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**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 1d

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

Same as yesterday, but this time, making a 2D Discrete Random Walk with my #R #Package #RandomWalker

A scatterplot titled "2D Random Walk Non-Outliers Only." It displays multiple colored linear paths representing random walks in a 2D space, constrained to non-outlier data points. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y." The paths are laid out in a grid-like pattern with varied colors.

A scatterplot titled "2D Random Walks with Outliers Highlighted." It shows multiple colored random walk paths in a 2D space, with outliers prominently highlighted in bold red dashed lines. Non-outliers are displayed in lighter colors. Black dashed horizontal and vertical lines indicate threshold boundaries for outlier detection. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y."

A scatterplot titled "2D Random Walk Outliers Only." It showcases only the outlier random walk paths in a 2D space, displayed in various colors on a white background. Black dashed horizontal and vertical lines mark the threshold boundaries for outlier detection. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y."

A screenshot of R programming code written for generating 2D random walks, analyzing outliers, and plotting results. The code uses libraries such as RandomWalker, dplyr, ggplot2, and tidyr. It calculates confidence intervals, identifies outliers, and generates three types of plots: non-outliers, outliers, and highlighted outliers. The syntax is color-coded, with comments explaining each step.

#RStats #RProgramming #RandomWalks

**Steven P. Sanderson II, MPH** @spsanderson.com@bsky.brid.gy · 2d

Steven P. Sanderson II, MPH @spsanderson.com@bsky.brid.gy

Generate and visualize a Two Dimensional Random Walk in #R with #RandomWalker #R #RStats #RProgramming #Code

A screenshot of an R script for generating and visualizing 2D random walks. The code uses the RandomWalker, dplyr, and ggplot2 libraries to compute random walks, identify outliers, and plot results. Key sections include calculating confidence intervals, filtering outliers, and creating customized plots with distinct visual styling for outliers and non-outliers.

A 2D scatter plot titled "2D Random Walks with Outliers Highlighted," combining both non-outlier and outlier trajectories. Non-outlier walks are displayed in various colors, while outliers are highlighted in bold red dashed lines. Black dashed lines denote confidence interval boundaries, and the x-axis and y-axis are labeled "Cumulative Sum X" and "Cumulative Sum Y," respectively.

A 2D scatter plot titled "2D Random Walk Non-Outliers Only," showing multiple random walk trajectories in various colors. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y." The trajectories are concentrated around the origin and do not include any outliers.

A 2D scatter plot titled "2D Random Walk Outliers Only," depicting random walk trajectories in various colors. The x-axis is labeled "Cumulative Sum X," and the y-axis is labeled "Cumulative Sum Y." Black dashed lines mark boundary limits, highlighting walks identified as outliers that extend beyond the main cluster.

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 2d

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

Generate and visualize a Two Dimensional Random Walk in #R with #RandomWalker

#RStats #RProgramming #Code

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · 4d

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

If you want to use the C5.0 #algorithm for #Classification you might want to make sure your data is in the right format.

Let my #R package healthyR.ai help you get there.

See an example here: https://www.spsanderson.com/healthyR.ai/reference/hai_c50_data_prepper.html

library(ggplot2)
library(tibble)

Titanic <- as_tibble(Titanic)

hai_c50_data_prepper(.data = Titanic, .recipe_formula = Survived ~ .)
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 4
#>
#> ── Operations
#> • Factor variables from: tidyselect::vars_select_helpers$where(is.character)
rec_obj <- hai_c50_data_prepper(Titanic, Survived ~ .)
get_juiced_data(rec_obj)
#> # A tibble: 32 × 5
#> Class Sex Age n Survived
#> <fct> <fct> <fct> <dbl> <fct>
#> 1 1st Male Child 0 No
#> 2 2nd Male Child 0 No
#> 3 3rd Male Child 35 No
#> 4 Crew Male Child 0 No
#> 5 1st Female Child 0 No
#> 6 2nd Female Child 0 No
#> 7 3rd Female Child 17 No
#> 8 Crew Female Child 0 No
#> 9 1st Male Adult 118 No
#> 10 2nd Male Adult 154 No
#> # ℹ 22 more rows

