R Weekly 2020-36 R packages, Plinko statistics, tidy error handling

07 Sep 2020

Release Date: 2020-09-07

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This week’s release was curated by Robert Hickman, with help from the RWeekly team members and contributors.

Highlight

• State of R packages in your library

• Plinko Statistics: Insights from the Bean Machine

• Handling errors using purrr’s possibly() and safely()

Insights

• Gotta go fast with “{tidytable}”

• Mixture distributions and reporting times for Covid-19 deaths in Florida by @ellis2013nz

• Using dplyr::filter when the condition is a string

• Avoid Apply() Function In Large Datasets

• State of R packages in your library

• Train and analyze many models for #TidyTuesday crop yields

• An Example Where Square Loss of a Sigmoid Prediction is not Convex in the Parameters

• Diagnosing and dealing with degenerate estimation in a Bayesian meta-analysis

• Never trust an average!

• An Intersting Aspect of the Omitted Variable Bias

• Simulations Comparing Interaction for Adjusted Risk Ratios versus Adjusted Odds Ratios

• Plinko Statistics: Insights from the Bean Machine

• A Shiny App on Hong Kong District Councillors

R in the Real World

• Task 1 - Retail Strategy and Analytics

• Crowd Counting Consortium Crowd Data and Shiny Dashboard

• Most popular on Netflix. Daily Tops for last 60 days

• Why do I have a data science blog? 7 benefits of sharing your code

R in Organizations

• September 2020 ISC Call for Proposals – Now Open!

• Newly certified instructors in August 2020

R in Academia

• Transitioning a Soil Mechanics Course to R/exams

Resources

• 3 Fun Shiny Apps for Your Long Labor Day Weekend

• Even easier-to-use R package for school district shape files - 02/09/2020

• From R Hub – JavaScript for the R package developer

• Sharing Your xaringan Slides

• ‘Looping’ and ‘Branching’ with Pipes

• Interview Questions About Stats And Probabilities

• R treasures: modifyList()

• Survey categorical variables with KableExtra

• Partial dependence plots for tidymodels-based xgboost

• How to Clean Data: {janitor} Package

• 100 Time Series Data Mining Questions (with answers!) - Part 1

• Exact Tests and Plots with edgeR – Basic Differential Expression Analysis

  New Packages

📦 Go Live for More New Pkgs 📦

GitHub or Bitbucket

• Easy email invites for your Shiny app users

• Back next week, and a few announcements

Updated Packages

• modelsummary 0.6.0 is out!

• Appsilon has created an R package to offload long running calculations so that the UI of Shiny apps can remain responsive. - 27/08/2020

• Numerical Partial Derivative Estimation – the {NNS} package

• RcppArmadillo 0.9.900.3.0

• RcppCCTZ 0.2.9: API Header Added

• RcppSMC 0.2.2: Small updates

• Introducing ‘insitu’ - functions for in-place modification

Videos and Podcasts

• Why R? Webinar - Reproducible research with workflowr

Tutorials

• Correcting for confounded variables with GLMs

• Hierarchical Clustering in R - 27/08/2020

• Data Visualization in R with ggplot2: A Beginner Tutorial

• Time Series in 5-Minutes, Part 5: Anomaly Detection

• Bootstrap confidence intervals and confidence distritbutions - application on X-men data using ggdist

• Web Scraping with rvest: Exploring Sports Industry Jobs

• Quick Tips for Data Cleaning in R

• NLP: preparing data

• An introduction to weather forecasting with deep learning

• Easiest flowcharts eveR?

• Handling errors using purrr’s possibly() and safely()

• Data manipulation in r using data frames - an extensive article of basics part2 - aggregation and sorting

R Project Updates

Updates from R Core:

Upcoming Events in 3 Months

Events in 3 Months:

• 88th TokyoR Virtual Meetup - September 19th, 2020

• VirtuEARL – EARL Conference online - October 2020

• A list of R conferences and meetings

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

Jobs

• Expert in Data Analysis and Visualization with R

• Online R Tutor for Data Science

Call for Participation

Your support will keep R Weekly team moving! 💡

Quotes of the Week

Who's excited for LEGO brick 3D bar charts in ggplot? 🙋‍♂️

Seeing how many data viz rules I can break before getting banned from #rstats twitter. pic.twitter.com/ALZskJL61R

— Ryan Timpe 🏳️‍🌈🦕 (@ryantimpe) September 3, 2020

OUT or notOUT? Wanna find OUT? checkOUT our latest OUTlier detection paper: "Anomaly Detection in High Dimensional Data", now online @ JCGS: https://t.co/GkiZGwpZ4E. Co-authored with my awesome supervisors @robjhyndman & Kate Smith-Miles. Companion #rstats 📦 stray is on CRAN 🐶 pic.twitter.com/du6PfbVxHH

— Priyanga Dilini Talagala (@pridiltal) September 1, 2020

Well this was a new R warning message for me; I guess my data are just too perfect! #RStats #Nonsense pic.twitter.com/MbDv2oqsg7

— Lydia Hopper (@LydiaMHopper) September 1, 2020