Javier Luraschi

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

Javier Luraschi | Datasets in Reproducible Research with ‘pins’ | RStudio (2020)

Open source code is an essential piece in making science reproducible. Tools like ‘rmarkdown’ and GitHub facilitate running and sharing outcomes with colleagues and with the broad scientific community at large. However, it is less clear what tools should be used to retrieve, store and share datasets; while it is possible to make datasets part of your workflows today, it is usually hard and we are often left with manually sharing or downloading links to datasets. Not only that, but it’s also hard to share or discover datasets. In this talk we will introduce for the first time the ‘pins’ package. A package designed to: pin, discover and share resources. Meaning that, you can use ‘pins’ to simplify your data science workflows by easily fetching resources from GitHub, Kaggle, CRAN and RStudio Connect. We will present a ‘pin’ as a generic resource that can contain tabular datasets like CSVs, unstructured data like JSON files, image archives as ZIP files and so on. This talk will be highly interactive showing you how to get started by installing ‘pins’ from CRAN, retrieve and cache resources, share and discover useful and fun data resources to improve and enhance your day-to-day workflows