I did another post on use with() and within() from Base R, simply because it bears repeating. I don't think it gets enough eyeballs.
Post: https://www.spsanderson.com/steveondata/posts/2025-07-14/index.html
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Ever wonder how the #tidyverse came to be? 
#TheTestSet 's first episode features @hadleywickham on his accidental empire of #RStats packages, bear encounters, and more!
Stream "Spreadsheets, bikes, and the accidental empire of R packages" at:
• https://thetestset.co
• Spotify: https://open.spotify.com/episode/7Cta4KzehwPpM3P6fHaqfQ?si=eJXDgBaeT9WS5BeTiVOh-w
• Apple Podcasts: https://podcasts.apple.com/us/podcast/spreadsheets-bikes-and-the-accidental-empire-of/id1823736938?i=1000715173867
Today was another post on using xtabs() in R
#R #RStats #Blog #Code #RProgramming
Post: www.spsanderson.com/steveondata/...
How to Use xtabs() in R to Cal...
Steve's Data Tips and TricksHow to Use xtabs() in R to Calculate Frequencies: A Comprehensive Guide – Steve’s Data Tips and TricksDiscover easy-to-follow tutorials that simplify complex topics and enhance your skills. Start learning today!
Today was another post on using xtabs() in R
#R #RStats #Blog #Code #RProgramming
Post: https://www.spsanderson.com/steveondata/posts/2025-06-30/
Steve's Data Tips and TricksHow to Use xtabs() in R to Calculate Frequencies: A Comprehensive Guide – Steve’s Data Tips and TricksDiscover easy-to-follow tutorials that simplify complex topics and enhance your skills. Start learning today!
The R Consortium’s Infrastructure Steering Committee (ISC) is proud to announce the first round of 2025 grant recipients.
Find out about the seven new projects receiving support to enhance and expand the capabilities of the R ecosystem. The projects range from economic policy tools and ecological data pipelines to foundational software engineering improvements.
https://r-consortium.org/posts/r-consortium-awards-first-round-of-2025-isc-grants/
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 · What'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.
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.
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.
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/
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
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/
![# Inefficient approach (without pre-allocation)
result_inefficient <- c()
for (i in 1:1000) {
result_inefficient <- c(result_inefficient, i^2)
}
# Efficient approach (with pre-allocation)
result_efficient <- numeric(1000)
for (i in 1:1000) {
result_efficient[i] <- i^2
}](https://cdn.masto.host/rstatsme/media_attachments/files/114/138/215/551/324/884/original/f44539a8cf5f811c.png "# Inefficient approach (without pre-allocation)
result_inefficient <- c()
for (i in 1:1000) {
result_inefficient <- c(result_inefficient, i^2)
}
# Efficient approach (with pre-allocation)
result_efficient <- numeric(1000)
for (i in 1:1000) {
result_efficient[i] <- i^2
}")
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/
![# Inefficient approach (without pre-allocation)
result_inefficient <- c()
for (i in 1:1000) {
result_inefficient <- c(result_inefficient, i^2)
}
# Efficient approach (with pre-allocation)
result_efficient <- numeric(1000)
for (i in 1:1000) {
result_efficient[i] <- i^2
}](https://media.mstdn.social/media_attachments/files/114/138/215/299/502/980/original/157236e6f1ecf396.png "# Inefficient approach (without pre-allocation)
result_inefficient <- c()
for (i in 1:1000) {
result_inefficient <- c(result_inefficient, i^2)
}
# Efficient approach (with pre-allocation)
result_efficient <- numeric(1000)
for (i in 1:1000) {
result_efficient[i] <- i^2
}")
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
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
Check out our most recent blog on the {sparkline} package in R from our Data Scientist Osheen Macoscar!
Discover how to create compact, informative inline charts like line graphs, bar charts, and box plots to enhance your data visualisations.
#RStats #DataVisualisation #DataScience #Shiny #RProgramming
www.jumpingrivers.com · Sparkline Package for Inline VisualisationsThis is part one of a multiple part blog series, in which I will be demonstrating how to create inline visualisations using the {sparkline} R package.
Struggling with duplicate rows in R? Our new guide has got you covered!
Learn effective techniques to clean your data and enhance your analysis. Dive in now at https://www.spsanderson.com/steveondata/posts/2025-01-30/
Struggling with duplicate rows in R? Our new guide has got you covered!
Learn effective techniques to clean your data and enhance your analysis. Dive in now at https://www.spsanderson.com/steveondata/posts/2025-01-30/
Summer School: Data Literacy with R for Humanities Students
4-15 August 2025
Charles University, Prague, Czechia
Learn R programming, data tidying, and visualization in a supportive environment tailored for humanities students.
Free of charge! Includes travel reimbursement, accommodation, and per diems.
Application deadline: 28 February 2025
Bring your laptop & headphones!
#DataLiteracy #RProgramming #DigitalHumanities #SummerSchool #DataScience #RStats
https://ufal.mff.cuni.cz/events/summer-school-data-literacy-r-students-humanities
ufal.mff.cuni.czSummer school: Data Literacy with R for Students of Humanities | ÚFAL