Tootfinder

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

A Grammar of Nested Data Manipulation
Provides functions for manipulating nested data frames in a list-column using dplyr syntax. Rather than unnesting, then manipulating a data frame, nplyr allows users to manipulate each nested data frame directly. nplyr is a wrapper for dplyr functions that provide tools for common data manipulation steps: filtering rows, selecting columns, summarising grouped data, among others.

#rstats mapgl is now on CRAN with flowmaps support! As previewed at #MobileTartu (Mobility Lab, University of Tartu) and Association of Geographic Information Laboratories in Europe (#AGILE) conferences! (Video by Kyle Walker). Backend is Flowmap.gl

A very detailed walk-through of adding a property to #Wikidata, while contributing some data cleanup using #Rstats
https://

{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

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

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

A general vectorised switch() — case_match
case_match() is deprecated. Please use recode_values() and replace_values() instead, which are more powerful, have more intuitive names, and have better safety. In addition to the familiar two-sided formula interface, these functions also have from and to arguments which allow you to incorporate a lookup table into the recoding process. This function allows you to vectorise multiple switch() statements. Each case is evaluated sequentially and the first match for each element determines the cor…

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.

I'm working on something big: A new package for async communication between R and Emacs. The output should be a terminal run in Emacs, at the level of Arf and Radian.
If you use #rstats on #emacs stay tunned

New blog post: Effective mental health techniques according to therapists and the public on Reddit – Dr Paul Matthews
#MentalHealth #RStats

RE: #rstats

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

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

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

What They Forgot to Teach You About R: #rstats

RE: #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...

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

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.

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.

RE: #rstats

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.

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

Everything is a linear model: #rstats #stats

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

{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.

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.

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.

{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.

#TIL that #RStudio supports multiple levels of code sections. The level depends on the number of # signs at the beginning of the line.
# First Level -----
## Second level ------
This is also reflected in the document outline you get with Cmd Shift O (on a Mac, I guess it's Ctrl Shift O anywhere else).
Why did no-one tell me that earlier?!?
#rstats

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.

Use cookies in shiny apps: #rstats #shiny

{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

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

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

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.

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

Julia Silge
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.

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

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

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

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.

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.

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

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.

{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.

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

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

{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.

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

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

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

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

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

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

Allison Horst - R Packages & Functions

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

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

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

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.

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.

Make fancy tables: #rstats #tables

This looks helpful: #rstats #optimization

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

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

Extract Tables from PDF Documents
Bindings for the Tabula Java library, which can extract tables from PDF files. This tool can reduce time and effort in data extraction processes in fields like investigative journalism. It allows for automatic and manual table extraction, the latter facilitated through a Shiny interface, enabling manual areas selection\ with a computer mouse for data retrieval.

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.

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

It has happened to the best of us: You forgot the name of a function or the package that function was in but you are sure its there somewhere and does exactly what you need! Introduce {forgot}, it helps you find that function: https://github.com/parmsam/forgot

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 #RStats

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. - cxli233/FriendsDontLetFriends

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

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

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.

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