Tootfinder

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.

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.

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.

Oh and don't get me started with all this package version mismatch in #Python.
What absolute nonsense!
#rstats #cran is not perfect, but I have never ever had an issue with versions not matching between packages.

Didn't know that {skimr::skim} worked an arrow object. It's pretty cool, but it hits your RAM pretty hard. That's only a 70MB directory of parquet files, and my RAM usage went up by ~1.3GB. #RStats Code: https://ray.so/fCoXwso

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

GitHub - parmsam/forgot: R package to search for that one function you need in that one package
R package to search for that one function you need in that one package - parmsam/forgot

#r5rgui has been on CRAN for a few days now! Your handy helper to quickly visually explore routes built by #rstats #r5r ( @…

My goal always was to run 500 km in one year. Today I made it for the first time 🥳
I built some visualisations in #rstats to keep me motivated throughout the year, of course!
#running

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.

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.

When #teaching #Rstats / #statistics courses, I (and several colleagues of mine) made the experience that it is indeed pretty hard for a lot of students to cope with the file system on their computer. They have questions like: How do I know the "path" of a file? How do I control in which directory something is saved? WHY DO I NEED THIS?!?
I don't want to make fun of these students because I know that this could be because operating systems are increasingly obscuring file/directory systems from their users.
But if I want to teach students to use a scripting/ #programming language independently, that's a real problem!
So my questions to you are: Do you have the same impression when teaching? And if so: How do you deal with this from a teaching perspective? To be honest, I don't want to use precious course time to teach the absolute basics of computers' file systems in the first session(s).

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

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

#Rstats problems: Did you ever think that the dots for dotted lines are a bit too far apart per default? TIL that it's super easy to change this.
In #ggplot2, simply try something like:
scale_linetype_manual(
values = c(a = "dotted", b = "11")
)
The '11' means: 1 point for a dot, 1 point for a gap. Find out more here: https://stackoverflow.com/questions/25788945/how-to-define-more-line-types-for-graphs-in-r-custom-linetype

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

Hmm — an htmlwidget serializer. I'd like to see that out in the wild. #RStats

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

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

What They Forgot to Teach You About R: #rstats

On my way to the U Cologne & the SFB „Prominence in Language“ - thanks to @… 😊 See you soon! #rstats #udpipe #linguistics

Spent my day ranting on Mastodon and helping teach applied statistics with #rstats to first year students.
I consider that a day well spent.
#PhDLife #PostdocLife #AcademicChatter

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

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

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

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…

Just as I've started to get a pretty comfortable with all the coordinate representations, projections, etc., I see this. If I would've came across an older dataset with this before now, I'm not sure what I would've done. Tried an internet search out of curiosity, but didn't come up with much. sp::char2dms converts it to numerical coordinates, but I didn't see any other functions/pkgs out there that does that conversion.

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.

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

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.

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 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.

This looks helpful: #rstats #optimization

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

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.

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

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

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.

{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.

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.

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

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.

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…

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

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

{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.

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

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.

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.

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

The past and future of Shiny - Posit

Use cookies in shiny apps: #rstats #shiny

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

{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.

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

{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.

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

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

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

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

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

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.

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

Allison Horst - R Packages & 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