¡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

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

Getting the medical policies

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

Big Book of R

https://www.bigbookofr.com/

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

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

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/

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/

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 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

Local regression is a non-parametric method for fitting smooth curves to data by applying multiple localized regressions. It is useful for uncovering non-linear relationships when the data’s exact form is unknown. Proper use of local regression can reveal trends in noisy data, but poor implementation might lead to misleading results.

Image: https://en.wikipedia.org/wiki/Local_regression#/media/File:Loess_curve.svg

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

See nested loops in action! I've shared some quasi real-world #RStats examples showing how to handle complex data structures effectively.

Explore the examples https://www.spsanderson.com/steveondata/posts/2025-03-10/

See nested loops in action! I've shared some quasi real-world #RStats examples showing how to handle complex data structures effectively.

Explore the examples https://www.spsanderson.com/steveondata/posts/2025-03-10/

Tired of NA values messing up your analysis?

Here are powerful R techniques to handle missing data like a pro:
tidyr::drop_na() for quick cleaning
dplyr::coalesce() for smart replacements
mice package for imputation
Custom functions for complex cases

Check out the full guide! #R #RStats #DataCleaning #Tidy #Stats #Blog #RProgramming #Data

https://www.spsanderson.com/steveondata/posts/2025-03-03/

Tired of NA values messing up your analysis?

Here are powerful R techniques to handle missing data like a pro:
tidyr::drop_na() for quick cleaning
dplyr::coalesce() for smart replacements
mice package for imputation
Custom functions for complex cases

Check out the full guide! #R #RStats #DataCleaning #Tidy #Stats #Blog #RProgramming #Data

https://www.spsanderson.com/steveondata/posts/2025-03-03/

Visualize genomic data with ease using gggenomes, an R package that extends ggplot2 to handle and display genomic information intuitively. Whether you’re comparing genomes, analyzing features, or showcasing synteny, gggenomes provides the tools you need to turn complex genomic data into clear, informative visualizations.

Visualization: https://github.com/thackl/gggenomes

Further details: https://statisticsglobe.com/online-course-data-visualization-ggplot2-r

Connecting R to PostgreSQL: A Guide to Database Interaction
Learn to connect R to your PostgreSQL database for powerful data analysis! This guide covers connections, error handling, data management, and best practices. #RPostgreSQL #DatabaseConnection #DataAnalysis #RProgramming #DB2 #SQL
https://tech-champion.com/database/db2luw/connecting-r-to-postgresql-a-guide-to-database-interaction/