Tootfinder

Im using case_when() quite a lot, case_match() is new to me: #rstats

A general vectorised switch() — case_match
This function allows you to vectorise multiple switch() statements. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used. case_match() is an R equivalent of the SQL "simple" CASE WHEN statement. Connection to case_when() While case_when() uses logical expressions on the left-hand side of the formula, case_match() uses values to match against .x with. The following two statement…

Linear programs help to find optimal solutions based on a set of constrains. I used {ompr} before, but the new package {tidyLP} looks promising and integrates with the tidyverse. #rstats #linearprograms #optimization

GitHub - colin-fraser/tidyLP: Tidy Linear Programs
Tidy Linear Programs. Contribute to colin-fraser/tidyLP development by creating an account on GitHub.

Base pipe vs magrittr pipe: a thread by TimTeaFan: #rstats

Thread by @TimTeaFan on Thread Reader App
@TimTeaFan: I see still a lot of #RStats tweets about the base pipe vs. the {magrittr} pipe, often in favor of the former. I have to admit: I still stick to the magrittr pipe and here is...

All the talks from rstudio::conf 2022: https://www.rstudio.com/conference/2022/2022-conf-talks/ 👩‍🏫 Keynotes: https://www.

Are you making slides with Quarto or R Markdown and need a timer e.g. for breaks or group work? There is the {countdown} package for you: #rstats

{ggbump} creates elegant bump charts in ggplot. #ggplot #rstats

GitHub - davidsjoberg/ggbump: A geom for ggplot to create bump plots
A geom for ggplot to create bump plots. Contribute to davidsjoberg/ggbump development by creating an account on GitHub.

Highlight a certain aspect of your data in ggplot: #rstats #dataviz

The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats

GitHub - fastverse/fastverse: An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R
An Extensible Suite of High-Performance and Low-Dependency Packages for Statistical Computing and Data Manipulation in R - fastverse/fastverse

Keynote from rstudio::conf 2022: The past and future of shiny. #rstats

The past and future of Shiny - Posit

There are many situations were you need access to different R versions: rig is a way to manage them #rstats

GitHub - r-lib/rig: The R Installation Manager
The R Installation Manager. Contribute to r-lib/rig development by creating an account on GitHub.

Customize what happens when you start R: #rstats #environment

Low key product launch to end the year: https://cct-development-management-bot.cct-datascience.xyz/index.html
It's a long story, but we've put out a very niche RAG bot relating to development management in

"Graphic Design with #ggplot2": All material from this 2 day workshop can be found at https://rstudio-conf-2022.github.io/ggplot2-graphic-design/

Not sure any longer which libraries your script actually needs? #rstats

Script for finding loaded but unused packages in R scripts
Script for finding loaded but unused packages in R scripts - analyze_pkg_usage.R

Rawgraphs is another tool to make figures without R: #dataviz #ui

RAWGraphs
A free and open source tool for data visualization.

Do you (sometimes) use print() or message() for debugging your code? Next time you can use {icecream} instead: #rstats

Beautiful palettes based on art for R and python: #rstats #ggplot

GitHub - BlakeRMills/MetBrewer: Color palette package in R inspired by works at the Metropolitan Museum of Art in New York
Color palette package in R inspired by works at the Metropolitan Museum of Art in New York - BlakeRMills/MetBrewer

You dont like the default color palett of #ggplot? You can change it: https://stackoverflow.com/questions/10504724/change-the-default-colour-palette-in-ggplot

Change the default colour palette in ggplot
I have written a function that returns a vector of color names: custom.colors <- function(n) { palette <- c("dodgerblue1", "skyblue4", "chocolate1", "seagreen4", "bisque3", ...

{slider} helps with aggregation over (sliding) windows, both index and time period based: #rstats

GitHub - r-lib/slider: Sliding Window Functions
Sliding Window Functions. Contribute to r-lib/slider development by creating an account on GitHub.

Everything is a linear model: #rstats #stats

{ggchicklet}: library for rounded Segmented Column Charts: #ggplot #rstats

GitHub - hrbrmstr/ggchicklet: 🀫 Create Chicklet (Rounded Segmented Column) Charts
🀫 Create Chicklet (Rounded Segmented Column) Charts - hrbrmstr/ggchicklet

{lubridate} makes working with dates in R just that little bit easier: #rstats #dates<…

A pictures says more than 1000 words. How much more can an audio representation of your data tell you? #rstats

sonify: Data Sonification - Turning Data into Sound
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.

Do you need inspiration how to present a dataset in a clear figure and what package to use? Check out #rstats

From data to Viz | Find the graphic you need
A classification of chart types based on their input data format.

