gigs of pain pill distribution data from the Drug Enforcement Administration as part of its investigation into the opioid epidemic. And how making the data public and showing their work enabled other journalists and researchers across the country to drill deeper than the Post could ever do on its own. | |
| LOCATION:https://global.rstudio.com/student/page/40598 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:8ea9eae82480fc4093919227ec798885 | |
| DTSTART:20210122T021845Z | |
| DTEND:20210122T022914Z | |
| SUMMARY:Data for good: rKenyaCensus Package (Shelmith Kariuki) | |
| DESCRIPTION:The rKenyaCensus package contains the results of the 2109 Kenya Population Census. The census exercise was carried out in August 2019, and the results were released in February 2020. Kenya leveraged on technology to capture data during cartographic mapping, enumeration and data transmission, making the 2019 Census the first paperless census to be conducted in Kenya.The data was published in four different pdf files (Volume 1 - Volume 4) which can be found in the Kenya National Bureau of statistics. The data in its current form was open and accessible, but not usable and so there was need to convert it into a machine readable format. This data can be used by the government, non-governmental organizations and any other entities for data driven policy making and development. During the talk, I will explain the reasons behind development of the package, take you through the steps I took during the process and finally showcase analysis of certain aspects of the data. | |
| LOCATION:https://global.rstudio.com/student/page/40639 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1f798405ceca897527ebaacce0a978ad | |
| DTSTART:20210122T022914Z | |
| DTEND:20210122T030000Z | |
| SUMMARY:Data for good: Discussion: Data for good 1 (*) | |
| DESCRIPTION:Join Tom Mock and Sharla Gelfand for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ea0941cdc2d76e4ab988c02c9543097b | |
| DTSTART:20210122T030000Z | |
| DTEND:20210122T031919Z | |
| SUMMARY:Data for good: Humanitarian Data Science with R (Ahmadou Dicko) | |
| DESCRIPTION:Humanitarian actors are increasingly using data to drive their decisions. Since the Haiti 2010 earthquake, the volume of data collected and used by humanitarians has been growing exponentially and organizations are now relying on data specialists to turn all this data into life-saving data products.These data products are created by teams using proprietary point and click software. The process from the raw data to the final data product involves a lot of clicking, copying and pasting and is usually not reproducible.Another approach to humanitarian data science is possible using R. In this talk, I will show how to seamlessly develop reproducible, reusable humanitarian data products using the tidyverse, rmarkdown and some domain-focused R packages. | |
| LOCATION:https://global.rstudio.com/student/page/40594 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c9adf862ef51c80abf2fd952441665a4 | |
| DTSTART:20210122T031919Z | |
| DTEND:20210122T033133Z | |
| SUMMARY:Data for good: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19 (Matt Thomas | |
| Mike Page) | |
| DESCRIPTION:We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon. | |
| LOCATION:https://global.rstudio.com/student/page/40624 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5205492406a80d337d8e3a4edf027a3f | |
| DTSTART:20210122T033133Z | |
| DTEND:20210122T040000Z | |
| SUMMARY:Data for good: Discussion: Data for good 2 (*) | |
| DESCRIPTION:Join Tom Mock and Sharla Gelfand for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:868e73fd7d2b06dbba4734a5071fa6b0 | |
| DTSTART:20210122T020000Z | |
| DTEND:20210122T022057Z | |
| SUMMARY:Organisational tooling: Not The App We Deserve. The App We Need: Putting a GMP Shiny App into Production (Marcus Adams) | |
| DESCRIPTION:In February 2020, the Digital Proactive Process Analytics (DPPA) group within Merck’s manufacturing division officially launched a Shiny app to automate the creation of Continuous Process Verification (CPV) reports into production. That’s right – the almighty, mysterious, coveted production. From a technical perspective, the app is nothing particularly special (except other than getting LaTeX successfully installed to support the use of R Markdown). Users enter a few parameters and out pops a PDF with a series statistical analyses of a product’s quality testing data. The R blogosphere is filled with examples of similar Shiny apps.What mattered was the app was in production, and furthermore it was approved for GMP use. This meant these reports could be submitted to the FDA and other regulatory agencies. This meant the data could be used to support product release decisions. This meant Merck’s engineers were about to save thousands of hours per year in compiling data, generating charts, and calculating summary statistics. This was the app manufacturing sites needed.Most of the work in getting this app into production was not implementing the top-level features. Sorry, no discussion of fancy statistical process control methods here. Instead this talk will discuss some of the many things the development team (none of which came from a software development background) needed to learn in order to create a robust, secure, and maintainable production application. | |
| LOCATION:https://global.rstudio.com/student/page/40622 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:0d42284d3866cbfa5cab939763021f5d | |
| DTSTART:20210122T022057Z | |
| DTEND:20210122T023340Z | |
| SUMMARY:Organisational tooling: How we made the switch: a case study on automating a complex report. (Eric Gunnar Cronstrom) | |
| DESCRIPTION:The Center for Charter Schools at Central Michigan University produces annual reports for about 60 schools. The reporting process used to be a cumbersome blend of many technologies. The Center used to use a blend of SQL, Excel, inDesign, and VBScript that would all culminate in a nice looking, branded report for each school. 2 years ago, staff turnover allowed the data team to rethink the process, having had experience in R from graduate work the team at the Center decided to go all in on R Studio and R Markdown for report production a mere 1 month before the reports were due to be published.This talk will be a case study of how we as an organization adopted RStudio tools to streamline a cumbersome process to fantastic results. | |
| LOCATION:https://global.rstudio.com/student/page/40608 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:834dc12c319e5440157519f54b6c2cd5 | |
| DTSTART:20210122T023340Z | |
| DTEND:20210122T030000Z | |
| SUMMARY:Organisational tooling: Discussion: Organisational tooling 1 (*) | |
| DESCRIPTION:Join Alex Gold and Elaine McVey for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:fd51ea93262f29a7a2d65a46a5944d9b | |
| DTSTART:20210122T030000Z | |
| DTEND:20210122T031734Z | |
| SUMMARY:Organisational tooling: From Zero to Hero: Best practices for setting up Rstudio Team in the Cloud (Rika) | |
| DESCRIPTION:Learn best practices for setting up the entire Rstudio team infrastructure - Server Pro, Connect, Package Manager from the perspective of a data scientist and for a data science audience - especially those who have never worked with servers, AWS, or bash. This talk will also be applicable to data scientists looking to start on an engineering project outside of Rstudio as well.I started out as a complete novice, & throughout my learning experience I noticed a distinct lack of resources for non-engineers. This talk will focus on best practices for AWS architecture and cloud formation, key security issues such as SSL and https, server configurations, deployment errors, and most importantly resources that are understandable for data scientists just getting into the data engineering or devops space. | |
| LOCATION:https://global.rstudio.com/student/page/40636 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c5a1b2c5d1f5b14ebb5e061e9ff83cf8 | |
| DTSTART:20210122T031734Z | |
| DTEND:20210122T032205Z | |
| SUMMARY:Organisational tooling: How reproducible am I? A retrospective on a year of commercial data science projects in R (Dean Marchiori) | |
| DESCRIPTION:Reproducibility is a critical aspect in science to enable trust & communication. In R, many tools exist to bring in the best practices of reproducibility into the hands of data scientists. However, outside of a research setting, how does reproducibility hold up in commercial data science projects? In this talk I take an honest retrospective of my own commercial R projects in the last year. I look at the various types of analyses completed, and which workflows were selected and why. Through this process we can learn how workflow choices may help in the short term but hinder in the long term. More importantly what can be done strike the balance between progress and perfection when doing data science in the wild? | |
| LOCATION:https://global.rstudio.com/student/page/40606 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7d482b245b82dbf182ea4fd427999fa4 | |
| DTSTART:20210122T032205Z | |
| DTEND:20210122T032613Z | |
| SUMMARY:Organisational tooling: Lifelong Learning with R Weekly (Wolfram King) | |
| DESCRIPTION:R Weekly is a weekly newsletter with many great R blogs post, tutorials, and other formats of resources. https://rweekly.orgR Weekly wants to keep track of these great things in the R community and make it more accessible to everyone.This is a warm and welcoming place. The team welcomes everyone who wants to contribute to the R community.In this talk I will cover these 6 topics:1. How to use the R Weekly website2. Why I created R Weekly3. How to Contribute to R Weekly4. How to release a new post5. How to join the team6. Learning from building the community | |