#RProgramming #parsnip #C50

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · May 5

May 5

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode

**Steven Sanderson** @spsanderson@rstats.me · May 5

May 5

Steven Sanderson @spsanderson@rstats.me

Level up your R functions! Discover best practices for returning multiple values - from simple vectors to structured outputs. Perfect for data pipeline development.

https://www.spsanderson.com/steveondata/posts/2025-05-05/

#rstats #Rprog #Rcode

**Miki :rstats:** @miki_peltzer@techhub.social · Apr 30

Apr 30

Miki :rstats: @miki_peltzer@techhub.social

¡Reto #30DayChartChallenge 2025 COMPLETADO! 30 días, 30 visualizaciones con #RStats y #ggplot2.

Ha sido un viaje increíble explorando comparaciones, distribuciones, relaciones (¡animales!), series temporales (sociales, económicas) e incertidumbre (riesgo, exoplanetas, mapas...).

Puedes ver la galería completa (y todo el código) en mi repositorio:
https://github.com/michal0091/dataviz/tree/main/R/30DayChartChallenge2025

¡Gracias por seguir el reto! #dataviz #DataVisualization #DataStorytelling #ChallengeComplete #Rprogramming

PDF con recopilación del reto de #30DayChartChallenge

**Statistics Globe** @StatisticsGlobe@mastodon.social · Apr 22

Apr 22

Statistics Globe @StatisticsGlobe@mastodon.social

If you're still using raw R outputs for presentations, it's time for an upgrade! Tools like gtsummary bring your statistical results to life, making them much more digestible for non-technical audiences.

The visualization included here was originally shared in a post by Dr. Alexander Krannich. Thanks to Alexander for inspiring me to create this post.

More details are available at this link: http://eepurl.com/gH6myT

#statisticalanalysis #rprogramming #bigdata

**Steven Sanderson** @spsanderson@rstats.me · Apr 18

Apr 18

Steven Sanderson @spsanderson@rstats.me

Getting the medical policies

#### ALT Text Description

A screenshot of R programming code displayed in a code editor. The code utilizes the `httr2` and `tidyverse` libraries to perform a GET request to a specified JSON URL, extract relevant data, and filter for PDF filenames. The last step involves downloading the first PDF file from the extracted data. The code is structured with comments indicating each step of the process. The background of the code editor is a light blue gradient.

#R #rprogramming #programming

Replied in thread

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · Apr 17

Apr 17

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

@wronglang I also see that when I use rvest and the read_html() function it just goes on and on. so strange. maybe I need an RSelenium approach

#R #RStats #Web

**Jumping Rivers** @jumpingrivers@fosstodon.org · Apr 11

Apr 11

Jumping Rivers @jumpingrivers@fosstodon.org

New version of R is out!

Our Data Scientist, Russ Hyde, has put together a quick review of the key features and changes in R 4.5 — from new language features to graphics updates and more.

Read the full blog post here: https://www.jumpingrivers.com/blog/whats-new-r45/

#rstats #update #Rprogramming #datascience #opensource
https://www.jumpingrivers.com/blog/whats-new-r45/

www.jumpingrivers.com · Apr 10What's new in R 4.5.0?Here we summarise some of the more interesting changes that have been introduced in R 4.5.0.

**Statistics Globe** @StatisticsGlobe@mastodon.social · Apr 11

Apr 11

Statistics Globe @StatisticsGlobe@mastodon.social

Handling missing data is a critical step in data analysis, as failing to address it properly can lead to biased results and reduced analytical power. The mice package for R, short for Multivariate Imputation by Chained Equations, provides a robust and flexible framework for handling missing values through multiple imputation.

