R Weekly 2018-27 Future, Function

02 Jul 2018

Highlight

• future.apply 1.0.0 - Apply Function to Elements in Parallel using Futures (a milestone release)

• The rewrite of the Advanced R functions chapter

New Packages

📦 Go Live for More New Pkgs 📦

CRAN

• cattonum - a tidy one-stop shop for categorical encodings

• roomba - A package for tidying nested lists

• flatxml - Working with XML files as R dataframes

• Rayshader - Draining Lake Mead: Making Beautiful Maps

BioC

GitHub or Bitbucket

• modelDown - a website generator for your predictive models

• paletteer - Collection of most color palettes in a single R package

Package Releases

• RSAGA 1.0.0 - an interface between R and the open-source geographic information system SAGA

• future.apply 1.0.0 - Apply Function to Elements in Parallel using Futures (a milestone release)

• Shiny 1.1.0 - Scaling Shiny with async

Insights

• The Role of Resources in Data Analysis

• future.apply - Parallelize Any Base R Apply Function

• Data Methods Discussion Site

• Creativity in Data Analysis

• Seriously, Put Your Work Online: A Cautionary Tale

R in the Real World

• Demography simulations

• How to Map Public Debt Data with ggplot2

• Mapping the Best States for Business

• Cultural Differences in Map Data Visualization

• Deep Learning for Time Series Forecasting: Predicting Sunspot Frequency with Keras

• Storrrify #satRdayCDF 2018

• Forecasting my weight with R

• The Financial Times and BBC use R for publication graphics

• Global Migration, animated with R

• Game of Friendship Paradox

Resources

• (Unit) Testing Shiny apps using testthat

• The rewrite of the Advanced R functions chapter

R in Organizations

• Announcing new software review editors: Anna Krystalli and Lincoln Mullen

Tutorials

• Beeswarms instead of histograms

• RNN vs. Dense neural networks for time-series

• Visualizing crochet circles / hexagons Part 2

• HTML Widgets for Non-HTML Output Formats

• On Cache Invalidation

• Ideally, I Hope to Simply Copy and Run Your Example

• One Little Thing: knitr::combine_words()

• Code for Workshop: Introduction to Machine Learning with R

• Delayed Future (Slides from eRum 2018)

• Simple Bootstrap and Simulation Approaches to Quantifying Reliability of High-Dimensional Feature Selection

• reprex-cellence 👑

• Intel MKL in Debian / Ubuntu follow-up

• Re-referencing factor levels to estimate standard errors when there is interaction turns out to be a really simple solution

• Text-to-speech with R

• Data wrangling operations with quantities

• Visualizing the World Cup with R! Part 1: Recreating goals with ggsoccer and ggplot2

• Time after time: calculating the autocorrelation function for uneven or grouped time series

• Should you learn R or Python for data science?

• Addendum: Text-to-Speech with the googleLanguageR package

• Running Python Inside the Rstudio Server IDE

Upcoming Events in 3 Months

Events in 3 Months:

• 7eme Rencontres R July 5 & 6
  Rennes - Agrocampus

• useR! 2018 July 10
  The annual useR! conference is the main meeting of the international R user and developer community.

• LatinR 2018 Sept 4-5
  Buenos Aires, Argentina.

• Upcoming R Community Events (week of 2018-06-18)

This week’s local R-User and applied stats events

More past events at R conferences & meetups.

Call for Participation

Your support will keep R Weekly team moving! 💡

Quotes of the Week

Occasionally you have to read data into #rstats where one observation is split across multiple rows. A handy pattern to fix is to mutate, fill, and filter pic.twitter.com/be7JQNscCE

— Hadley Wickham (@hadleywickham) June 22, 2018

Stop saying #rstats is "only for statistics", it's ridiculous. Statistics needed something new because real modelling is actually very diverse and hard. It's a programming language and was up to the challenge.

— Michael Sumner (@mdsumner) June 28, 2018

every week #rstats pic.twitter.com/r2TTBBiUOJ

— Dr Andrew MacDonald (@polesasunder) June 29, 2018

I'm impressed with the quality of my students' homework this year. In 20h of class they've gone from zero #rstats knowledge to being able to visualise data, fit and interpret GLMs.
I'm actually learning many R tricks they got from internet (like real data scientists!). So happy pic.twitter.com/OuLpwRmoTR

— F Rodriguez-Sanchez (@frod_san) June 25, 2018