Tootfinder

Everything is a linear model: #rstats #stats

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

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.

#Accessibility modelers using #r5r #rstats, check this GUI for playing around with R5 network. If many people find it useful, I would get signal if I should invest any more free time into it.

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

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.

New #rstats https://github.com/e-kotov/gridmaker Creates Eurostat GISCO compatible and INSPIRE-compliant grids with IDs that look like ‘CRS3035RES1000mN3497000E4448000’ or ‘1kmN3497E4447’. Input can be sf, or …

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...

Getting started with Shiny to make interactive web-apps with R: #rstats

Overview | Building Web Apps with R Shiny
This class will teach you the basics of Shiny app programming, giving you skills that will form the basis of almost any app you want to build. By the end of the class, you will have created a custom app that collects and saves data, allows users to dynamically visualize the data, and produces downloadable reports.

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

I have often been unhappy that the line type in a #ggplot2 legend is not clearly visible. Today, I found a simple solution: simply use
theme(legend.key.width = unit(1, "cm"))
Replace 1 with any other value that you like (not too small, of course!).
#rstats

We're making hand-made buttons/pins for the @… booth at @…
Any ideas for good motifs anyone?
#rstats

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

Does anyone have some practical advice how do visualize/summarize factor level smooth/slope differences for a #GAM fitted with sz? #gratia is only partially supporting it and especially difference_smooth() does not seem to work with sz.
Any pointers for packages, tutorials etc appreciated!
#rstats

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

Posit

The cool things you sometimes find in the CRAN submission queue. #rstats

Und wie nerdig ist eure Fediverse-Instanz?

The dbcooper package turns a database connection into a collection of functions. #rstats

GitHub - pipeline-tools/dbcooper: Create user-friendly accessor functions from a database connection
Create user-friendly accessor functions from a database connection - pipeline-tools/dbcooper

Here's an ad that Google Opal created for #rstats #spanishoddata https://ropenspain.github.io/spanishod

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

Here's an ad that Google Opal created for #rstats #spanishoddata https://ropenspain.github.io/spanishod

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

{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.

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

Make Dealing with Dates a Little Easier
Functions to work with date-times and time-spans: fast and user friendly parsing of date-time data, extraction and updating of components of a date-time (years, months, days, hours, minutes, and seconds), algebraic manipulation on date-time and time-span objects. The lubridate package has a consistent and memorable syntax that makes working with dates easy and fun.

📝🗃️ 𝗿𝗱𝗼𝗰𝗱𝘂𝗺𝗽: Dump ‘R’ Package Source, Documentation, and Vignettes into One File for use in LLMs #rstats #LLM is on CRAN https://www.ekotov.pro/rdocdum…

rdocdump: Dump R Package Documentation and Vignettes into One File
Dump R Package Documentation and Vignettes into One File

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

{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

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

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

paint() your data when you print them to make them easier to grasp: #rstats #console

GitHub - MilesMcBain/paint: paint the data
paint the data. Contribute to MilesMcBain/paint development by creating an account on GitHub.

🚀 𝘀𝗽𝗮𝗻𝗶𝘀𝗵𝗼𝗱𝗱𝗮𝘁𝗮 0.2.0 is here. As before, you are getting nicely formatted Open Mobility Big Data released by the Spanish Ministry of Transport and Sustainable Mobility (MITMS) in a reproducible way. #rstats #opendata

Get Spanish Origin-Destination Data
Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at . This package simplifies the management of these large datasets by providing tools to download zone boundaries, handle associated origin-destination data, and process it efficiently with the duckdb database interface. Local caching minimizes repeated downloads, streamlining wo…

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

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.

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

Ice Cream for R Programmers - Turtletopia: a blog about programming in R
Icecream is a tool which may serve as a more sophisticated replacement of using print while debugging.

I am more fluent in LaTeX than in plotmath expression. If you are the same, latex2exp will make your life easier. https://cran.r-project.org/web/packages/latex2exp/vignettes/using-latex2exp.html

Get 9-30x speed doing areal-weighted interpolation with my new {𝐝𝐮𝐜𝐤𝐬𝐟} #rstats package compared to {sf}/{areal}. Experimental, but tested against both {areal} and {sf}. https://github.com/e-kotov/ducksf . Despite…

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

{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats

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.

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.

{ggrain}: Raincloud plots for ggplot2: #rstats #dataviz

GitHub - njudd/ggrain: {package} Make beautiful Raincloud plots in R!
{package} Make beautiful Raincloud plots in R! Contribute to njudd/ggrain development by creating an account on GitHub.

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.

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

{testthat} is great for automatic testing. Here are some tricks for the heavy user: #rstats

{ivs} makes it easier to work with intervals: #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

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

GitHub - cutterkom/generativeart: Create Generative Art with R
Create Generative Art with R. Contribute to cutterkom/generativeart development by creating an account on GitHub.

A template for data analysis projects structured as R packages (or not) #rstats #datascience

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

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

Tidy Modeling with R: #rstats #machinelearning

Tidy Modeling with R
The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This book provides a thorough introduction to how to use tidymodels, and an outline of good methodology and statistical practice for phases of the modeling process.

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

GitHub - jokergoo/pkgndep: Analyzing Dependency Heaviness of R Packages
Analyzing Dependency Heaviness of R Packages. Contribute to jokergoo/pkgndep development by creating an account on GitHub.

Extract tables from pdfs with {tabulapdf} #rstats #datasciece

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.

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

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

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

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

Big Book of R

{nplyr} has helper functions to work on nested dataframes: #rstats #datascience

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…

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

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

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

{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.

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.

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.

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

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

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

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

Sometimes (often) one ends up needing to run older versions of R using older versions of packages. Evercran might be just the tool to help with that: #RStats

GitHub - r-hub/evercran: Any version of R -- with the matching version of CRAN
Any version of R -- with the matching version of CRAN - r-hub/evercran

{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

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.

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

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

Advanced and Fast Data Transformation in R
A large C/C++-based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust, and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), an…

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

This looks helpful: #rstats #optimization

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