The visualizations shown below originate from the package website: https://github.com/amices/mice

More info: http://eepurl.com/gH6myT

#rprogramming #package #datascience

**Hacker News** @h4ckernews@mastodon.social · Apr 10

Apr 10

Hacker News @h4ckernews@mastodon.social

Big Book of R

https://www.bigbookofr.com/

Big Book of RBig Book of R

#HackerNews #BigBookOfR #RProgramming

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · Apr 8

Apr 8

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

I have hear a link with a bunch of R related material. It is constantly updated:

https://app.dotadda.io/teams/bad2fd50-1971-4103-903a-4c6406e3f445/dots

app.dotadda.ioDoTaddaGain total control of your team's critical project emails, files, and notes.

#R #RProgramming #Programming

**Steven Sanderson** @spsanderson@rstats.me · Apr 8

Apr 8

Steven Sanderson @spsanderson@rstats.me

I have hear a link with a bunch of R related material. It is constantly updated:

https://app.dotadda.io/teams/bad2fd50-1971-4103-903a-4c6406e3f445/dots

app.dotadda.ioDoTaddaGain total control of your team's critical project emails, files, and notes.

#R #RProgramming #Programming

**OpenSource** @OpenSource@activitypub.awakari.com · Apr 1

Apr 1

OpenSource @OpenSource@activitypub.awakari.com

We Replaced Our Marketing Team with AI: Here’s What Happened Follow along as we present the HOL...

https://medium.com/@marketingdatascience/we-replaced-our-marketing-team-with-ai-heres-what-happened-bf86f114a90b?source=rss------machine_learning-5

#digital-marketing #artificial-intelligence #r-programming #recommendation-system #machine-learning

Event Attributes

Our marketing agency staff in real life (top image) and their AI personas (bottom image). Image by Country Fried AI

Medium · Apr 1We Replaced Our Marketing Team with AI: Here’s What HappenedBy Marketing Data Science with Joe Domaleski

#digitalmarketing #artificialintelligence #rprogramming

**Steven Sanderson** @spsanderson@rstats.me · Mar 31

Mar 31

Steven Sanderson @spsanderson@rstats.me

Master while loops in R! Learn how to optimize performance, avoid common pitfalls, and write cleaner code. This guide is perfect for R programmers looking to enhance their skills.

Check it out: The Complete Guide to While Loops in R here: https://www.spsanderson.com/steveondata/posts/2025-03-31/

#R #RStats #Programming

**Steven P. Sanderson II, MPH** @stevensanderson@mstdn.social · Mar 31

Mar 31

Steven P. Sanderson II, MPH @stevensanderson@mstdn.social

Master while loops in R! Learn how to optimize performance, avoid common pitfalls, and write cleaner code. This guide is perfect for R programmers looking to enhance their skills.

Check it out: The Complete Guide to While Loops in R here: https://www.spsanderson.com/steveondata/posts/2025-03-31/

#R #RStats #Programming

**Data Science** @datascience@genomic.social · Mar 31

Mar 31

Data Science @datascience@genomic.social

Discover the power of property-based testing in R with the #quickcheck package! Seamlessly integrates with #testthat and offers a variety of generators for atomic vectors, lists, and tibbles. Perfect for ensuring your code's reliability. Check it out: https://github.com/armcn/quickcheck #rstats #rprogramming #testing

**Bioconductor** @bioconductor@genomic.social · Mar 25

Mar 25

Bioconductor @bioconductor@genomic.social

Bioconductor Kenya Course Kickoff! Our first Bioconductor course in Africa started in Nairobi! Day 1 featured learning and collaboration with Laurent Gatto, and Michael Landi. Sessions later this week by Fabricio Almeida-Silva, Laurah Ondari, and Zedias Chikwambi. Thanks to CZI and UL Global for funding.
#Bioconductor #Bioinformatics #RProgramming #Carpentries #Africa

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