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Javier Luraschi | Using pins with Python and JavaScript | RStudio

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.

About Javier: Javier is the author of “Mastering Spark with R”, pins, sparklyr, mlflow and torch. He holds a double degree in Math and Software Engineer and decades of industry experience with a focus on data analysis. Javier is currently working on a project of his own; and previously worked in RStudio, Microsoft Research and SAP

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Javier Luraschi Javascript Pins

Alan Dipert | Integrating React.js and Shiny | RStudio (2019)

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from color selection (react-color) to animation (react-spring). While it’s always been technically possible to integrate React.js components with Shiny applications, it hasn’t always been particularly obvious how. To make it easier, we augmented the excellent reactR package with functions specifically designed to make it easier to create new htmlwidgets, inputs, and outputs based on React.js components. In this talk, I will further motivate and demonstrate these new tools and do my best to empower the audience to try them out.

About the Author Alan Dipert Alan is a software engineer on the Shiny team at RStudio. In the past, he’s helped build web applications, reporting pipelines, and many things between. When he’s not working, Alan likes to spend his time reading or being with his family

Shiny Team

rstudio Shiny Alan Dipert Reactjs React.js Javascript RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats