Tootfinder

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.

🚀 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…

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

{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.

R learning for applied statistics by Chenxin Li: #rstats

Deaths by domain of protest over 12 Bolivian presidencies.
Same data, same graphing technique, but sorting the categories (by which occurs across the most bins) brings visual order to this data visualization. #dataviz #rstats

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

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

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...

A new experimental, but very simple #rstats package #rdocdump saves all package source code, documentation and vignettes into plain text file or into a string. Very useful for feeding into an LLM or tokenizing and setting up Retrieval-Augmented Generation workflow. Feel free to try it out. Caution: fu…

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

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

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.

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…

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

I use my #rdocdump #rstats package myself very often to make sure LLM always has the most recent docs of an R package that I am using. In one line of code you get a text file with basically all package docs and vignettes. Not on

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

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.

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

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

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.

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

Everything is a linear model: #rstats #stats

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.

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.

Use cookies in shiny apps: #rstats #shiny

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

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

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

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.

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

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

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

{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

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

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

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

Visualize dependencies between functions: #rstats

Make fancy tables: #rstats #tables

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

Allison Horst - R Packages & Functions

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.

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

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...

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

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