Reading in multiple files to append them is quite common in muy field. Let’s go through a couple ways you can do that in R.

When you have lots of old blog posts, writing good summaries for SEO can be very tedious. Using AI, we can get summaries for our posts easily. Using httr2 we can access models through the Hugging Face API.

In August, Heidi Seibold wrote in her Newsletter about how she made it FAIR. This made me think about my own writing and how to secure the content even when/if I port by blog or change its structure. Here, I show how I use R, GitHub Actions and Zenodo API to create dois for all my posts.

In May I wrote about my favorite IDE’s. As a result of that post, I was asked to join Posits private beta-testers for their new IDE, Positron. Now Positron is out of private beta, so let’s see if it can replace my current favorite IDE’s.

GitHub is a major developer platform, and your profile is the first thing people see when they visit your account. Your GitHub profile should then showcase who you are, what you know and what you’re learning. This is how I vamped up my GitHub profile, to showcase who I am, what I know and what I’m learning.

We all know the R startup message. It tells us the version of R we are running, the date, the platform we are running on, and some informative tips on using R. When you’ve been using R a while, though, it gets old. Let me show you how you can customise your startup message!

People who code have a tendency to spend a lot of time in various IDEs (Integrated Development Environments). They can be as simple as a text editor or as complex as a full-blown development environment. In this post, I’ll go through my two go-to IDE’s, RStudio and VScode, and why I switch between them rather than sticking to a single one.

Version control is great, but sometimes it can trip you up! In this post I recount my rather stupid mistake of using git clean when I shouldn’t have, and how I got out of it.

Running Freesurfer Linear Mixed Models on vertex-vise data is a popular way of capturing more advanced statistical models in neuroimaging. In this post I highlight how you can prepare your data in R for Freesurfer LMMs.

Teaching can be extremely rewarding, and teaching can be rewarding . Teaching you - teaching me is the beauty of teaching . I love teaching coding and programming, and helping scientists unlock a new way of working . My university hosts an annual event called [Digital Scholarship Days] This year, I taught two workshops at the Universtity of Oslo .

There is a general air of excitement, nerdiness and support I just rarely feel at any other conf I go to. Posit::conf is like it gathers all the best Data science and R geeks in one place and we just go bananas in each others company. I’d like to share some thoughts and joys I had in the 4 days of the conf .

Advent of R Functions is a series of 24 pieces of code I often use in my work . This advent brings you 24 code examples that I hope can be of interest and help to you . I’ll continue using the calendar as for this first part of the first day of the month .

When programming we want to write code that iterates well, we do this through loops . Let’s Get Apply’ing is based on the very well-known mtcars dataset built-in to R . We’ll only use two columns, just to keep it simple .

A new version of ggseg R-package for visualisations of brain atlases was released a couple of months ago . I wanted to create such an update for a long time, but getting the internal logics of a ggplot geom working as I wanted was rather difficult . New {ggseg} with Geom! The position is what I’m truly happy about!

New Year’s Wishes is a ggplot2 holiday card wishing you all a better 2021 than 2020! I find these holiday cards help me understand and explore how data need to look to create a certain output . I wanted to use ggforce::geom_circle for this, which would need x and y coordinates for the center of the circle and the radius .

The advent calendar is one R-package per day that I personally use and find very useful in my work . The hope is that it would give people a mix of familiar and less familiar packages that might help their work too . R-Package Advent Calendar 2020 is one package per day .

In this post we will go through examples of how we can specify different linear models (linear regressions) in R . The Linear Regression Family in RI will focus on how you can run models in R, and it will be up to you to try to translate your hypothesis into a model .

Blogdown is usually used with [go hugo], a static webpage builder . Blogdown renders directly from Rmd to html, meaning it circumvents hugo’s processing of md` . This can cause some unwanted issues depending on your theme . It can also make it difficult to maintain your webpage over time .

Do you wanna build a snowman? Do you want to build a Christmas tree? We’re going to use [ggforce] from the [gg force] package by Thomas Lin Pedersen . We need to specify the center of the circles we’re drawing’s locations and their radii . We use ‘geom_circle’ and ’tidyr’ to create the snowman .

ggseg3d is a package for 3d mesh plots of brain atlases using plotly . It is made to plot brain atlas data . It should be fairly easy to use or atlele to make the first plot . It can be installed from our works GitHub account using remotes .

Plotting GAMM interactions with ggplot2 is not always easy, especially if you have some interactions in there . Plotting the results of GAMMs are not always so easy . I’ll be using the diamonds dataset for this, as it has some nice non-linear features .

R for Psychologists - part III: Getting SPSS data into R, doing some data wrangling (altering the data), plotting, and running a simple linear model . Part II: Plotting in R through ggplot2 really is a way to understand how to organise and alter data in R to suit your needs .

Building tidy tools - a quick recap of RStudio::conf 2019 workshop & teeny demonstration . I attended a two-day workshop with Charlotte Wickham and Hadley Wickham . The sibling duo of R-package development did not fail to deliver what the workshop title announced, a workshop in how to create packages in a tidy way .

RStudio::conf Austin is the best conference experience I have had . It was thoroughly enjoyable and educational to see the different ways the same tools were applied to solve “real life-life problems” It was also enjoyable to see how they applied to problems in industry to solve real life problems . It is the empitamy of what this community actually is .

R requires you to be proficient in R in a different way than SPSS . Once you are proficient enough in R, you have a gazillion tools to do more or less anything you want to do, be it analysing complex data, a simple linear regression, writing a blog or making interactive data portals . RStudio is a completely blank slate, no data, no fancy stuff, just a program .

The ggseg package was so well received that we almost immediately got it on [Neuroconductor]. We got some pull requests from people helping us improve the code, and particularly get it ready for CRAN when we decide to submit it there . The package vignettes are update on how you may supply data for facetting .

The ggseg R-package for brain segmentations is intended for those who need to easily plot results or overviews of segmentet data . The package is developed as part of our lab [The Center for Lifespanism and Cognition [LCBC-UiO]

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 .

Adding external images to plots is a good way to showcase results, and make it easier for me and my reader to understand what the findings are . I use [ImageMagick] for this, but there are R-packages to help, like [imager] and [imaging] software .

I’m working a lot with Generalized Additive Mixed Mixed Models (GAMMs) I started seeing some differences in the output from other mixed models I had run through [lme4] and even Bayesian mixed models with [rstanarm] The random effects terms were specified differently . At first I thought this was mainly the fact that I was now using smoothing splines, which I was unfamiliar with running before .

A co-worker was working on some generalized additive mixed models (GAMM) through the R package [mgcv] The analyses work very well, and results were as expected . There were also some built in plotting functions for the gamm output in R . However, they were with base-r, and we all know base r is not the most beautiful .