Founded by Josh Reich and Drew Conway, the New York Open Statistical Programming Meetup started as the New York R Meetup with a handful of people in an office at Union Square Ventures. Since then it has grown to over 8,000 members and has been hosted at NYU, Columbia, AOL, iHeartRadio, eBay, Work-Bench and other locations.

Our mission is to spread knowledge of statistical programming techniques in open-source languages such as R, Python, Julia and Go, and data science in general. Another important aspect is community building and socializing. The meetups start with pizza, followed by a 45-90 minute talk, ending with a trip to the local bar.


To attend please visit the meetup page.

Presentations and Videos

Whenever possible we make presentations available at the Presentations page.

We now stream and host videos of meetups on Facebook and YouTube and older videos are scattered on a variety of services. They are also listed on the Presentations page.


Job openings and other announcements are on the meetup discussion board.

Upcoming Meetup

Tidy Your Time Series Analysis with tsibble

October 4, 2018 06:30:00 PM

Following up on our earlier meetup about time series forecasting we have Earo Wang coming to us from Australia to talk about tsibble for tidy time series data.

Thank you to AT&T for hosting us. Note, this is a smaller space than usual for us so slots will fill up fast.

About the Talk:

Mining temporal-context data for information is often inhibited by a multitude of time formats: irregular or multiple time intervals, multiple observational units or repeated measurements on multiple individuals, heterogeneous data types and nested and crossed factors indicating hierarchical sub-groups. Time series models, in particular, the software supporting time series forecasting, makes strict assumptions on data that need to be provided, typically a matrix of numeric data with an implicit time index. Going from raw data to model-ready data is painful.

This work presents a cohesive and conceptual framework for organizing and manipulating temporal data, which in turn flows into visualization and forecasting routines. Tidy data principles are applied, and extended to temporal data: (1) mapping the semantics of a dataset into its physical layout, (2) including an explicitly declared index variable representing time, (3) incorporating a "key" comprised of single or multiple variables to uniquely identify units over time, using a syntax-based and user-oriented approach in which it imposes nested or crossed structures on the data.

This tidy data representation most naturally supports thinking of operations on the data as building blocks, forming part of a "data pipeline" in time-based context. A sound data pipeline facilitates a fluent and transparent workflow for analyzing temporal data. Applications are included to illustrate tidy temporal data structure, data pipeline structure and usage. The infrastructure of tidy temporal data has been implemented in the R package tsibble.

About Earo:

Earo is currently doing research on tidy data structure and visualisation of temporal-context data, as part of her PhD at Monash University. She enjoys developing open-source tools with R, and (co)authors some widely-used R packages including tsibble, sugrrants, hts, rwalkr, anomalous and icon. She was described by Yihui Xie (author of rmarkdown, knitr, bookdown and blogdown) as "one of the most impressive R ladies I have ever met".

Pizza ( begins at 6:30, the talk starts at 7, then after we head to the local bar.


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