Keep track of the TODO notes in your code: #rstats #todo

GitHub - dokato/todor: TODOr - RStudio add-in for finding TODO, FIXME, CHANGED etc. comments in your code.
TODOr - RStudio add-in for finding TODO, FIXME, CHANGED etc. comments in your code. - dokato/todor

The {esquisse} package makes it easy to plot your data in different ways with a drag and drop interface: #rstats

Explore and Visualize Your Data Interactively
A shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

Want to check the google trends for a topic? Use {gtrendsR} directly from within your favorite language: #googletrends

GitHub - PMassicotte/gtrendsR: R functions to perform and display Google Trends queries
R functions to perform and display Google Trends queries - PMassicotte/gtrendsR

A curated list of awesome tools to assist 📦 development in R programming language. #rstats #📦

Video tutorials for modern ideas and open source tools. #python

Code. Simply. Clearly. Calmly.
Short and simple video lessons that start from scratch. Tools and thoughts that might make your professional life more enjoyable.

If you just need a pretty figure from a dataset and not the full power of R, have a look at #gui

How nice @… made it into #researchdatamanagement. Also #Coscine (developed at

Data Organization Made Easy: Comprehensive Folder Structure Template for Early Career Life/Natural Science Researchers | Data Science Journal

The functions in the {withr} package allow to change your environment temporarily. E.g. create a temp file for a {testthat} test and clean it up afterwards. #rstats

Run Code With Temporarily Modified Global State
A set of functions to run code with safely and temporarily modified global state. Many of these functions were originally a part of the devtools package, this provides a simple package with limited dependencies to provide access to these functions.

Use cookies in shiny apps: #rstats #shiny

Want to use {pak} as backend for {renv} actions? Set the variable RENV_CONFIG_PAK_ENABLED = TRUE #renv

[pak] Use pak for package installation · Issue #1210 · rstudio/renv
Related issues: #1268 #1331 #1330 #1334

The {purrr} package is a powerfull way to replace loops. The {furrr} package takes this approach one step further by parallel execution: #rstats

Apply Mapping Functions in Parallel using Futures
Implementations of the family of map() functions from purrr that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

Cute comics of R functions by @…: https://allisonhorst.com/r-packages-functions

Allison Horst - R Packages & Functions

quick and easy way to build a website for your r package: #rstats #package

Build websites for R packages
Generate an attractive and useful website from a source package. pkgdown converts your documentation, vignettes, README, and more to HTML making it easy to share information about your package online.

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out:

Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: #RStats

r-graph-gallery.com provides example code for a variety of chart types, both in base R and ggplot: #rstats #ggplot

The R Graph Gallery – Help and inspiration for R charts
The R graph gallery displays hundreds of charts made with R, always providing the reproducible code.

Make sure your code follows a consitent style using the {lintr} package. #rstats

A Linter for R Code
Checks adherence to a given style, syntax errors and possible semantic issues. Supports on the fly checking of R code edited with RStudio IDE, Emacs, Vim, Sublime Text, Atom and Visual Studio Code.

Examples of creative data visualizations, mostly ggplot: #ggplot #dataviz

GitHub - tashapiro/tanya-data-viz: Personal data visualization side projects.
Personal data visualization side projects. Contribute to tashapiro/tanya-data-viz development by creating an account on GitHub.

Add richer tooltips to ggiraph with kableExtra and friends: #dataviz

The {cli} package makes it easy to show pretty and informative messages to the user of your code. #rstats #terminal

GitHub - r-lib/cli: Tools for making beautiful & useful command line interfaces
Tools for making beautiful & useful command line interfaces - r-lib/cli

Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML

Make interactive ggplots with {ggiraph}: #ggplot #dataviz

r-charts.com provides example code for a variety of chart types, both in base R and ggplot: #rstats #ggplot…

R CHARTS | A collection of charts and graphs made with the R programming language
Over 1400 graphs with reproducible code divided in 8 big categories and over 50 chart types, in addition of tools to choose and create colors and color palettes

The inner working of parquette/arrow data in R: #rstats

Unpacking Arrow Datasets – Notes from a data witch
A comment on how Datasets work in Apache Arrow. I’m not really sure who the audience for this one. Am I just talking to myself? Probably.