| LOCATION:https://global.rstudio.com/student/page/40645 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:39cb5829083c05c5c8ee7b569d012f03 | |
| DTSTART:20210122T032613Z | |
| DTEND:20210122T033117Z | |
| SUMMARY:Organisational tooling: Custom theming in Shiny & R Markdown with bslib & thematic (Carson Sievert) | |
| DESCRIPTION:Custom theming in Shiny and R Markdown often requires writing styling rules in both CSS and R. In particular, styles for HTML content (e.g., actionButton(), tabsetPanel(), titlePanel(), etc) derive from Bootstrap CSS, so customization is traditionally done by overwriting that CSS, which is difficult to do 100% correctly. The {bslib} package helps solve this problem by making it easy to customize (any version of) Bootstrap CSS defaults from R. However, this only solves part of the problem since CSS doesn't necessarily effect output(s) rendered by R, such as plotOutput(). The thematic package helps solve this problem by providing auto theming of plotOutput()s (based on CSS) as well as a simple interface for styling any R graphic for any output format. | |
| LOCATION:https://global.rstudio.com/student/page/40601 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:2ec8e280b0767148ed50753ed2edaaa9 | |
| DTSTART:20210122T033117Z | |
| DTEND:20210122T033620Z | |
| SUMMARY:Organisational tooling: How Content Makes the Data Go 'Round (Danielle Oberdier) | |
| DESCRIPTION:What makes a successful data science community thrive across industries? A recent Aflac WorkForces Report showed that professionals who are engaged in a community within their industry are 70% more likely to be satisfied with their work.I believe anyone can and should create content about data. In this talk, I will direct your attention towards 1) the ways that content creation can lead to heightened data science opportunities 2) how to know which type/s of content mediums (podcasts, blogs, video) are right for you 3) how to leverage social media and networking connections to make your content reach the right audiences. I hope to inspire listeners to create their own content and online brands as resources for fellow R community members. | |
| LOCATION:https://global.rstudio.com/student/page/40604 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:288e05d843397c32fe1c85d7bf2831e6 | |
| DTSTART:20210122T033620Z | |
| DTEND:20210122T040000Z | |
| SUMMARY:Organisational tooling: Discussion: Organisational tooling 2 (*) | |
| DESCRIPTION:Join Alex Gold and Elaine McVey for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7a8ca1fd33a186371ef63ded8ebf83ba | |
| DTSTART:20210122T020000Z | |
| DTEND:20210122T021814Z | |
| SUMMARY:Programming: Introducing xrprof: A New Way to Profile R (Aaron Jacobs) | |
| DESCRIPTION:Tracking down performance issues in R code usually means using R's built-in Rprof() profiler or one of the packages built around it. But the changing nature of the R community (towards more deployed applications) makes local profiling workflows frustrating, which is why I have written a new profiler: xrprof.xprof is compatible with existing R tools, but unlike them it can be used to profile R code that is already running -- in fact, it is designed to be safe to point at R code running "in production". xrprof also works seamlessly when R is run inside Docker, and can even be run in complex environments like Kubernetes clusters.Taking inspiration from the {jointprof} package, xrprof can also show function calls at the C/C++ level alongside those from R. This can be immensely useful for diagnosing problems in packages that make heavy use of compiled code. | |
| LOCATION:https://global.rstudio.com/student/page/40593 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:6a87ddb81b3e8c3cd94110beda2b1fbb | |
| DTSTART:20210122T021814Z | |
| DTEND:20210122T022632Z | |
| SUMMARY:Programming: plumber + future: Async Web APIs (Barret Schloerke) | |
| DESCRIPTION:plumber is an R package that allows users to create web APIs by decorating R functions using roxygen2-like comments. In the latest release, asynchronous code (using future or promises) may be inserted at any stage of a plumber route execution, enabling parallel processing using multiple workers. In this talk, I will go through how you can set up your own asynchronous plumber API to leverage your full computing potential. | |
| LOCATION:https://global.rstudio.com/student/page/40600 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:772dc262a6b58fd0eea692a92777630e | |
| DTSTART:20210122T022632Z | |
| DTEND:20210122T023144Z | |
| SUMMARY:Programming: Making Shiny apps faster with caching (Winston Chang) | |
| DESCRIPTION:Shiny's 1.6 has a new function, bindCache(), which makes it easy to dramatically speed up reactive expressions and output rendering functions. This allows many applications to scale up to serve several times more users without an increase in server resources. | |
| LOCATION:https://global.rstudio.com/student/page/40644 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:0b2644d9e0d494099d593164ed111d0c | |
| DTSTART:20210122T023144Z | |
| DTEND:20210122T030000Z | |
| SUMMARY:Programming: Discussion: Programming 1 (*) | |
| DESCRIPTION:Join Nick Strayer and Jasmine Daly for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5620137e31f44472a21a9e85139d949f | |
| DTSTART:20210122T030000Z | |
| DTEND:20210122T031955Z | |
| SUMMARY:Programming: Bigger Data With Ease Using Apache Arrow (Neal Richardson) | |
| DESCRIPTION:The Apache Arrow project enables data scientists using R, Python, and other languages to work with large datasets efficiently and with interactive speed. Arrow is so fast at some workflows that it seems to defy reality--or at least the limits of R's capabilities. This talk examines the unique characteristics of the Arrow project that enable it to redefine what is possible in R. The talk also highlights some of the latest developments in the arrow R package, including how you can query and manipulate multi-file datasets, and it presents strategies for speeding up workflows by up to 100x. | |
| LOCATION:https://global.rstudio.com/student/page/40631 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ab51b3f00228147042563574b2917fda | |
| DTSTART:20210122T031955Z | |
| DTEND:20210122T032422Z | |
| SUMMARY:Programming: Easy larger-than-RAM data manipulation with {disk.frame} (ZJ) | |
| DESCRIPTION:Learn how to handle 100GBs of data with ease using {disk.frame} - the larger-than-RAM-data manipulation package.R loads data in its entirety into RAM. However, RAM is a precious resource and often do run out. That's why most R user would have run into the "cannot allocate vector of size xxB." error at some point.However, the need to handle larger-than-RAM data doesn't go away just because RAM isn't large enough. So many useRs turn to big data tools like Spark for the task. In this talk, I will make the case that {disk.frame} is sufficient and often preferable for manipulating larger-than-RAM data that fit on disk. I will show how you can apply familiar {dplyr}-verbs to manipulate larger-than-RAM data with {disk.frame}. | |
| LOCATION:https://global.rstudio.com/student/page/40647 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:3d618a6d29c98eeb149a41d44eb7623b | |
| DTSTART:20210122T032422Z | |
| DTEND:20210122T032915Z | |
| SUMMARY:Programming: xaringan Playground: Using xaringan to learn web development (Garrick Aden-Buie) | |
| DESCRIPTION:xaringan is a quirky package that extends R Markdown to create beautiful web-based HTML slides. Some of xaringan’s quirks come from the JavaScript library it uses, remarkjs, and some of it from the unusual naming scheme xaringan uses for its functions. But under this quirky exterior lies a powerful tool for learning and practicing web development, especially when combined with infinite_moon_reader() for immediate feedback. In this talk I'll cover some basic web concepts that illustrate how fun and rewarding it can to learn HTML, CSS and JavaScript while building awesome slides in R Markdown. | |
| LOCATION:https://global.rstudio.com/student/page/40609 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:e4e52a0d591c959dc77499cfc75a1cdf | |
| DTSTART:20210122T032915Z | |
| DTEND:20210122T033429Z | |
| SUMMARY:Programming: parsermd - parsing R Markdown for fun and profit (Colin Rundel) | |
| DESCRIPTION:parsermd is a new R package for parsing and programmatically interacting with R Markdown (Rmd) documents. This package implements a formal grammar for Rmd documents in C++ using Boost's Spirit X3 library and provides additional user facing functions for the resulting abstract syntax tree. In this talk we will provide background on the structure and grammar of Rmd documents as well as discuss the ways in which the parsing of these documents enables a variety of automatable tasks. Specifically, we will focus on demonstrating how these tools can be used to provide automated feedback on student submissions in a statisical programming course. | |
| LOCATION:https://global.rstudio.com/student/page/40603 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ecda48701ec1b908f01f1cec28829984 | |
| DTSTART:20210122T033429Z | |
| DTEND:20210122T033937Z | |
| SUMMARY:Programming: Designing Randomized Studies using Shiny (Lucy D'Agostino McGowan) | |
| DESCRIPTION:This talk will walk through building a self-contained randomized study using Shiny and learnr modules. We will discuss building informed consent, the randomization process, demographic surveys, and R-based studies into a single online framework to allow users to seamlessly enroll and participate in randomized studies via a single URL. The talk will include both practical recommendations as well as technical code snippets. | |
