R Weekly 2022-W40 Data Analysis Principles, Code Explorer, and Simulating Environment Change

03 Oct 2022

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

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

Highlight

• Design Principles for Data Analysis

• {Unravel} - A fluent code explorer for R

• Case Study: Simulating Environment Change Agents on Species in Canada’s Western Boreal Forests

Insights

• Performance Benchmarking Data Read Write

• Design Principles for Data Analysis

R in the Real World

• Housing Markets Down: Hierarchical Time Series

R in Academia

• Understanding Deviance Residuals

• Leading the Way for New R useRs with Strong Connections To Local Universities

Resources

• 5 New books added to Big Book of R

• August 2022: “Top 40” New CRAN Packages

New Packages

📦 Go Live for More New Pkgs 📦

CRAN

• {tabledown} 0.0.3: Create Publication Quality Tables and Plots

• {JATSdecoder} 1.1: A Metadata and Text Extraction and Manipulation Tool Set

• {openxlsx2} 0.3: Read, Write and Edit ‘xlsx’ Files

• {CDMConnector} 0.1.0: Connect to an OMOP Common Data Model

• {pandoc} 0.1.0: Manage and Run Universal Converter ‘Pandoc’ from ‘R’

• {organizr} 0.1.0: Shortcuts for File Creation with Informative Prefixes

• {neonOS} 1.0.0: Basic Data Wrangling for NEON Observational Data

• {easydb} 1.0.0: Easily Connect to Common Types of Databases

• {less} 0.1.0: Learning with Subset Stacking

• {getpublist} 0.1.0: Obtain a List of Publications from ‘PubMed’ and ‘Google Scholar’

• {shinyXYpad} 0.2.0: XY Controller for ‘Shiny’

• {NMcalc} 0.0.1: Basic Calculations for PK/PD Modeling

• {SDPrism2D} 0.1.1: Visualizing the Standard Deviation as the Size of a Prism

GitHub or Bitbucket

• {elvis} — ‘Shiny’ renderers and observers made safer

• {Unravel} - A fluent code explorer for R

Updated Packages

🔍 Search on R-universe 🔍

• All the New Things in {gt} 0.7.0

• brulee 0.2.0

• {connectapi} 0.1.2: Utilities for Interacting with the ‘RStudio Connect’ Server API - diffify

• {decorators} 0.3.0: Extend the Behaviour of a Function without Explicitly Modifying it - diffify

• {gtsummary} 1.6.2: Presentation-Ready Data Summary and Analytic Result Tables - diffify

• {rAmCharts} 2.1.15: JavaScript Charts Tool - diffify

• {NMproject} 0.6.9: Script Based ‘NONMEM’ Model Development - diffify

• {tableschema.r} 1.1.2: Table Schema ‘Frictionless Data’ - diffify

• {visNetwork} 2.1.2: Network Visualization using ‘vis.js’ Library - diffify

• {suncalc} 0.5.1: Compute Sun Position, Sunlight Phases, Moon Position and Lunar Phase - diffify

• {forestploter} 0.2.1: Create Flexible Forest Plot - diffify

• {shinypivottabler} 1.1: Shiny Module to Create Pivot Tables - diffify

• {graphlayouts} 0.8.2: Additional Layout Algorithms for Network Visualizations - diffify

• {prnsamplr} 0.2.1: Permanent Random Number Sampling - diffify

• {renv} 0.16.0: Project Environments - diffify

• {ggforce} 0.4.0: Accelerating ‘ggplot2’ - diffify

• {shinydrive} 0.1.3: File Sharing Shiny Module - diffify

• {schrute} 1.0.1: The Entire Transcript from the Office in Tidy Format - diffify

• {ggip} 0.2.2: Data Visualization for IP Addresses and Networks - diffify

• {fitzRoy} 1.2.0: Easily Scrape and Process AFL Data - diffify

• {shinymanager} 1.0.410: Authentication Management for ‘Shiny’ Applications - diffify

• {roger} 1.1-0: Automated Grading of R Scripts - diffify

• {ggcorrplot} 0.1.4: Visualization of a Correlation Matrix using ‘ggplot2’ - diffify

• {pagedown} 0.19: Paginate the HTML Output of R Markdown with CSS for Print - diffify

• {multidplyr} 0.1.2: A Multi-Process ‘dplyr’ Backend - diffify

• {shinytest2} 0.2.0: Testing for Shiny Applications - diffify

• {colorDF} 0.1.7: Colorful Data Frames in R Terminal - diffify

• {workflows} 1.1.0: Modeling Workflows - diffify

• {riskRegression} 2022.09.23: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks - diffify

• {golem} 0.3.4: A Framework for Robust Shiny Applications - diffify

• {osfr} 0.2.9: Interface to the ‘Open Science Framework’ (‘OSF’) - diffify

• {graphql} 1.5.1: A GraphQL Query Parser - diffify

  Videos and Podcasts

• Listen to the R-Weekly Highlights Podcast

• R for the Rest of Us Podcast Episode 1: Cedric Scherer and Georgios Karamanis

• It’s about time

• How to include FontAwesome icons in Quarto documents

• How to send automated emails on a schedule using R for free (blastula, Quarto, and GitHub Actions)

• Beautiful Presentations and Reports with Quarto

Shiny Apps

• Case Study: Simulating Environment Change Agents on Species in Canada’s Western Boreal Forests

• Infinite Scrolling in Shiny

Tutorials

• A Spatial/GIS Workflow for Interactive Maps in R

• Materials for an {renv} demo

• API as a package: Testing

• Handling Empty Paragraphs from R Markdown

• Data Visualisation with Markdown, Flexdashboard and Shiny

R Project Updates

Updates from R Core:

Upcoming Events in 3 Months

Events in 3 Months:

• Shiny in Production Conference

• R/Pharma 2022

• A list of R conferences and meetings

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

• Weekly workshops in R/Python ‘Workshops for Ukraine’

Jobs

💼 Explore Jobs & Gigs Board on RStudio Community 💼

Quotes of the Week

Have you written any short #rstats functions to reduce duplication in your plotting code? e.g.

histogram <- function(df, var, binwidth) {
df |>
ggplot(aes()) +
geom_histogram(binwidth = binwidth)
}

I'm looking for inspiration for examples for R4DS

— Hadley Wickham (@hadleywickham) September 26, 2022

Really happy with this: have just reproduced the entirety of Geocomputation with R inside a 'devcontainer' with VS Code. It works 🎉
A small step on the #geocompr journey, a giant leap for reproducibility + accessibility! #RSpatial #DataScience pic.twitter.com/NOA4grh7X8

— Robin Lovelace (@robinlovelace) October 1, 2022

Do all #rstats folks do this? I have a presentation due Monday. I could make my maps in Q and slides in PPT, but instead I am using it as an opportunity to try #quartopub slides and try out new (to me) mapping 📦s. The learning curve involved in reproducible research is... pic.twitter.com/swh0CwEHBd

— Tesla DuBois (@TeslaDuBois) October 1, 2022