Enrich your ggplots with extra panels along the x and y axis: #ggplot #dataviz <…

GitHub - jtlandis/ggside: ggplot2 extension allowing for plotting various geometries as side panels using the ggplot2 API
ggplot2 extension allowing for plotting various geometries as side panels using the ggplot2 API - jtlandis/ggside

Do you have a long running calculation freezing up your shiny app? {callr} or {crew} might help: https://discindo.org/post/asynchronous-execution-in-shiny/

{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats

R doesnt need to be a hard and scientific tool 📈. You can use it to make art 🎨: #rstats

{constructive} prints code that can be used to recreate R objects. Like dput, but better... #rstats

Display Idiomatic Code to Construct Most R Objects
Prints code that can be used to recreate R objects. In a sense it is similar to base

Make paint-by-number pictures in R: #rstats

GitHub - cj-holmes/paintr: Create paint by numbers images
Create paint by numbers images. Contribute to cj-holmes/paintr development by creating an account on GitHub.

Add highlighting to your quarto presentation using the RoughNotation library: #rstats

Function-oriented Make-like declarative workflows for R #rstats

GitHub - ropensci/targets: Function-oriented Make-like declarative workflows for R
Function-oriented Make-like declarative workflows for R - ropensci/targets

Friends Don't Let Friends Make Bad Graphs! Do you agree with the examples of bad graphs and the alternatives Chenxin Li (@chenxinli2.bsky.social) lists at https://github.com/cxli233/FriendsDontLetFriends

GitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain types of data visualization - What are they and why are they bad.
Friends don't let friends make certain types of data visualization - What are they and why are they bad. - GitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain ty...

Are you interested in how dependency-heavy your (or another) package is and why? #rstats

A template for data analysis projects structured as R packages (or not) https://github.com/Pakillo/template by @…

GitHub - Pakillo/template: A template for data analysis projects structured as R packages (or not)
A template for data analysis projects structured as R packages (or not) - Pakillo/template

Handy RStudio addins for reshaping and navigating in code: #rstats #rstudio

Using fonts in R graphics can be tricky at times. {showtext} aims to make it easier: #rstats

There are frameworks like {golem} and {rhino} to make shiny development more robust, but I like the concept of {shinytest2} in providing a testing framework for pure shiny. https://rstudio.github.io/shinytest2/index.html

Lets be honest, we spend too much time cleaning data. {janitor} can help with that: #rstats #datasciece

Yet another cool and useful package from @…: {minixml} lets you build xml files in a programmatic way. I see good use for it in one of the internal packages at work... https://coolbutu…

Beside the {report} package (yesterdays note) there are more tools in the easystats collection. #rstats

GitHub - easystats/easystats: :milky_way: The R easystats-project
:milky_way: The R easystats-project. Contribute to easystats/easystats development by creating an account on GitHub.

Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @…

GitHub - MilesMcBain/breakerofchains: Break your chain at the cursor line. Run the first bit. See the output. Be free.
Break your chain at the cursor line. Run the first bit. See the output. Be free. - MilesMcBain/breakerofchains

Need some data to test a plot idea or algorithm? On #rstats #synthetic…

Automatically describe data and models as text using the {report} package. #rstats

If you set limits for a scale (e.g. x-axis) in ggplot, how would you like data outside of that range be handled? There is the oob parameter for that and a set of functions to use with it: https://scales.r-lib.org/reference/oob.html

Sometimes you get data in less than optimal format, e.g. as a png of a figure 😭... In that case https://cran.r-project.org/web/packages/metaDigitise/vignettes/metaDigitise.html might be the rescue.

Sometimes you need to know the exact color used in a picture, or you might want to make a pallet from a foto. Check out {eyedroppeR} by @…: https://gradientdescending…

Select colours from an image in R with {eyedroppeR} - Dan Oehm | Gradient Descending
Click on an image to extract the hex code of the selected pixel and return it all within R

Visualize dependencies between functions: #rstats

GitHub - lewinfox/foodwebr: Function dependency graphs in R
Function dependency graphs in R. Contribute to lewinfox/foodwebr development by creating an account on GitHub.

Find the best contrast between one color and a list of options, e.g. for labels in geom_tile: {prismatic::best_contrast()} https://emilhvitfeldt.github.io/prismatic/reference/best_contrast.html

If you feel you should be reading more: #rstats #ebooks

Big Book of R

Make fancy tables: #rstats #tables

This looks helpful: #rstats #optimization

Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: #rstats

More Efficient Tidyverse Code, Using Polars in the Background
Polars is a cross-language tool for manipulating very large data. However, one drawback is that the R implementation has a syntax that will look odd to many R users who are not used to Python syntax. The objective of tidypolars is to improve the ease-of-use of Polars in R by providing tidyverse syntax to polars.

