With the recent mass exodus from Xitter, Bluesky has emerged as a new home for the R community. Discover how to join the community with starter packs and automate posting to Bluesky from R using GitHub Actions. Explore detailed steps for setup and automation, making it easy to share your latest blog posts on social media platforms.

Learn how to use apply-functions in R to read multiple files efficiently. This guide covers the basics of using sapply and lapply to iterate over files quickly, eliminating the need for loops.

Optimize your blog’s SEO and Zenodo metadata with automated summaries using Hugging Face’s text summarization models. Learn how to connect to the Hugging Face API, prepare content, and integrate summaries into your markdown files using R and the httr2 package.

Making your blog FAIR (Findable, Accessible, Interoperable, and Reusable) by archiving posts on Zenodo and adding DOIs. Learn how to use Hugo, GitHub actions, and the Zenodo API to ensure your content is preserved. Enhance your blog’s visibility and reliability.

Positron, an IDE developed for data science, combines the best features of RStudio and Visual Studio Code. Learn about its features, customization options, and extensions. Discover how Positron compares to other popular IDEs and why it’s a promising tool for data scientists.

Improve your GitHub profile with these tips. Learn how to create a repository with your username, add stats, coding skills, preferred IDEs, blog stats, and more. Customize your profile to make it engaging and showcase your skills and interests effectively.

Customize your R startup experience by modifying the R startup message, adding useful system information, and fun elements like a praise or colour bar. Learn how to edit the .Rprofile file, clear the console, print system details, and add a personalised message using the {praise} and {cli} packages for an enjoyable R session.

Explore my programming IDE journey, from MATLAB and gedit to RStudio and VSCode. Learn about the strengths of each IDE for different languages and tasks, and why I switch between RStudio and VSCode depending on the project. Insights on optimizing your coding environment for efficiency and productivity.

Learn how to handle git mistakes and recover lost work with simple solutions. Explore commands like git clean and git reset, and discover how to undo mistakes using ctrl + z in RStudio. Gain confidence in your git journey with tips from experienced developers and helpful resources.

Learn to create model matrices for Freesurfer Linear Mixed Models (LMM) in R using built-in functions. This guide walks you through manually creating model matrices, utilizing the model.matrix function, and standardizing variables. Includes creating a function to streamline the process, making neuroimaging data analysis more efficient.

Teaching is rewarding, especially in coding workshops. Reflecting on experiences from Digital Scholarship Days at the University of Oslo, I learned valuable lessons from learners about Quarto slides, project management, and teaching challenges. Continuous improvement in teaching methods benefits both instructors and learners.

Reflecting on the past years, I’ve focused on significant career milestones, volunteer work, and a new website revamp. Highlights include leading a new Core Facility at the University of Oslo, attending Posit::conf 2023, contributing to R-Ladies, rOpenSci, and R-Hub communities. My new Hugo Igloo theme offers a fresh look for my website.

Revamping a website and organizing content can be challenging. Here’s how I restructured my blog posts into yearly subfolders using a Bash script, added Hugo aliases for seamless redirects, and simplified file slugs for cleaner URLs. This automation ensures better content organization and link integrity.

Attending Posit::conf (formerly RStudio::conf) in Chicago was an exhilarating experience filled with learning, speaking engagements, insightful workshops, and connecting with the R and Data Science communities. Highlights included workshops on R fundamentals and package development, exciting updates on Quarto, and invaluable networking opportunities.

Explore 15 essential pieces of R code to streamline your data analysis and visualization workflow. From creating directories and handling files to customizing ggplot and working with logit scales, these snippets can help you enhance your programming efficiency and maintain cleaner scripts.

Learn to replace loops with vectorized apply functions in R for efficient data manipulation. This guide covers converting loops to functions using sapply and mapply, enhancing your programming skills by making code more readable and less error-prone.

Generate automated reports in Rmarkdown by creating reusable child templates. Learn to call child documents from a main Rmd using knitr and environment variables, enabling efficient and flexible report generation for survey data analysis. Follow this guide to streamline your workflow and reduce repetitive code.

Switch from Disqus to Giscus for comments on your Hugo site using blogdown. This guide shows how to integrate Giscus, a GitHub discussions-based comment section, into your blog seamlessly. Learn how to set up Giscus, modify your theme, and customize the comment section for a streamlined experience.

Learn to build Hugo websites using GitHub Actions and deploy them with Netlify. This guide covers setting up GitHub Actions to build your site, install dependencies, render Rmarkdown files, and deploy to Netlify or GitHub Pages. Enhanced control and maintenance for your site, with step-by-step instructions.

We released a new version of the ggseg R-package for visualizing brain atlases. It features a geom_brain function and position_brain for flexible brain slice positioning in ggplot2. This update enhances atlas plotting, offering more customization and improved visuals.

Here’s a ggplot2 holiday card wishing you a better 2021! Explore how to create a festive holiday plot in R with ggplot2. Understanding the preparation of data for plotting, using geom_circle, adding blur effects, and customizing text. This exercise enhances your skills in working with ggplot2 and thinking critically about data structures and visual outputs.