| LOCATION:https://global.rstudio.com/student/page/40620 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7f9fed740e2b152098cfb0a339cb7f35 | |
| DTSTART:20210122T033937Z | |
| DTEND:20210122T040000Z | |
| SUMMARY:Programming: Discussion: Programming 2 (*) | |
| DESCRIPTION:Join Nick Strayer and Jasmine Daly for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f747fc84119a6524c7f3962b72d80f29 | |
| DTSTART:20210122T040000Z | |
| DTEND:20210122T043300Z | |
| SUMMARY:Keynote: Maintaining the house the tidyverse built (Hadley Wickham) | |
| DESCRIPTION:Hadley will talk about how the tidyverse has evolved since its creation (just five years ago!). You'll learn about our greatest successes, learn from our biggest failures, and get some hints of what's coming down the pipeline for the future. | |
| LOCATION:https://global.rstudio.com/student/page/40521 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:950cba50b6bcefa442116c5c3e3f6f31 | |
| DTSTART:20210122T043300Z | |
| DTEND:20210122T052000Z | |
| SUMMARY:Keynote: Q&A: Hadley Wickham (*) | |
| DESCRIPTION:Join Andrie de Vries for audience Q&A with keynote speaker Hadley Wickham. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:8a446aad91a08c1230e91506968cc9b8 | |
| DTSTART:20210122T060000Z | |
| DTEND:20210122T062156Z | |
| SUMMARY:Learning: Always look on the bright side of plots (Kara Woo) | |
| DESCRIPTION:Everyone who creates visualizations in R is bound to make mistakes that prevent their plots from looking as they should. Sometimes, these mistakes create beautiful "accidental aRt", though other times they're just plain frustrating. Either way, however, there's something to be learned. This talk will draw on years of watching both the ggplot2 issue tracker and the @accidental__aRt twitter account to highlight some common plot foibles and explain what they can teach us about how ggplot2 works. | |
| LOCATION:https://global.rstudio.com/student/page/40618 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:d1fb3363c6d3c9fe28161a18dc82dfcc | |
| DTSTART:20210122T062156Z | |
| DTEND:20210122T062902Z | |
| SUMMARY:Learning: Starting an R Book Club: Cooking Up Friendships in Isolation (Maya Gans) | |
| DESCRIPTION:Amidst a global pandemic there’s been one consistency in my life: every Tuesday a group of friends meet to discuss Hadley Wickham’s Advanced R. I crowdsourced interest using the R4DS Slack and the results were magical: a group of incredibly curious and generous people motivated to learn and teach one another emerged. The meetings evolved from a group of strangers giving timid presentations to a safe space where we share and improve upon personal applications. The 1 club has grown to 3 regional cohorts, and became a model for discussing other books too. This talk will go over the structure of our meetings in hopes of empowering others to start their own book clubs, showcasing a different way people can create and engage in communities. | |
| LOCATION:https://global.rstudio.com/student/page/40626 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5ce716e6228c56a26c0508b280781f3a | |
| DTSTART:20210122T062902Z | |
| DTEND:20210122T070000Z | |
| SUMMARY:Learning: Discussion: Learning 1 (*) | |
| DESCRIPTION:Join Rob Hyndman and Emi Tanaka for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f769ca88b0b6010b513cec93483d7df5 | |
| DTSTART:20210122T070000Z | |
| DTEND:20210122T071932Z | |
| SUMMARY:Learning: Art Lessons: One Year as RStudio’s Artist-in-Residence (Allison Horst) | |
| DESCRIPTION:Art can be a welcoming bridge for learners and users to engage with and learn tools and skills in R. As RStudio’s first Artist-in-Residence, my goal has been to make the R landscape more welcoming for a broader community of users through engaging, didactic artwork. In this R, art, and heart-filled talk, I’ll share the motivation behind my R artwork and some lessons learned over the past year as Artist-in-Residence, including: - Learning to embrace cute and credible artwork - Art to help students engage with, learn and remember R skills - Art for community building and supportI hope this talk inspires viewers to use, create and share more artwork, so that together we can make the R landscape feel even brighter. | |
| LOCATION:https://global.rstudio.com/student/page/40597 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9f4648117fd566b9beebd09a5c48f5e9 | |
| DTSTART:20210122T071932Z | |
| DTEND:20210122T073818Z | |
| SUMMARY:Learning: Aesthetically automated figure production (Megan Beckett) | |
| DESCRIPTION:Automation, reproducibility, data driven. These are not normally concepts one would associate with the traditional publishing industry, where designers normally manually produce every artefact in proprietary software. And, when you have 1000s of figures to produce and update for a single textbook, this becomes an insurmountable task, meaning our textbooks quickly become outdated, especially in our rapidly advancing world.With R and the tidyverse in our back pocket, we rose to the challenge to revolutionize this workflow. I will explain how we collaborated with a publishing group to develop a system to aesthetically automate the production of figures for a textbook including translations into several languages.I think you’ll find this talk interesting as it shows how we applied tools that are familiar to us, but in an unconventional way to fundamentally transform a conventional process. | |
| LOCATION:https://global.rstudio.com/student/page/40627 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:72df7f455b6a9e0329599214b2ac6f26 | |
| DTSTART:20210122T073818Z | |
| DTEND:20210122T080000Z | |
| SUMMARY:Learning: Discussion: Learning 2 (*) | |
| DESCRIPTION:Join Rob Hyndman and Emi Tanaka for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1ef9840ce53ca03a220bad10cfd27acf | |
| DTSTART:20210122T060000Z | |
| DTEND:20210122T061917Z | |
| SUMMARY:Language interop: Bringing the Tidyverse to Python with Siuba (Michael Chow) | |
| DESCRIPTION:Last January I left my job to spend a year developing siuba, a python port of dplyr. At its core, this decision was driven by a decade of watching python and R users produce similar analyses, but in very different ways.In this talk, I'll discuss 3 ways siuba enables R users to transfer their hard-earned programming knowledge to python: (1) leveraging the power of dplyr syntax, (2) options to generate SQL code, and (3) working with the plotnine plotting library.Looking back, I'll consider two critical pieces that have helped me develop siuba: using it to livecode TidyTuesday analyses, and building an interactive tutorial for absolute beginners. | |
| LOCATION:https://global.rstudio.com/student/page/40629 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1f9b94295e70ff86b94f203af0710e7c | |
| DTSTART:20210122T061917Z | |
| DTEND:20210122T063432Z | |
| SUMMARY:Language interop: R & Python: Going Steady (Sean Lopp) | |
| DESCRIPTION:While there has been a lot of excitement about the R and Python love story, there are still misconceptions that individuals, teams, or organizations must pick between R or Python. This talk will explain why this false choice exists, debunk the myths that cause teams to be stuck with only one tool, and clarify how data scientists can use both languages to be more effective. We will explore this love story's blossoming relationship by looking at updates to RStudio's packages and products that make it easier to develop and collaborate in R and Python. This talk is for individuals who want to uncover the benefits of multilingual data science, IT professionals who are skeptical their life can get better by supporting more languages, and data science managers interested in enabling their teams instead of forcing their data superheros to be subservient to particular tools. | |
| LOCATION:https://global.rstudio.com/student/page/40638 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:41448e06fcf9c57c1b26ab70e915f772 | |
| DTSTART:20210122T063432Z | |
| DTEND:20210122T070000Z | |
| SUMMARY:Language interop: Discussion: Language interop 1 (*) | |
| DESCRIPTION:Join Earo Wang and Stuart Lee for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:abd7523707d6df81bc636b209db673c1 | |
| DTSTART:20210122T070000Z | |
| DTEND:20210122T071924Z | |
| SUMMARY:Language interop: Using pins with Python and JavaScript (Javier Luraschi) | |
| DESCRIPTION:Last year, pins got released as a brand new R package to pin, discover and cache remote resources for R users. This package has matured to support many use cases; from caching remote URLs, and easily sharing datasets with other R users, to building automated pipelines.However, in order to truly collaborate in multi-disciplinary data-driven teams, one needs to consider how to collaborate beyond R? How can we share resources with designers and machine learning experts who happen to use different programming languages like Python and JavaScript?This talk will introduce the pinsjs project, a cross-language community project which has the goal of bringing pins to the broader open source community to enable rich workflows across larger data-driven teams. | |
| LOCATION:https://global.rstudio.com/student/page/40613 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:65cd5c11967feed1ce9c078fedda1969 | |
| DTSTART:20210122T071924Z | |
| DTEND:20210122T073723Z | |
| SUMMARY:Language interop: The dynamic duo: SQL & R (Irene Steves) | |
