R Weekly 2020-45 Web SVG graphics device, markdown powered ggplot themes, playing Midi

22 Jun 2020

Release Date: 2020-06-22

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

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

Highlight

• Using httpgd in VSCode: A web-based SVG graphics device

• {mdthemes} is on CRAN: markdown powered themes for {ggplot2}

• Midi Player with R - Part 2 (French)

Insights

• Just Quickly: Removing Sawtooth Patterns in Line Graphs

• Rcpp 1.0.5 in two+ weeks: Please help test

• Logging in R: Why, When, and How

• How does the pipe operator actually work?

• overlap, overstreched

• NWSL Mini-Previews: OL Reign, Portland Thorns, and Utah Royals FC

• Bioconductor submissions: How long does it take? And what to expect

• Implementing supervised discretization step with XgBoost backend for {tidymodels}

• Using httpgd in VSCode: A web-based SVG graphics device

• R tips and tricks

• R Can Pull the Fire Alarm!

• From R Hub – Counting and Visualizing CRAN Downloads with packageRank (with Caveats!)

• David Robinson’s R Programming Screencasts

• Neat and tidy data exploration with list-columns and purrr - 6/21/2020

R in the Real World

• Visualizing COVID cases in Belgium

• Searching a first name for a child

• Bayesian Portfolio Optimization: a use case for RStan

R in Academia

• Critique of “Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period” — Part 2: Proxies for incidence of coronaviruses

• Visualising the COVID-19 Pandemic

• Represent: geographical breakdown of a virtual seminar series

Resources

• A very short introduction to Tidyverse

New Packages

📦 Go Live for More New Pkgs 📦

CRAN

• {mdthemes} is on CRAN: markdown powered themes for {ggplot2}

• {glmm} 1.4.2: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation

• {FDboost} 0.3-3: Boosting Functional Regression Models

• {gemtc} 0.8-5: Network Meta-Analysis Using Bayesian Methods

• {gRim} 0.2.3: Graphical Interaction Models

• {Gammareg} 3.0: Classic Gamma Regression: Joint Modeling of Mean and Shape Parameters

• {define} 0.2.9: Create FDA-Style Data and Program Definitions

• {cutpointr} 1.0.32: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks

• {BGVAR} 2.0.0: Bayesian Global Vector Autoregressions

• {tilemaps} 0.1.0: Generate Tile Maps

• {revulyticsR} 0.0.1: Connect to Your ‘Revulytics’ Data

• {doc2concrete} 0.4.6: Measuring Concreteness in Natural Language

• {rLFT} 1.0.0: Processing Linear Features

• {RcmdrPlugin.BWS1} 0.1-2: R Commander Plug-in for Case 1 (Object Case) Best-Worst Scaling

• {frechet} 0.1.0: Statistical Analysis for Random Objects and Non-Euclidean Data

• {CFilt} 0.1.0: Collaborative Filtering by Reference Classes

• {anomalize} 0.2.1: Tidy Anomaly Detection

• {JMcmprsk} 0.9.8: Joint Models for Longitudinal and Competing Risks Data

• {sigmajs} 0.1.5: Interface to ‘Sigma.js’ Graph Visualization Library

• {yamlet} 0.4.8: Versatile Curation of Table Metadata

• {regress} 1.3-21: Gaussian Linear Models with Linear Covariance Structure

• {rbi.helpers} 0.3.2: ‘RBi’ Helper Functions

• {spinifex} 0.2.0: Manual Tours, Manual Control of Dynamic Projections of Numeric Multivariate Data

• {selac} 1.7.5: Selection Models for Amino Acid and/or Codon Evolution

• {adjclust} 0.5.99: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

• {treeheatr} 0.1.0: Heatmap-Integrated Decision Tree Visualizations

• {WEGE} 0.1.0: A Metric to Rank Locations for Biodiversity Conservation

• {loon.ggplot} 1.0.0: Making ‘ggplot2’ Plots Interactive with ‘loon’ and Vice Versa

• {ggrastr} 0.1.9: Raster Layers for ‘ggplot2’

• {bibliometrix} 3.0.2: Comprehensive Science Mapping Analysis

• {VFP} 1.3: Variance Function Program

• {STB} 0.6.4: Simultaneous Tolerance Bounds

• {GomoGomonoMi} 0.1.0: Animate Text using the ‘Animate.css’ Library

• {geodrawr} 1.0.1: Making Geospatial Objects

• {extras} 0.0.1: Helper Functions for Bayesian Analyses

• {DysPIAData} 0.1.1: Background and Pathway Data Used in ‘DysPIA’

• {bcfrailph} 0.1.0: Semiparametric Bivariate Correlated Frailty Model

• {SMDIC} 0.1.1: Identification of Somatic Mutation-Driven Immune Cells

• {RSBJson} 1.1.2: Handle R Requests from R Service Bus Applications with JSON Payloads

• {docinfeR} 2020.1.0: Automatic Reporter for Inference Analysis

• {docdescriptR} 2020.1.0: Automatic Reporter for Descriptive Analysis

• {quint} 2.1.0: Qualitative Interaction Trees

• {huxtable} 5.0.0: Easily Create and Style Tables for LaTeX, HTML and Other Formats

• {brm} 1.1.1: Binary Regression Model

• {mds} 0.3.2: Medical Devices Surveillance

GitHub or Bitbucket

• {zstdlite} - fast, configurable, in-memory compression of numeric vectors with Zstandard

• hordes, a module that makes R available from NodeJS.

• Take a {ghdump} to download GitHub repos

Updated Packages

• RVowpalWabbit 0.0.14: More Keeping CRAN happy

• shinymobile 0.7.0

Videos and Podcasts

• ggplot2 Text Customization with ggtext - Data Visualization in R

• Penguins Dataset Overview - iris alternative in R

• Impute missing data for #TidyTuesday voyages of captive Africans with tidymodels

• Catch up on e-RUM 2020 on YouTube

R Internationally

• Midi Player with R - Part 2 (French)

Tutorials

• Using the geofacet package to spatially arrange plots

• New NIMBLE cheatsheat available

• {tidybayes} AND {ggdist} 2.1.1

• Tidy Workflow with MplusAutomation

Upcoming Events in 3 Months

Events in 3 Months:

• 86th TokyoR (Virtual) Meetup: June 27, 2020

• A list of R conferences and meetings

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

Jobs

• From R Epidemics Consortium – RECON COVID-19 challenge – Two Paid Consultant Positions Available

Call for Participation

Your support will keep R Weekly team moving! 💡

Quotes of the Week

I realize I'm probably the last horse crossing the finish line here but... can we talk about with() and pipe-unfriendly functions???

mtcars %>%
with(lm(mpg ~ hp)) %>%
summary()

Soooo satisfying! 🤩#rstats

— Kelly Bodwin (@KellyBodwin) June 16, 2020

Inspired by @_ColinFay, I made a 30-day workout #shiny app based on my @orangetheory workout logs posted on @github.
- 📱shinyMobile: Shiny package using #Framework7 by @divadnojnarg
- 📊echarts4r: interactive #dataviz package by @jdatap
- ⏲️waiter: Loading screens by @jdatap pic.twitter.com/Mmem49nzmt

— Zhi Yang, PhD (@zhiiiyang) June 19, 2020

Hi #Rstats,#ggplot2 #rspatial twitter. Want to quickly get colors for your plots and maps? I use palette_explorer() by Martijn Tennekes. With this function you call an awesome shiny app that lets you choose colors for different cases and provides the corresponding code. Outtake: pic.twitter.com/9RkdI8CKb7

— Tobias Stalder (@toeb18) June 16, 2020