This year, I started a Twitter advent calendar showcasing one R-package per day, each of which I find essential for my work. The aim is to highlight useful packages, both familiar and lesser-known, and offer insights into their functionalities. Follow along to discover packages from CRAN, BioConductor, GitHub, and more, and perhaps find new tools to enhance your work.

Linear regression is essential in psychology, often disguised as t-tests or ANOVAs. This guide explains running linear models in R, comparing t-tests, Pearson correlations, ANOVAs, ANCOVAs, and more. Understand how to specify models, run tests, and interpret them with real data examples. Gain confidence in translating your hypotheses into linear models and applying statistical methods effectively.

Updating my hugo setup on netlify took over a year of delays due to complexities. After extensive work, I transitioned from knitting .html to .md, fixed theme issues, and resolved netlify CSS problems. Follow my step-by-step guide to smoothly update your blogdown website. Learn to make use of hugo’s page bundles, adjust configurations, and ensure seamless rendering both locally and on netlify. This guide addresses key changes you might face, from altering your workflow to understanding theme changes and troubleshooting rendering issues.

For neuroimaging, toggling between R and imaging software like FSL or Freesurfer is common. We segment large 3D brain data into meaningful regions. Our ggseg and ggseg3d packages visualize the brain’s Desikan-Killany atlas in 2D and 3D. Learn the steps to convert Freesurfer annotation files to mesh files using the ggsegExtra package for enhanced brain data visualization.

Celebrate with another Christmas special! Following last year’s festive ggplot tree, build an interactive snowman using R, ggplot2, ggforce, and gganimate. Learn to create a snowman from circles, add features like a face, arms, and accessories, and animate falling snow. Enhance your R skills and holiday spirit with this detailed tutorial, ending with a personalized “Merry Christmas!” message.

Discover how to create interactive 3D brain atlas plots using the ggseg3d package in R. Learn to visualize neuroimaging data with plotly, including setting up and customizing brain atlas plots. Ideal for neuroscientists and data scientists working with brain segmentation data.

Discover how to visualize Generalized Additive Mixed Models (GAMMs) with interactions in R using ggplot2 and mgcv. This tutorial covers numeric and factor interactions, creating prediction datasets, and generating smooth plots. Ideal for enhancing your data analysis and visualization skills.

Improve your data visualization skills in R with this comprehensive guide on creating plots using ggplot2. Learn to wrangle data, create long-format datasets, and produce various types of plots, including histograms, scatter plots, and regression lines. Ideal for psychologists transitioning from SPSS to R.

Discover the key steps to building R packages effectively with insights from the “Building tidy tools” workshop at RStudio::conf. Learn about package setup, unit testing, documentation, naming conventions, and using essential tools like usethis, testthat, and pkgdown. Create your first package with practical examples and improve your package development workflow.

Reflecting on the unique experience of RStudio::conf, this blog post highlights the community-driven atmosphere, inclusivity, diverse attendance, and the genuine support for speakers. Discover why RStudio::conf stands out among other conferences and how it fosters an inspiring environment for R enthusiasts.

Celebrate Christmas with a fun and festive Christmas tree plot in ggplot2! Follow this tutorial to create your own animated Christmas tree in R, complete with baubles, a tree topper, and falling snow using gganimate. Learn how to use ggplot2 and gganimate for creative data visualizations.

Learn the basics of using R and RStudio for psychologists. This tutorial covers reading SPSS data, recoding variables, using pipes, creating simple plots, transforming data to a tall format, and running linear models. Discover how to transition from SPSS to R and take advantage of its powerful data analysis capabilities.

Explore how to create animated visualizations of brain data using the ggseg and gganimate packages in R. Learn to prepare data for animations, customize plots, and generate smooth transitions to visualize changes across different conditions such as age.

Discover ggseg, an R package designed for plotting brain segmentations directly in R with ggplot2. Learn how to visualize results from different brain atlases, customize plots, and integrate your own data. Ideal for neuroimaging researchers needing to plot segmented brain data efficiently.

Last post was definately my most succesful (and I have so much experience :P). But it was fun with all the feedback and appreciation. So I’ll continue with a little more plotting. Circular plots are becoming more and more popular in for visualising associatetions between different phenomena. I think they look pretty cool, and while I have not yet published a paper with it, I did prep a lot of scripts for plotting my analysis results with it, with BRAINS! because, brains make it better. Always.

Learn how to add external images to your ggplot visualizations in R. This tutorial covers steps to read images and plot them alongside your data, useful for enhancing interpretation and presentation in fields like neuroimaging. Discover how to use functions like readPNG, rasterGrob, and annotation_custom for integrating images with your plots.

Explore the use of Generalized Additive Mixed Models (GAMMs) with random effects using mgcv in R. This blog post covers the differences between specifying random effects in mgcv compared to other packages like lme4 and walks through a detailed example with simulated data. Learn about gamm model output, interpreting random effects, and plot comparisons of different model specifications.

Discover how to plot Generalized Additive Mixed Models (GAMMs) using ggplot2 in R. This tutorial covers generating simulated data, creating GAMM models with mgcv, predicting single predictor effects, and using the itsadug package for smooth predictions. Learn the steps to visualize complex models and create informative plots.