| DESCRIPTION:There's a point in every data wranglers' career in which their full dataset can no longer fit into just CSV files, and the journey to database-world begins. I reached this point about two years ago, when I transitioned from ecological research to the world of eCommerce fraud prevention. My calls to read_csv became scarcer as I came to rely more and more on databases. In this talk, I'll demonstrate how I use R and SQL to access database tables, and how I incorporate both into my daily workflow, aided by features in RStudio IDE. I'll also discuss our company’s "riskiconn" package for handling database connections and queries, which includes customizations to simplify day-to-day data querying. | |
| LOCATION:https://global.rstudio.com/student/page/40612 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:fd2f5174f0fbf79f8a049cc779059838 | |
| DTSTART:20210122T073723Z | |
| DTEND:20210122T080000Z | |
| SUMMARY:Language interop: Discussion: Language interop 2 (*) | |
| DESCRIPTION:Join Earo Wang and Stuart Lee for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ba35d1559f116f9db9b7a38e0d806bef | |
| DTSTART:20210122T060000Z | |
| DTEND:20210122T061934Z | |
| SUMMARY:Visualisation: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages (JooYoung Seo) | |
| DESCRIPTION:Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models.However, data science itself should require insight and computational thinking beyond what is just seen by eyes.JooYoung Seo, who is a blind data scientist and who was working for RStudio's accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data.If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking. | |
| LOCATION:https://global.rstudio.com/student/page/40617 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:2be9b4868c9155825fc2b32b1bbb1fbc | |
| DTSTART:20210122T061934Z | |
| DTEND:20210122T063342Z | |
| SUMMARY:Visualisation: A New Paradigm for Multifigure, Coordinate-Based Plotting in R (Nicole Kramer) | |
| DESCRIPTION:R is unparalleled in its ability to transform raw data into a wide array of beautiful graphics, all within the same environment. However, when it comes to complex, multi-paneled plots, users rely on 3rd party graphic design software to arrange plots. Here I present the new world of programmatic, coordinate-based multi-figure plotting in R. Employing grid Graphics and drawing from the paradigms of base plotting and ggplot2, I am developing a package that will revolutionize the way plots are laid out in R. Not only will individual plots be aesthetically customizable and tailored for speed, users will also be offered exquisite control over all aspects of page layout, plot placement, and arrangements. Come join me in changing how we plot in R! | |
| LOCATION:https://global.rstudio.com/student/page/40633 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:e711fa726bb43c99630e44007c4394bc | |
| DTSTART:20210122T063342Z | |
| DTEND:20210122T070000Z | |
| SUMMARY:Visualisation: Discussion: Visualisation 1 (*) | |
| DESCRIPTION:Join Di Cook and Ursula Laa for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9f381a50eafbf78e27fd69446f3d3c44 | |
| DTSTART:20210122T070000Z | |
| DTEND:20210122T071600Z | |
| SUMMARY:Visualisation: Trial and Error in Data Viz at the ACLU (Sophie Beiers) | |
| DESCRIPTION:Visualizing data the “right” way requires many considerations — the topic, the quality of your data, your audience, your time frame, and the various channels of (sometimes conflicting) feedback you received. In this presentation, I’ll introduce some reflections on these considerations and ways I’ve incorporated feedback (or not) into my work as Data Journalist at the ACLU. Lastly, I’ll present some of the sillier trials and errors I’ve made that were arguably necessary to my process in creating effective data visualizations using R. | |
| LOCATION:https://global.rstudio.com/student/page/40642 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1c7e62ae2be6ff79b4df794a5e82a6be | |
| DTSTART:20210122T071600Z | |
| DTEND:20210122T072045Z | |
| SUMMARY:Visualisation: Racial Equity Dashboard: Unpacking Systemic Inequity (Nicholas Pylypiw) | |
| DESCRIPTION:At Cape Fear Collective, we’re redefining what a town square looks like in our community, serving as a place where all people, organizations, and ideas can come together to effect real, lasting, and systemic change. By merging cutting edge data science with an emphasis on equity and the lived experience of our most marginalized communities, Cape Fear Collective supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. This talk is about how we bring that model to life through our Racial Equity Dashboard, from data sourcing, to modeling and, ultimately, action. | |
| LOCATION:https://global.rstudio.com/student/page/40632 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:0acc6a32a21d38c24ba7089f30870418 | |
| DTSTART:20210122T072045Z | |
| DTEND:20210122T072540Z | |
| SUMMARY:Visualisation: Your R is My R too: Reflections on creating the Mi-R community (Danielle Smalls-Perkins | |
| Dorris Scott) | |
| DESCRIPTION:While the R community has made strides in increasing the representation and participation for women and users from underrepresented regions, there are still members of the R community that have expressed desires for a more inclusive space in addition to these strides. In addition, there are unique challenges that underrepresented R users experience in their respective workspaces or academic environments. In late February of 2020, Danielle Smalls-Perkins and Dorris Scott created Mi-R (Minorities in R) as a result of their various experiences both in and outside the R community. The purpose of this talk is to reflect on the challenges, highlights, and future directions of the first six months since the creation of Mi-R. | |
| LOCATION:https://global.rstudio.com/student/page/40605 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:91cf016b54904a251d99a3046fe9d470 | |
| DTSTART:20210122T072540Z | |
| DTEND:20210122T073040Z | |
| SUMMARY:Visualisation: An easy and friendly way to build your multilingual website (Pamela E. Pairo) | |
| DESCRIPTION:Having a personal website is a great way to share our experiences with other people, that also allows us to improve our communication skills and expand our networking groups. Besides, if the website is multilingual, the scope will be extended considerably by facilitating the exchange of ideas. I will give the key steps, some tips, and important considerations to bear in mind when creating a multilingual website using Blogdown, Hugo, and Netlify. Although having a multilingual website demands more effort, R enables us to build a website easily and keep it updated. I aim to help and encourage others to build their website to promote exchange experiences among people from different native languages. | |
| LOCATION:https://global.rstudio.com/student/page/40634 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:74d7a0a7c38142b50cb81cc97ca520b6 | |
| DTSTART:20210122T073040Z | |
| DTEND:20210122T073522Z | |
| SUMMARY:Visualisation: Making .pot-ery with R: Translations in R Packages (Michael Chirico) | |
| DESCRIPTION:The R community is globally distributed and R itself is available with messages in 14 languages. Adding translations for non-native English-speaking users of your package can ease their experience and empower them to build better things with less frustration (though please note that "object of type 'closure' is not subsettable" is equally inscrutable in all human languages).In this talk, I will cover translations in R packages -- how to implement them, why to do so, and how to maintain them. This will summarize and extend learnings based on our experience adding Mandarin translations to data.table and culminating in the potools package. | |
| LOCATION:https://global.rstudio.com/student/page/40628 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:2552778abbd7db00002ae51367025500 | |
| DTSTART:20210122T073522Z | |
| DTEND:20210122T080000Z | |
| SUMMARY:Visualisation: Discussion: Visualisation 2 (*) | |
| DESCRIPTION:Join Di Cook and Ursula Laa for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:3a67dbeef735581909c6add48d415f01 | |
| DTSTART:20210122T080000Z | |
| DTEND:20210122T085011Z | |
| SUMMARY:Keynote: Reporting on and visualising the pandemic (John Burn-Murdoch) | |
| DESCRIPTION:John will discuss the lessons he's learned reporting on and visualising the pandemic, including the world of difference between making charts for a technical audience and making charts for a mass audience. You'll learn from his experience navigating the highly personal and political context within which people consume and evaluate graphics and data, and how that can help us better design and communicate with visualisations down the pipeline for the future. | |
| LOCATION:https://global.rstudio.com/student/page/40615 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1f5b9326ada73070e660a91b032cc6a7 | |
| DTSTART:20210122T085011Z | |
| DTEND:20210122T092000Z | |
| SUMMARY:Keynote: Q&A: John Burn-Murdoch (*) | |
| DESCRIPTION:Join Andrie de Vries for audience Q&A with keynote speaker John Burn-Murdoch. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f92ce6bc26888d55e5b8c18f21d49f15 | |
| DTSTART:20210122T100000Z | |
| DTEND:20210122T102228Z | |
| SUMMARY:Teaching: Making the jump from learning to applying: R training and documentation for different levels of expertise (Kate Hertweck) | |
| DESCRIPTION:How does someone make the leap from learning R to actively applying R in professional work?At what point (if ever!) do we get to call ourselves "experts" in R?This talk explores what differentiates novice, practitioner, and expert R programmers,and how transitionsbetween these stages occur.I'll discuss the type of support required for R users to move from one level of expertise to the next,and how different types of training and documentation can support R users at each level.Understanding variable levels of education among R practitioners supports our own professional work,from collaborative coding to package development,and helps build a bigger, more inclusive R community. | |
