BLACK LIVES MATTER
Join us and donate
The premier IDE for R
RStudio anywhere using a web browser
Put Shiny applications online
Shiny, R Markdown, Tidyverse and more
Do, share, teach and learn data science
An easy way to access R packages
Let us host your Shiny applications
A single home for R & Python Data Science Teams
Scale, develop, and collaborate across R & Python
Easily share your insights
Control and distribute packages
RStudio Public Package Manager
RStudio Package Manager
Bringing the Tidyverse to Python with Siuba
January 21, 2021
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.
Michael Chow and Sean Lopp Q&A 1
Michael Chow and Sean Lopp Q&A 2
Michael Chow is a data scientist and learning researcher. He serves as a co-director at Code for Philly. In past lives, he worked on adaptive assessment tools in ed tech, and received a PhD in cognitive psychology from Princeton University.