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Talks from rtudio:conf 2019 that I implemented and inspired me

Posted on 2 mins read

I think that it is great that the talks from rstudio:conf are all accessible online at: https://resources.rstudio.com/rstudio-conf-2019

These are the talks, without a specific order, that inspired me or that gave me ideas to implement some libraries.

  1. Introducing the gt package - Rich Iannone https://resources.rstudio.com/rstudio-conf-2019/introducing-the-gt-package
  • thanks to this talk I discovered the gt package to make wonderful-looking tables.
  1. Democratizing R with Plumber APIs - James Blair https://resources.rstudio.com/rstudio-conf-2019/democratizing-r-with-plumber-apis
  • the plumber library converts your existing R code to a web API.
  • this is useful to distribuite your data outputs to other IT people who use other languages.
  1. Don’t let long running tasks hang your users Introducing ipc for Shiny - Ian Fellows https://resources.rstudio.com/rstudio-conf-2019/don-t-let-long-running-tasks-hang-your-users-introducing-ipc-for-shiny
  1. The unreasonable effectiveness of public work - David Robinson https://resources.rstudio.com/rstudio-conf-2019/the-unreasonable-effectiveness-of-public-work
  • This talk gave me the motivation to start this blog.
  1. pagedown: Creating beautiful PDFs with R Markdown and CSS - Yihui Xie https://resources.rstudio.com/rstudio-conf-2019/pagedown-creating-beautiful-pdfs-with-r-markdown-and-css
  • after watching this talk I decided to write my CV with the pagedown library, which is much more easy to manage than what I was previously doing with word.
  1. 3D mapping, plotting, and printing with rayshader - Tyler Morgan-Wall https://resources.rstudio.com/rstudio-conf-2019/3d-mapping-plotting-and-printing-with-rayshader
  • Here the library rayshader is presented.
  • With this library you can create fancy 3D maps and 3D printing.
  1. Panel discussion Growth and Data Science: Individuals, leaders, organizations and responsibilities https://resources.rstudio.com/rstudio-conf-2019/panel
  • Tips that I noted down from the discussion:
    • be flexible with tooling;
    • be empathic to understand the users;
    • have the humility to say that you don’t know;
    • One of the biggest sources of value of a data scientist is the ability to articulate a business problem into a quantitive answerable hypothesis;
    • “Junior data scientists do not know what it is impossible yet”