| LOCATION:https://global.rstudio.com/student/page/40619 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:93402fdfc1d73152b4223f4fb9791755 | |
| DTSTART:20210122T102228Z | |
| DTEND:20210122T103948Z | |
| SUMMARY:Teaching: On programming, teaching, and building interactive tutorials with learnr:: (Yanina Bellini Saibene) | |
| DESCRIPTION:Teaching R is part of my activities as a community organizer, an RStudio Certified Instructor, a conference chair, and a researcher. Since 2019, I use the learnr package to generate interactive tutorials to teach R synchronously and asynchronously. The addition of the Tutorials panel in RStudio IDE and the need for virtual classes made the use of this package even more interesting. In this talk, I will tell you how to generate interactive tutorials, how to add pedagogical tools to them, what other packages you can use with {learnr} and show multilingual examples. | |
| LOCATION:https://global.rstudio.com/student/page/40646 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ab81955c00fd619fd0773d287eac901b | |
| DTSTART:20210122T103948Z | |
| DTEND:20210122T110000Z | |
| SUMMARY:Teaching: Discussion: Teaching 1 (*) | |
| DESCRIPTION:Join Maelle Salmon and Vebashini Naidoo for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:9e47d3bab017779271796c3f882ce14a | |
| DTSTART:20210122T110000Z | |
| DTEND:20210122T112006Z | |
| SUMMARY:Teaching: Feedback at scale (Mine Çetinkaya-Rundel) | |
| DESCRIPTION:As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge -- providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash. | |
| LOCATION:https://global.rstudio.com/student/page/40630 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:8982f6bc54c76d4f45e9ae008b5f6c44 | |
| DTSTART:20210122T112006Z | |
| DTEND:20210122T113706Z | |
| SUMMARY:Teaching: How to do things with words: learning to program in R with a "communicative approach" (Riva Quiroga) | |
| DESCRIPTION:Textbooks for learning a new language always start the same: you learn to say hello, to introduce yourself, and some simple and useful sentences to communicate with others. In language teaching, this is called a “communicative approach”, and is based on the idea that learning a language successfully comes through having to communicate real meaning to real people. This is what I expected to find when I first tried to learn R seven years ago. Sadly, I got stuck in resources that started with definitions of abstract concepts and no real examples of how to say things with data. In this talk I will discuss the benefits of adopting a communicative approach and how to implement it when teaching/learning R, writing documentation, and writing code that will be read by other human beings. | |
| LOCATION:https://global.rstudio.com/student/page/40637 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1e3cf9e077fdb8f1ff00bc52d2edf354 | |
| DTSTART:20210122T113706Z | |
| DTEND:20210122T120000Z | |
| SUMMARY:Teaching: Discussion: Teaching 2 (*) | |
| DESCRIPTION:Join Maelle Salmon and Vebashini Naidoo for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:a2940a9325f624f1a39f5b08064ca6ef | |
| DTSTART:20210122T100000Z | |
| DTEND:20210122T101940Z | |
| SUMMARY:Package dev: oRganization: How to make internal R packages part of your team (Emily Riederer) | |
| DESCRIPTION:Many case studies demonstrate the benefits of organizations developing internal R packages. But how do you move your organization from individual internal packages to a coherent internal ecosystem?This talk applies the jobs-to-be-done framework to consider the different roles that internal tools can play, from unblocking IT challenges to democratizing tribal knowledge. Beyond technical functionality, we will explore design principles and practice that make internal packages good teammates and consider how these deviate from open-source standards.Finally, we will consider how to exploit the unique challenges and opportunities of developing within an organization to make packages that collaborate well -- both with other packages and their human teammates. | |
| LOCATION:https://global.rstudio.com/student/page/40607 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c6e47e5fa772c7bade51188867a09f8b | |
| DTSTART:20210122T101940Z | |
| DTEND:20210122T103453Z | |
| SUMMARY:Package dev: You're Already Ready: Zen and the Art of R Package Development (Malcolm Barrett) | |
| DESCRIPTION:R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. When you write R packages, you also unlock a whole ecosystem of tools that will make it easier to test, document, and share your code. Despite these benefits, many believe package development is too advanced for them or that they have nothing to offer. A fundamental belief in Zen is that you are already complete, that you already have everything you need. I’ll talk about why your project is already an R package, why you’re already an R package developer, and why you already have the skills to walk the path of development. | |
| LOCATION:https://global.rstudio.com/student/page/40621 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:6381ed2e39290888ac7b858fa6fc78a6 | |
| DTSTART:20210122T103453Z | |
| DTEND:20210122T110000Z | |
| SUMMARY:Package dev: Discussion: Package dev 1 (*) | |
| DESCRIPTION:Join Colin Fay and Ahmadou Dicko for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:bba098414687be81e11ed8a04f77a12e | |
| DTSTART:20210122T110000Z | |
| DTEND:20210122T112020Z | |
| SUMMARY:Package dev: Monitoring health and impact of open-source projects (Jeroen Ooms) | |
| DESCRIPTION:At rOpenSci, we have come to realize that in order to help researchers get the most out of R, we need better tooling to monitor the quality, health, and impact of R packages. This applies both to our internal projects, as well as other packages in the R ecosystem. But what exactly makes a good R package?In this talk we discuss various aspects of open-source software that are not always immediately obvious, and that you may want to consider when depending on an R package. We identify several categories of indicators you could look for, ranging from the role in the dependency network, to expectations around maintenance and participation.Finally we introduce an ambitious new rOpenSci project called R-universe: an open platform, where we will experiment with showing metrics and other background information about packages, that may reveal something about the health and the impact of the project, and also facilitate discovery of other software. | |
| LOCATION:https://global.rstudio.com/student/page/40614 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c37ef4c3464de367bfb191d1a79afd7f | |
| DTSTART:20210122T112020Z | |
| DTEND:20210122T112525Z | |
| SUMMARY:Package dev: Make a package - Make some friends (Athanasia M. Mowinckel) | |
| DESCRIPTION:In 2017, I had never exposed my code to anyone other than a select few before, and I was terrified. I had some functions made from a colleagues script that I thought might be useful for others, and dared myself to make a package and push it to github.In stead of the dreaded ridiculing of poor code and development, people embraced the package and helped us make it better. Within just a couple of days, pull requests came from others to help us improve the code, implement tests, and improve documentation. I learned so much just by looking through the PRs and seeing how others worked.Rather than make me shy off development, the R neuro community's positive feedback has helped me find a new interest and joy in developing tools. | |
| LOCATION:https://global.rstudio.com/student/page/40599 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:0632ff69cef95635765d5ba89a60f5a7 | |
| DTSTART:20210122T112525Z | |
| DTEND:20210122T113037Z | |
| SUMMARY:Package dev: Using formr to create R-powered surveys with individualized feedback (John Helveston) | |
| DESCRIPTION:This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students. | |
| LOCATION:https://global.rstudio.com/student/page/40616 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:e3ea731b722bf285cc09852005e0807a | |
| DTSTART:20210122T113037Z | |
| DTEND:20210122T113550Z | |
| SUMMARY:Package dev: Towards an integrated {verse}: lessons learned developing a library of validated packages (Marie Vendettuoli) | |
| DESCRIPTION:Developing R packages as a unified {verse} – a set of packages that work well together but with each focusing on individual tasks – is an efficient strategy to structure support for complex workflows. The ongoing challenge becomes managing the growth of related packages in a holistic manner. This is especially problematic in industries with a heavy emphasis on stability, for example if packages need to be validated prior to use in production. In this talk, I will discuss a paradigm for developing and maintaining validated R packages, emphasizing the following areas: (1) strategies for organizing packages to prevent excessive re-work (2) facilitating responsive, iterative development and (3) empathy for developer and user experiences. | |
| LOCATION:https://global.rstudio.com/student/page/40623 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:820f15313986424159538dc338c62fd0 | |
| DTSTART:20210122T113550Z | |
| DTEND:20210122T114052Z | |
| SUMMARY:Package dev: The Power of Great Datasets (Alex Cookson) | |
