R Weekly 2022-W30 {nplyr}, {rtweet}, & {ggarchery}!

25 Jul 2022

• How to have (my) content shared by R Weekly?

This week’s release was curated by Ryo Nakagawara, with help from the R Weekly team members and contributors.

Highlight

• {nplyr} 0.1.0: A grammar of (nested) data manipulation.

• Upgrading rtweet

• {ggarchery}: Flexible segment geoms with arrows for ggplot2.

Insights

• Heads up! Quarto is here to stay. Immediately combine R & Python in your next document

• The ultimate guide to starting a Quarto blog

• Upgrading rtweet

• Robin Donatello Talks About Growing an R Community at a State University

R in the Real World

• Text Mining: Histories of Herodotus

• Hillshade effects

R in Organizations

• {a11ytables} 0.1.0: Generate stats spreadsheets for publication that adhere to the latest guidance (June 2021) on releasing statistics in spreadsheets from the Best Practice and Impact Division (BPID) of the UK’s Government Statistical Service (GSS).

Resources

• rstudio::conf(2022, “program”)

• Really Useful Engines: Engineering Data Science (Slides)

• Gist: How to set up and analyse an {arrow} dataset with {dplyr} or duckDB

• Gist: Find most used variables names in all R scripts

New Packages

📦 Go Live for More New Pkgs 📦

CRAN

• {string2path} 0.1.0

• {nplyr} 0.1.0: A grammar of (nested) data manipulation.

• {globaltrends} 0.0.12: An R package that builds on gtrendsR to facilitate downloading, storing, and analyzing large-scale Google Trends queries.

GitHub or Bitbucket

• {strangeRthings} 0.1.0: Stranger Things episode transcripts in tidy format (github.com).

• {primermd} 0.0.1: An Accessible and Responsive Template for R Markdown, Based on GitHub’s Primer CSS.

• {ggarchery} 0.3.0: Flexible segment geoms with arrows for ggplot2.

• {gfwr} 0.0.0.9000: R package for accessing data from Global Fishing Watch APIs.

• {ggdensity} 0.1.0: An R package for interpretable visualizations of bivariate density estimates.

• {epinowcast 0.1.0}: Flexible hierarchical nowcasting.

• {NatParksPalettes} 0.1.0: Color palette package inspired by National Parks.

• {gosling} 0.0.0.9000: Interface to ‘Gos’.

Updated Packages

🔍 Search on R-universe 🔍

• {mirai} 0.5.2: Minimalist async evaluation framework for R. - diffify

• {preferably} 0.4.1: An Accessible ‘pkgdown’ Template. - diffify

• {s2} 1.1.0: Spherical Geometry Operators Using the S2 Geometry Library. - diffify

• {sfdep} 0.2.0: A tidy interface for spatial dependence. - diffify

• {blsR} 0.3.1: Make Requests from the Bureau of Labor Statistics API - diffify

• {accessibility} 1.0.0: Transport Accessibility Measures - diffify

• {audubon} 0.3.0: Japanese Text Processing Tools - diffify

• {future} 1.27.0: Unified Parallel and Distributed Processing in R for Everyone - diffify

• {tibble} 3.1.8: Simple Data Frames - diffify

• {dm} 1.0.0: Relational Data Models - diffify

• {datardis} 0.0.3: Data from the Doctor Who Series - diffify

• {mlr3} 0.13.4: Machine Learning in R - Next Generation - diffify

• {stars} 0.5-6: Spatiotemporal Arrays, Raster and Vector Data Cubes - diffify

• {rtweet} 1.0.2: Collecting Twitter Data - diffify

• {ggtikz} 0.1.1: Post-Process ‘ggplot2’ Plots with ‘TikZ’ Code Using Plot Coordinates - diffify

• {devtools} 2.4.4: Tools to Make Developing R Packages Easier - diffify

• {fontawesome} 0.3.0: Easily Work with ‘Font Awesome’ Icons - diffify

• {igraph} 1.3.4: Network Analysis and Visualization - diffify

• {bsTools} 1.0.2: Create HTML Content with Bootstrap 5 Classes and Layouts - diffify

• {ggdist} 3.2.0: Visualizations of Distributions and Uncertainty - diffify

• {tvthemes} 1.3.1: TV Show Themes and Color Palettes for ‘ggplot2’ Graphics - diffify

• {shiny} 1.7.2: Web Application Framework for R - diffify

• {roxygen2} 7.2.1: In-Line Documentation for R - diffify

• {pillar} 1.8.0: Coloured Formatting for Columns - diffify

• {htmltools} 0.5.3: Tools for HTML - diffify

• {RSQLite} 2.2.15: SQLite Interface for R - diffify

• {ggdag} 0.2.5: Analyze and Create Elegant Directed Acyclic Graphs - diffify

• {ggshakeR 0.2.0}: Analytics and Visualization Package for Soccer Data. - diffify

• {bslib} 0.4.0: Custom ‘Bootstrap’ ‘Sass’ Themes for ‘shiny’ and ‘rmarkdown’ - diffify

Videos and Podcasts

• Listen to the R-Weekly Highlights Podcast

• TidyX Episode 111: Nate Latshaw, UFC Data, and data.table

• NHS-R Webinar: Shiny Tool Predicting Primary Care Demand from Large Scale Housing Devs, July 2022

• Mastering Shiny Book Club: Bookmarking (mshiny03 11)

• How to exchange NA values by another variable in the R programming language

Tutorials

• Draft position for players in the NBA for the 2020-21 season

• Developer diary for {ggshakeR} 0.2.0: Working smoothly as a team on GitHub for R package development!

• What’s the fastest way to search and replace strings in a data frame?

• Introduction to ggpattern Package in R (6 Examples): ggplot2 Plots with Textures

• A Kaggle Dataset of R Package History for rstudio::conf(2022)

R Project Updates

Updates from R Core:

Upcoming Events in 3 Months

Events in 3 Months:

• Why this is the year you should take the stage at EARL 2022…

• A list of R conferences and meetings

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

Jobs

• Senior Data Scientist - Chief

• Data Scientist – Apple Cloud Services (R programming)

Call for Participation

• Submit your abstract to NHS-R Community Conference 2022

Your support will keep R Weekly team moving! 💡

Quotes of the Week

messy, realistic datasets are **extremely** useful for learning, teaching, exploring, and practicing your #rstats skills. so where do you find datasets like this?!

here are some practical tips you can use to find messy datasets! 🧵

— We are R-Ladies (@WeAreRLadies) July 21, 2022

Today's #rstats function I really should have tried before now: purrr::walk()

I love a good "for" loop, because it's very readable, but walk() is more succinct *and* very readable - arguably more so!

Say hi to the Penguins with me 👋🐧🐧🐧

P.S. Thanks @dgkeyes for the tip! pic.twitter.com/cW3K7gEa4f

— Cara Thompson (@cararthompson) July 22, 2022