Tootfinder

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

Any #rstats people know how to capture / react to a plot.ly hover event in #shiny (using a #quarto doc)? The examples I'm finding are old / don't work..

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

The {cli} package makes it easy to show pretty and informative messages to the user of your code. #rstats

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

I just couldn't help myself: Sure, the documentary photo project @… are doing on the villages of the Argentinean Pampa might be rooted in #analogphotography.
But who says that one can’t mix in some #rstats #rayshader data visualisations for the forthcoming project website? As good an excuse as any for looking at how those villages with their small populations are distributed.
A slightly longer writeup – that also explains the pattern one can see – is here: https://tzovar.as/rayshading-argentina/

I have programmed a small #rstats script combining the web-based #API of our SolarEdge #photovoltaic system with the local API of our go-e #wallbox.
If the car charges, the script looks up the production of the PV system and increases/decreases the charging current for the car accordingly. This makes the most of the collected #solar power. What now runs on an old #Raspberry Pi would’ve easily cost us 500 EUR installation costs – and I have full control over everything! 🤓🤩

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

I have programmed a small #rstats script combining the web-based #API of our SolarEdge #photovoltaic system with the local API of our go-e #wallbox.
If the car charges, the script looks up the production of the PV system and increases/decreases the charging current for the car accordingly. This makes the most of the collected #solar power. What now runs on an old #Raspberry Pi would’ve easily cost us 500 EUR installation costs – and I have full control over everything! 🤓🤩

This new blog by Nick Clark providing tips for ecological modeling with a focus on time-series/forecasting and #rstats is excellent. I highly recommend checking it out.
https://ecogambler.netlify.app/blog/<…

GAMbler | GAMbler
This is a growing collection of interesting and (hopefully) useful tips for ecological modeling, with (perhaps too much) emphasis on Generalized Additive Models.

{constructive} prints code that can be used to recreate R objects. Like dput, but better... #rstats

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.

The University of Manchester is now recruiting students for its 2024 intake in the MSc Social Network Analysis :fediverse: :igraph: :rstats: - run by the wonderful Dr Elisa Bellotti, a friend and dear conference buddy of mine
More information on the programme:
https://www.

MSc Social Network Analysis (2024 entry) | The University of Manchester
Enhance your knowledge of social network analysis and help meet increasing national and international demand for skills in this area with this course.

just a couple years of language and package updates, what could go wrong? #Rstats

Function-oriented Make-like declarative workflows for R #rstats

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

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

The University of Manchester is now recruiting students for its 2024 intake in the MSc Social Network Analysis :fediverse: :igraph: :rstats: - run by the wonderful Dr Elisa Bellotti, a friend and dear conference buddy of mine
More information on the programme:
https://www.

MSc Social Network Analysis (2024 entry) | The University of Manchester
Enhance your knowledge of social network analysis and help meet increasing national and international demand for skills in this area with this course.

Yet another package to speed up different calculations in R: #rstats

GitHub - SebKrantz/collapse: Advanced and Fast Data Transformation in R
Advanced and Fast Data Transformation in R. Contribute to SebKrantz/collapse development by creating an account on GitHub.

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

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.

What They Forgot to Teach You About R: #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
400+ Free R programming books

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

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

This looks helpful: #rstats #optimization

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

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

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

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

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

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.

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.

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

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

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

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

Not sure any longer which libraries your script actually needs? #rstats

Not sure any longer which libraries your script actually needs? #rstats

Dplyr 1.1.0 changed how joins are working: #rstats

Beautiful palettes based on art for R and python: #rstats #ggplot

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

Everything is a linear model: #rstats #stats

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

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

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

A pictures says more than 1000 words. How much more can an audio representation of your data tell you? #rstats <…

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

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.

Use cookies in shiny apps: #rstats #shiny

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

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

Cute comics of R functions: #rstats

{slider} helps with aggregation over (sliding) windows, both index and time period based: #rstats

{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

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

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

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

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 <…

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

The inner working of parquette/arrow data in R: #rstats

Make sure your code follows a consitent style using the {lintr} package. #rstats

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

{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: https://github.com/r-lib/actions

Do you (sometimes) use print() or message() for debugging your code? Next time you can use {icecream} instead: https://turtletopia.github.io/2022/07/28/ice-cream-for-r-programmers/

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

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