| DESCRIPTION:There are a few classic datasets, like mtcars, nycflights, or Titanic passengers. They're okay, but they leave something to be desired for folks learning R: they're kind of boring.There's a big difference between "Okay Datasets" and "Great Datasets". Great Datasets prompt you to exclaim, "That's so cool!" They get your blood pumping and mind racing with questions you want answered. They give tremendous motivation to answer those questions. And in answering those questions, you'll probably learn some R.I want you to curate Great Datasets. You'll contribute to the richness of our community, you'll learn some R yourself, and you'll feel fantastic when someone finds your Great Dataset and exclaims, "That's so cool!" | |
| LOCATION:https://global.rstudio.com/student/page/40596 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c6270a41a5ba5b88488d68af747e9648 | |
| DTSTART:20210122T114052Z | |
| DTEND:20210122T120000Z | |
| SUMMARY:Package dev: Discussion: Package dev 2 (*) | |
| DESCRIPTION:Join Colin Fay and Ahmadou Dicko for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f962305ab540f1b07455981daa04850e | |
| DTSTART:20210122T100000Z | |
| DTEND:20210122T101622Z | |
| SUMMARY:Modelling: What's new in tidymodels? (Max Kuhn) | |
| DESCRIPTION:tidymodels is a collection of packages for modeling using a tidy interface. In the last year there have been numerous improvements and extensions. This talk gives an overview of additional tuning methods, new extension packages for models and recipes, and other features. | |
| LOCATION:https://global.rstudio.com/student/page/40625 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f384dc956b9cc71c9ccf3bc0d8f3a69f | |
| DTSTART:20210122T101622Z | |
| DTEND:20210122T102909Z | |
| SUMMARY:Modelling: Using R to Up Your Experimentation Game (Shirbi Ish-Shalom) | |
| DESCRIPTION:Have you ever cut an A/B test short? Maybe because of traffic constraints, your antsy boss, or early successful results. In reality, cutting your test short can be catastrophic, making your business decision no better than a coin flip. Learn some R-driven tips & tricks to get meaningful results quickly with a statistically rigorous methodology called sequential testing, an A/B testing enhancement my team employs at Intuit.Key Takeaways.1. What is sequential testing and how to use it.2. How to learn (and fail!) quickly by taking big metric swings3. How I used R to share my learnings & make them useful for anyone (even non-data scientists!) at my company. | |
| LOCATION:https://global.rstudio.com/student/page/40640 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5a2ec2403ca06635fe6e88fed307ffae | |
| DTSTART:20210122T102909Z | |
| DTEND:20210122T110000Z | |
| SUMMARY:Modelling: Discussion: Modelling 1 (*) | |
| DESCRIPTION:Join Hannah Frick and Bruna Wundervald for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:281214affcf66d04e9ca697d1dc99761 | |
| DTSTART:20210122T110000Z | |
| DTEND:20210122T112349Z | |
| SUMMARY:Modelling: Fairness and Data Science: Failures, Factors, and Futures (Grant Fleming) | |
| DESCRIPTION:In recent years, numerous highly publicized failures in data science have made evident that biases or issues of fairness in training data can sneak into, and be magnified by, our models, leading to harmful, incorrect predictions being made once the models are deployed into the real world. But what actually constitutes an unfiar or biased model, and how can we diagnose and address these issues within our own work? In this talk, I will present a framework for better understanding how issues of fairness overlap with data science as well as how we can improve our modeling pipelines to make them more interpretable, reproducible, and fair to the groups that they are intended to serve. We will explore this new framework together through an analysis of ProPublica's COMPAS recidivism dataset using the tidymodels, drake, and iml packages. | |
| LOCATION:https://global.rstudio.com/student/page/40610 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7b86c3624d03f37a803f88a9ac26abea | |
| DTSTART:20210122T112349Z | |
| DTEND:20210122T112803Z | |
| SUMMARY:Modelling: tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling (Simon Couch) | |
| DESCRIPTION:Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. | |
| LOCATION:https://global.rstudio.com/student/page/40641 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1c9fcc0443e357376408e8b2629e07a6 | |
| DTSTART:20210122T112803Z | |
| DTEND:20210122T113314Z | |
| SUMMARY:Modelling: Using Guided Simulation Exercises to Teach Data Science with R (Chelsea Parlett-Pelleriti) | |
| DESCRIPTION:With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable. Guided simulation exercises in R allow learners to explore concepts deeply, on their own time, and with others. They can also experiment with the simulations, try out edge cases, and challenge their assumptions, leading to more fruitful discussions. The comparison between coefficient estimates in regular, LASSO, and RIDGE regression, or how PCA performs when data are related are great examples of concepts where guided simulations can encourage learners to build intuitive knowledge. This talk explores how to use simulation exercises in R to help learners explore DS concepts and provides examples. | |
| LOCATION:https://global.rstudio.com/student/page/40602 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:89137660f8c37c8fb943fc795a570ebc | |
| DTSTART:20210122T113314Z | |
| DTEND:20210122T113820Z | |
| SUMMARY:Modelling: How I became a Data Composer – examples of simulated datasets that bring value to a data-driven company (Richard Vogg) | |
| DESCRIPTION:How can I get the buy-in from business partners to use more advanced techniques? What can I do to make a data project involving several teams more efficient? And how can I train analysts who do not (yet) have access to sensitive data?A good data composer is skilled at creating suitable data quickly and efficiently. R has many functions and packages that help with simulating independent variables and composing those in a meaningful way.In this talk, I will share how I started creating data and how this skill helped me with solving some of the issues described above. Showing a few examples – of small, medium-sized, and large data composition – I want to encourage attendees to simulate data and enrich their data skillset. | |
| LOCATION:https://global.rstudio.com/student/page/40635 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:d101c01ffa2bd217849ef9f82e8d0648 | |
| DTSTART:20210122T113820Z | |
| DTEND:20210122T114303Z | |
| SUMMARY:Modelling: Categorical Embeddings: New Ways to Simplify Complex Data (Alan Feder) | |
| DESCRIPTION:When building a predictive model in R, many of the functions (such as lm(), glm(), randomForest, xgboost, or neural networks in keras) require that all input variables are numeric. If your data has categorical variables, you may have to choose between ignoring some of your data and too many new columns.Categorical embeddings are a relative new method, utilizing methods popularized in Natural Language Processing that help models solve this problem and can help you understand more about the categories themselves.While there are a number of online tutorials on how to use Keras (usually in Python) to create these embeddings, this talk will use embed::step_embed(), an extension of the recipes package, to create the embeddings. | |
| LOCATION:https://global.rstudio.com/student/page/40595 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:fd5bc8fe9319b9c624e238f0a50bf513 | |
| DTSTART:20210122T114303Z | |
| DTEND:20210122T120000Z | |
| SUMMARY:Modelling: Discussion: Modelling 2 (*) | |
| DESCRIPTION:Join Hannah Frick and Bruna Wundervald for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:bc6d3349798bba5f058a9b3747189c9b | |
| DTSTART:20210122T120000Z | |
| DTEND:20210122T122414Z | |
| SUMMARY:Keynote: Your public garden (Vicki Boykis) | |
| DESCRIPTION:Vicky will discuss how that as people who can write code and analyze data, we have a lot of input and power over what our digital and work worlds looks like, and therefore can act as agents of change and repair. | |
| LOCATION:https://global.rstudio.com/student/page/40643 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:cb7e02f5f66b69c04677885663956880 | |
| DTSTART:20210122T122414Z | |
| DTEND:20210122T132000Z | |
| SUMMARY:Keynote: Q&A: Vicki Boykis (*) | |
| DESCRIPTION:Join Andrie de Vries for audience Q&A with keynote speaker Vicki Boykis. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f620fbb83352e47a2112039b258bd473 | |
| DTSTART:20210122T140000Z | |
| DTEND:20210122T141845Z | |
| SUMMARY:Data for good: The Opioid Files: Turning big pharmacy data over to the public (Andrew Ba Tran) | |
| DESCRIPTION:Just because data is public doesn't mean it's accessible. It takes more effort, but designing data distribution so it can be analyzed by people with differing levels of data analysis skills opens up the possibility of more stories that can be told. This talk will go over how The Washington Post used R (and Python) to analyze hundreds of gigs of pain pill distribution data from the Drug Enforcement Administration as part of its investigation into the opioid epidemic. And how making the data public and showing their work enabled other journalists and researchers across the country to drill deeper than the Post could ever do on its own. | |
| LOCATION:https://global.rstudio.com/student/page/40598 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:837c00a4c3b2b9c4dab5cf3ecc2e6089 | |
| DTSTART:20210122T141845Z | |
| DTEND:20210122T142914Z | |
| SUMMARY:Data for good: rKenyaCensus Package (Shelmith Kariuki) | |
