Tootfinder

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.

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

#rstats {duckspatial} 0.9 is on CRAN, makes @… 🦆 #spatial 🌍 more user-friendly, enabling faster big data spatial ops in R. Adrišn Cidree shared a great breakdown of how we ( …

{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

#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

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

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

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

#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

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

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

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

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

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

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.

What They Forgot to Teach You About 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

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.

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

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.

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

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.

{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.

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.

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...

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…

{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.

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

{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.

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.

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.

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).

{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

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

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.

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

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

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

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

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

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.

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 {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.

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.

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

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.

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

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

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

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

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