Add some swag to your ggplots, with fontawesome symbols and colors: #rstats

Adding social media icons to charts with {ggplot2} – Nicola Rennie
Adding social media icons to your data visualisation is a great, concise way to put your name on your work, and make it easy for people to find your profile from your work. This blog post explains how to add social media icons to {ggplot2} charts.

Do you need better performance than what the standard #tidyverse functions have? {collapse} might be worth a look: https://sebkrantz.github.io/collapse/

{ggdist}: Visualizations of distributions and uncertainty #rstats #ggplot

R learning for applied statistics by Chenxin Li: #rstats

GitHub - cxli233/Online_R_learning: Online R learning for applied statistics
Online R learning for applied statistics . Contribute to cxli233/Online_R_learning development by creating an account on GitHub.

If you use Quarto to make presentations for a professional setting, it is important to choose the right theme, e.g. #rstats

GitHub - EmilHvitfeldt/quarto-nes-theme: A Quarto reveal.js theme based on NES.css
A Quarto reveal.js theme based on NES.css. Contribute to EmilHvitfeldt/quarto-nes-theme development by creating an account on GitHub.

{piggyback} makes it easier to attach large files (e.g. input data) to code in github repos: #rstats

GitHub - ropensci/piggyback: :package: for using large(r) data files on GitHub
:package: for using large(r) data files on GitHub. Contribute to ropensci/piggyback development by creating an account on GitHub.

{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats

Wrap a function to capture errors — safely
Creates a modified version of .f that always succeeds. It returns a list with components result and error. If the function succeeds, result contains the returned value and error is NULL. If an error occurred, error is an error object and result is either NULL or otherwise.

TidyX: screencasts explaining different aspects of the R language and the coding process. #rstats

TidyX
TidyX is a screen cast where we discuss how Data Science topics and code work line-by-line, explaining what they did and how the functions they used work. We also break down the visualizations they create and talk about how to apply similar approaches to other data sets. The objective is to help more people learn R and get involved in the TidyTuesday community. The hosts are Ellis Hughes (@ellis_hughes) and Patrick Ward (@OSPpatrick). Ellis has been working with R since 2015 and has a backgr…

Primer to get you started with Optimization and Mathematical Programming in R #rstats

CRAN Task View: Optimization and Mathematical Programming
This CRAN Task View contains a list of packages that offer facilities for solving optimization problems. Although every regression model in statistics solves an optimization problem, they are not part of this view. If you are looking for regression methods, the following views will also contain useful starting points: MachineLearning, Econometrics, Robust Packages are categorized according to the following sections. See also the “Related Links” and “Other Resources” sections at the end.

Interactive resizing of picture and table content in Rmd and Quarto: #rstats

I did not know about ggplot_build() before. It can come in handy in situations where you want to access computed metrics of a #ggplot. https://ggplot2.tidyverse.org/reference/ggplot_build.html…

Build ggplot for rendering. — ggplot_build
build_ggplot() takes the plot object, and performs all steps necessary to produce an object that can be rendered. This function outputs two pieces: a list of data frames (one for each layer), and a panel object, which contain all information about axis limits, breaks etc. The ggplot_build() function is vestigial and build_ggplot() should be used instead.

Follow along when @… walks you through how she tackles a new dataset: https://www.youtube.com/c/JuliaSilge

Julia Silge
Share your videos with friends, family, and the world

The {conflicted} package makes sure that namespace conflicts are solved explicitly and prevents unpleasent surprises: #rstats

GitHub Actions for the R language: Makes automatic testing of your R package much easier and making sure your package works on different OS and R versions is a matter of just a few lines of yaml: #rstats

GitHub - r-lib/actions: GitHub Actions for the R community
GitHub Actions for the R community. Contribute to r-lib/actions development by creating an account on GitHub.

{ggblanket}, a wrapper around #ggplot for quick, explorative plots with sensible defaults and less code. https://davidhodge931.github.io/ggblanket/

Simplify ggplot2 Visualisation
Simplify ggplot2 visualisation with ggblanket wrapper functions.

I have a habbit of making (too) many (small) packages for functionality that might be reused in different context. {box} might be an alternative by making scripts into modlues that can be loaded: #RStats <…

‘box’: Write Reusable, Composable and Modular R Code
A modern module system for R. Organise code into hierarchical, composable, reusable modules, and use it effortlessly across projects via a flexible, declarative dependency loading syntax.

{spiralize} can be used to highlight cyclic data, e.g. multi year time series. #rstats

GitHub - jokergoo/spiralize: Visualize data on spirals
Visualize data on spirals. Contribute to jokergoo/spiralize development by creating an account on GitHub.