| DESCRIPTION:The rKenyaCensus package contains the results of the 2109 Kenya Population Census. The census exercise was carried out in August 2019, and the results were released in February 2020. Kenya leveraged on technology to capture data during cartographic mapping, enumeration and data transmission, making the 2019 Census the first paperless census to be conducted in Kenya.The data was published in four different pdf files (Volume 1 - Volume 4) which can be found in the Kenya National Bureau of statistics. The data in its current form was open and accessible, but not usable and so there was need to convert it into a machine readable format. This data can be used by the government, non-governmental organizations and any other entities for data driven policy making and development. During the talk, I will explain the reasons behind development of the package, take you through the steps I took during the process and finally showcase analysis of certain aspects of the data. | |
| LOCATION:https://global.rstudio.com/student/page/40639 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:2ef090a0d0e42e7cde4b307d1ad249f7 | |
| DTSTART:20210122T142914Z | |
| DTEND:20210122T150000Z | |
| SUMMARY:Data for good: Discussion: Data for good 1 (*) | |
| DESCRIPTION:Join Carl Howe and Jeffrey Breen for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:bc1350e75ee9b8b047ba158f26e72e22 | |
| DTSTART:20210122T150000Z | |
| DTEND:20210122T151919Z | |
| SUMMARY:Data for good: Humanitarian Data Science with R (Ahmadou Dicko) | |
| DESCRIPTION:Humanitarian actors are increasingly using data to drive their decisions. Since the Haiti 2010 earthquake, the volume of data collected and used by humanitarians has been growing exponentially and organizations are now relying on data specialists to turn all this data into life-saving data products.These data products are created by teams using proprietary point and click software. The process from the raw data to the final data product involves a lot of clicking, copying and pasting and is usually not reproducible.Another approach to humanitarian data science is possible using R. In this talk, I will show how to seamlessly develop reproducible, reusable humanitarian data products using the tidyverse, rmarkdown and some domain-focused R packages. | |
| LOCATION:https://global.rstudio.com/student/page/40594 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:018bfe07c02c1ded549e26d445d40d3b | |
| DTSTART:20210122T151919Z | |
| DTEND:20210122T153133Z | |
| SUMMARY:Data for good: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19 (Matt Thomas | |
| Mike Page) | |
| DESCRIPTION:We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon. | |
| LOCATION:https://global.rstudio.com/student/page/40624 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:99686cd3899ffe64b90d096011cf37fb | |
| DTSTART:20210122T153133Z | |
| DTEND:20210122T160000Z | |
| SUMMARY:Data for good: Discussion: Data for good 2 (*) | |
| DESCRIPTION:Join Carl Howe and Jeffrey Breen for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:696ae9b1b59a439192ee18c376a7ff7f | |
| DTSTART:20210122T140000Z | |
| DTEND:20210122T142057Z | |
| SUMMARY:Organisational tooling: Not The App We Deserve. The App We Need: Putting a GMP Shiny App into Production (Marcus Adams) | |
| DESCRIPTION:In February 2020, the Digital Proactive Process Analytics (DPPA) group within Merck’s manufacturing division officially launched a Shiny app to automate the creation of Continuous Process Verification (CPV) reports into production. That’s right – the almighty, mysterious, coveted production. From a technical perspective, the app is nothing particularly special (except other than getting LaTeX successfully installed to support the use of R Markdown). Users enter a few parameters and out pops a PDF with a series statistical analyses of a product’s quality testing data. The R blogosphere is filled with examples of similar Shiny apps.What mattered was the app was in production, and furthermore it was approved for GMP use. This meant these reports could be submitted to the FDA and other regulatory agencies. This meant the data could be used to support product release decisions. This meant Merck’s engineers were about to save thousands of hours per year in compiling data, generating charts, and calculating summary statistics. This was the app manufacturing sites needed.Most of the work in getting this app into production was not implementing the top-level features. Sorry, no discussion of fancy statistical process control methods here. Instead this talk will discuss some of the many things the development team (none of which came from a software development background) needed to learn in order to create a robust, secure, and maintainable production application. | |
| LOCATION:https://global.rstudio.com/student/page/40622 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7f144ba57bd3578f446083f8e6cdfa28 | |
| DTSTART:20210122T142057Z | |
| DTEND:20210122T143340Z | |
| SUMMARY:Organisational tooling: How we made the switch: a case study on automating a complex report. (Eric Gunnar Cronstrom) | |
| DESCRIPTION:The Center for Charter Schools at Central Michigan University produces annual reports for about 60 schools. The reporting process used to be a cumbersome blend of many technologies. The Center used to use a blend of SQL, Excel, inDesign, and VBScript that would all culminate in a nice looking, branded report for each school. 2 years ago, staff turnover allowed the data team to rethink the process, having had experience in R from graduate work the team at the Center decided to go all in on R Studio and R Markdown for report production a mere 1 month before the reports were due to be published.This talk will be a case study of how we as an organization adopted RStudio tools to streamline a cumbersome process to fantastic results. | |
| LOCATION:https://global.rstudio.com/student/page/40608 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:a59c91ecffe3f76ee5f99c846b1678fe | |
| DTSTART:20210122T143340Z | |
| DTEND:20210122T150000Z | |
| SUMMARY:Organisational tooling: Discussion: Organisational tooling 1 (*) | |
| DESCRIPTION:Join Rachael Dempsey and Daniella Mark for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f960ac5beff74918136311e2e99a7c82 | |
| DTSTART:20210122T150000Z | |
| DTEND:20210122T151734Z | |
| SUMMARY:Organisational tooling: From Zero to Hero: Best practices for setting up Rstudio Team in the Cloud (Rika) | |
| DESCRIPTION:Learn best practices for setting up the entire Rstudio team infrastructure - Server Pro, Connect, Package Manager from the perspective of a data scientist and for a data science audience - especially those who have never worked with servers, AWS, or bash. This talk will also be applicable to data scientists looking to start on an engineering project outside of Rstudio as well.I started out as a complete novice, & throughout my learning experience I noticed a distinct lack of resources for non-engineers. This talk will focus on best practices for AWS architecture and cloud formation, key security issues such as SSL and https, server configurations, deployment errors, and most importantly resources that are understandable for data scientists just getting into the data engineering or devops space. | |
| LOCATION:https://global.rstudio.com/student/page/40636 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:53a15c36828a9cef88c3aa4a7ce79ed8 | |
| DTSTART:20210122T151734Z | |
| DTEND:20210122T152205Z | |
| SUMMARY:Organisational tooling: How reproducible am I? A retrospective on a year of commercial data science projects in R (Dean Marchiori) | |
| DESCRIPTION:Reproducibility is a critical aspect in science to enable trust & communication. In R, many tools exist to bring in the best practices of reproducibility into the hands of data scientists. However, outside of a research setting, how does reproducibility hold up in commercial data science projects? In this talk I take an honest retrospective of my own commercial R projects in the last year. I look at the various types of analyses completed, and which workflows were selected and why. Through this process we can learn how workflow choices may help in the short term but hinder in the long term. More importantly what can be done strike the balance between progress and perfection when doing data science in the wild? | |
| LOCATION:https://global.rstudio.com/student/page/40606 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ec10443c44b4c1aef6808a2cdb651e56 | |
| DTSTART:20210122T152205Z | |
| DTEND:20210122T152613Z | |
| SUMMARY:Organisational tooling: Lifelong Learning with R Weekly (Wolfram King) | |
| DESCRIPTION:R Weekly is a weekly newsletter with many great R blogs post, tutorials, and other formats of resources. https://rweekly.orgR Weekly wants to keep track of these great things in the R community and make it more accessible to everyone.This is a warm and welcoming place. The team welcomes everyone who wants to contribute to the R community.In this talk I will cover these 6 topics:1. How to use the R Weekly website2. Why I created R Weekly3. How to Contribute to R Weekly4. How to release a new post5. How to join the team6. Learning from building the community | |
| LOCATION:https://global.rstudio.com/student/page/40645 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:017f827958bc20b7b87f3d1d53040590 | |
| DTSTART:20210122T152613Z | |
| DTEND:20210122T153117Z | |
| SUMMARY:Organisational tooling: Custom theming in Shiny & R Markdown with bslib & thematic (Carson Sievert) | |
