Lmst

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

#R #RStats #RProgramming #RandomWalks #Visual #ggplot2

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 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 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.

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

#R #RStats #RProgramming #RandomWalks #Visual #ggplot2

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

#R #RStats #RProgramming #Code

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.

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 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.

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

#R #RStats #RProgramming #Code

Jiangshan Lai et al. analyzed the use of R💻 in #ForestryResearch.
#DataAnalysis #OpenSource #RProgramming
https://doi.org/10.1093/jpe/rtad047

The references to the most widely utilized R packages (appearing in over 100 articles) across the eight top forestry journals from 2013 to 2022.

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

#R #RProgramming #parsnip #C50 #tips #Rtips

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

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

#R #RProgramming #parsnip #C50 #tips #Rtips

🎯 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 #DataSci #R4ds #blog #function #RProgramming

🎯 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 #DataSci #R4ds #blog #function #RProgramming

¡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

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 #coding

Getting the medical policies

#R #rprogramming #programming #blog

#### 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.

@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 #RProgramming #rvest #tidyverse

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/

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 #data

🐢📚 Ah, the "Big Book of R"—a tome so comprehensive it could double as a doorstop or a weapon in a pinch. It's like someone took the syllabus of every course ever offered in R and threw them into a blender, producing the world's most overwhelming smoothie. 🥤🚀 Who needs concise when you can have a category for every letter of the alphabet, right? 🙄
https://www.bigbookofr.com/ #BigBookOfR #RProgramming #DataScience #OverwhelmingLearning #HumorInTech #HackerNews #ngated

Big Book of R

https://www.bigbookofr.com/

#HackerNews #BigBookOfR #RProgramming #DataScience #Analytics #Learning

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

#R #RProgramming #Programming #OpenSource #DoTadda #List #Blog

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

#R #RProgramming #Programming #OpenSource #DoTadda #List #Blog

💻 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 #Code #Blog #RProgramming

#RProgramming

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