From deep learning to clean_names(), resources from Data Journalism in R

Data Journalism in R
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Books to check out

  1. Deep Learning in R
  2. Analyzing Baseball Data in R 2nd Edition
    • We love the second edition of this text because 1) it updated everything from the first edition to tidyversefunctions, and 2) Ben Baumer was added as a co-author.
  3. Data Visualization – a practical introduction
    • This is available online as an eBook or you can order it from amazon. Kieran Healy distills tons of valuable information on working in R, RStudio, and the benefits of working in plain text (see his other book dedicated to this topic here).

Articles to read

  1. Ever get your data in a PDF and need to wrangle it in R? Check out How did Axios rectangle Trump’s PDF schedule? A try with R.
  2. YouTube decided to pull ads on videos that they deemed were anti-vaccination (or advocated against vaccinations). See this Buzzfeed article to learn more.
  3. If you want great coverage of Politics and R, check out R for Political Data. This article is part of a weekly series published by G. Elliott Morris from The Economist.

New software & tools

  1. This package is not necessarily new, but I feel it needs more attention than it’s been getting. The janitor package “has simple functions for examining and cleaning dirty data. It was built with beginning and intermediate R users in mind and is optimized for user-friendliness.” The clean_names() function is a life-saver.
  2. Do you do everything inside RStudio? I try to, and now I can use Python without ever leaving the comfort of my IDE thanks to the reticulate package. Check out the tutorial on RStudio here.
  3. If you ever have the pleasure of using regular expressions, check out regexplain, an excellent RStudio Add-In.

Courses/moocs

  1. Jeff Leek, Lucy D’Agostino McGowan, and Elizabeth Matsui have released a course on leanpub, “Understanding data and statistics in the medical literature.” Just about anything Jeff Leek touches turns into gold (see his Chromebook Data Science course), so expect awesomeness.
  2. This great Python course on Github, Algorithms – Lede 2018. Lots of materials and documentation.
  3. The #TidyTuesday posts are an outstanding addition to Twitter, but you can also check out this list of videos on YouTube.

Stuff to listen to

  1. Julia Silge was on Data Skeptic talking about Data mining in R.
  2. Do you ever wonder where all this sh!t came from? The Command Line Heroes podcast has a great history of technology. Check out Press Start: How Gaming Shapes Development
  3. fivethirtyeight science writer Christie Aschwanden was interviewed on Stats & Stories about her newly released book on exercise recovery titled, “Good to Go: What the Athlete in All of Us Can Learn from the Strange Science of Recovery.”
Martin Frigaard
Martin Frigaard is a tidyverse/R trainer in Oakland, CA. Find him on Twitter.

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