| DESCRIPTION:Custom theming in Shiny and R Markdown often requires writing styling rules in both CSS and R. In particular, styles for HTML content (e.g., actionButton(), tabsetPanel(), titlePanel(), etc) derive from Bootstrap CSS, so customization is traditionally done by overwriting that CSS, which is difficult to do 100% correctly. The {bslib} package helps solve this problem by making it easy to customize (any version of) Bootstrap CSS defaults from R. However, this only solves part of the problem since CSS doesn't necessarily effect output(s) rendered by R, such as plotOutput(). The thematic package helps solve this problem by providing auto theming of plotOutput()s (based on CSS) as well as a simple interface for styling any R graphic for any output format. | |
| LOCATION:https://global.rstudio.com/student/page/40601 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:23f5f61220d4a33028800a189df8bcaf | |
| DTSTART:20210122T153117Z | |
| DTEND:20210122T153620Z | |
| SUMMARY:Organisational tooling: How Content Makes the Data Go 'Round (Danielle Oberdier) | |
| DESCRIPTION:What makes a successful data science community thrive across industries? A recent Aflac WorkForces Report showed that professionals who are engaged in a community within their industry are 70% more likely to be satisfied with their work.I believe anyone can and should create content about data. In this talk, I will direct your attention towards 1) the ways that content creation can lead to heightened data science opportunities 2) how to know which type/s of content mediums (podcasts, blogs, video) are right for you 3) how to leverage social media and networking connections to make your content reach the right audiences. I hope to inspire listeners to create their own content and online brands as resources for fellow R community members. | |
| LOCATION:https://global.rstudio.com/student/page/40604 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:239ed15184aca9a05a7493867e91142b | |
| DTSTART:20210122T153620Z | |
| DTEND:20210122T160000Z | |
| SUMMARY:Organisational tooling: Discussion: Organisational tooling 2 (*) | |
| DESCRIPTION:Join Rachael Dempsey and Daniella Mark for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:1c1c57a98e53bfe19a049abce2c0955d | |
| DTSTART:20210122T140000Z | |
| DTEND:20210122T141814Z | |
| SUMMARY:Programming: Introducing xrprof: A New Way to Profile R (Aaron Jacobs) | |
| DESCRIPTION:Tracking down performance issues in R code usually means using R's built-in Rprof() profiler or one of the packages built around it. But the changing nature of the R community (towards more deployed applications) makes local profiling workflows frustrating, which is why I have written a new profiler: xrprof.xprof is compatible with existing R tools, but unlike them it can be used to profile R code that is already running -- in fact, it is designed to be safe to point at R code running "in production". xrprof also works seamlessly when R is run inside Docker, and can even be run in complex environments like Kubernetes clusters.Taking inspiration from the {jointprof} package, xrprof can also show function calls at the C/C++ level alongside those from R. This can be immensely useful for diagnosing problems in packages that make heavy use of compiled code. | |
| LOCATION:https://global.rstudio.com/student/page/40593 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:26b174653e906c291fce52f6d4e2288e | |
| DTSTART:20210122T141814Z | |
| DTEND:20210122T142632Z | |
| SUMMARY:Programming: plumber + future: Async Web APIs (Barret Schloerke) | |
| DESCRIPTION:plumber is an R package that allows users to create web APIs by decorating R functions using roxygen2-like comments. In the latest release, asynchronous code (using future or promises) may be inserted at any stage of a plumber route execution, enabling parallel processing using multiple workers. In this talk, I will go through how you can set up your own asynchronous plumber API to leverage your full computing potential. | |
| LOCATION:https://global.rstudio.com/student/page/40600 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:ca5c035be764643b9f16200c3855ea50 | |
| DTSTART:20210122T142632Z | |
| DTEND:20210122T143144Z | |
| SUMMARY:Programming: Making Shiny apps faster with caching (Winston Chang) | |
| DESCRIPTION:Shiny's 1.6 has a new function, bindCache(), which makes it easy to dramatically speed up reactive expressions and output rendering functions. This allows many applications to scale up to serve several times more users without an increase in server resources. | |
| LOCATION:https://global.rstudio.com/student/page/40644 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:348b55d32a9c3be76b1f8cd12aa18fc0 | |
| DTSTART:20210122T143144Z | |
| DTEND:20210122T150000Z | |
| SUMMARY:Programming: Discussion: Programming 1 (*) | |
| DESCRIPTION:Join Joe Cheng and Susan Vanderplas for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:5cc0dc258ea34baf0fba8844812e56f5 | |
| DTSTART:20210122T150000Z | |
| DTEND:20210122T151955Z | |
| SUMMARY:Programming: Bigger Data With Ease Using Apache Arrow (Neal Richardson) | |
| DESCRIPTION:The Apache Arrow project enables data scientists using R, Python, and other languages to work with large datasets efficiently and with interactive speed. Arrow is so fast at some workflows that it seems to defy reality--or at least the limits of R's capabilities. This talk examines the unique characteristics of the Arrow project that enable it to redefine what is possible in R. The talk also highlights some of the latest developments in the arrow R package, including how you can query and manipulate multi-file datasets, and it presents strategies for speeding up workflows by up to 100x. | |
| LOCATION:https://global.rstudio.com/student/page/40631 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:7db54758e0b9c076717ff260f28782d2 | |
| DTSTART:20210122T151955Z | |
| DTEND:20210122T152422Z | |
| SUMMARY:Programming: Easy larger-than-RAM data manipulation with {disk.frame} (ZJ) | |
| DESCRIPTION:Learn how to handle 100GBs of data with ease using {disk.frame} - the larger-than-RAM-data manipulation package.R loads data in its entirety into RAM. However, RAM is a precious resource and often do run out. That's why most R user would have run into the "cannot allocate vector of size xxB." error at some point.However, the need to handle larger-than-RAM data doesn't go away just because RAM isn't large enough. So many useRs turn to big data tools like Spark for the task. In this talk, I will make the case that {disk.frame} is sufficient and often preferable for manipulating larger-than-RAM data that fit on disk. I will show how you can apply familiar {dplyr}-verbs to manipulate larger-than-RAM data with {disk.frame}. | |
| LOCATION:https://global.rstudio.com/student/page/40647 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:bcbbd54e302261d42be33d0bdfce74fc | |
| DTSTART:20210122T152422Z | |
| DTEND:20210122T152915Z | |
| SUMMARY:Programming: xaringan Playground: Using xaringan to learn web development (Garrick Aden-Buie) | |
| DESCRIPTION:xaringan is a quirky package that extends R Markdown to create beautiful web-based HTML slides. Some of xaringan’s quirks come from the JavaScript library it uses, remarkjs, and some of it from the unusual naming scheme xaringan uses for its functions. But under this quirky exterior lies a powerful tool for learning and practicing web development, especially when combined with infinite_moon_reader() for immediate feedback. In this talk I'll cover some basic web concepts that illustrate how fun and rewarding it can to learn HTML, CSS and JavaScript while building awesome slides in R Markdown. | |
| LOCATION:https://global.rstudio.com/student/page/40609 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:f1a62dbca7b9ec0a2ebfcc8d608bb030 | |
| DTSTART:20210122T152915Z | |
| DTEND:20210122T153429Z | |
| SUMMARY:Programming: parsermd - parsing R Markdown for fun and profit (Colin Rundel) | |
| DESCRIPTION:parsermd is a new R package for parsing and programmatically interacting with R Markdown (Rmd) documents. This package implements a formal grammar for Rmd documents in C++ using Boost's Spirit X3 library and provides additional user facing functions for the resulting abstract syntax tree. In this talk we will provide background on the structure and grammar of Rmd documents as well as discuss the ways in which the parsing of these documents enables a variety of automatable tasks. Specifically, we will focus on demonstrating how these tools can be used to provide automated feedback on student submissions in a statisical programming course. | |
| LOCATION:https://global.rstudio.com/student/page/40603 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:923b5353d6e8af3889d2a7aeb50095ce | |
| DTSTART:20210122T153429Z | |
| DTEND:20210122T153937Z | |
| SUMMARY:Programming: Designing Randomized Studies using Shiny (Lucy D'Agostino McGowan) | |
| DESCRIPTION:This talk will walk through building a self-contained randomized study using Shiny and learnr modules. We will discuss building informed consent, the randomization process, demographic surveys, and R-based studies into a single online framework to allow users to seamlessly enroll and participate in randomized studies via a single URL. The talk will include both practical recommendations as well as technical code snippets. | |
| LOCATION:https://global.rstudio.com/student/page/40620 | |
| END:VEVENT | |
| BEGIN:VEVENT | |
| DTSTAMP:20210118T000000Z | |
| UID:c2afebde35900279ce4c091674c7bb1a | |
| DTSTART:20210122T153937Z | |
| DTEND:20210122T160000Z | |
| SUMMARY:Programming: Discussion: Programming 2 (*) | |
| DESCRIPTION:Join Joe Cheng and Susan Vanderplas for audience Q&A with the preceding speakers in this session. | |
| LOCATION: | |
| END:VEVENT | |
| END:VCALENDAR |
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