R

Why Code?
In this talk, I summarise the benefits of using code to process and analyse data, give some practical tips for developing your skills, demonstrate how data simulation can improve your research, and discuss the benefits of code review.
2022-09-28 14:00 — 15:00 Lancaster University
Slides
Why Code?
Code Check
This project aims to make code review easier and more common in research. The first project is to create a guide for research code review aimed at novice to intermediate coders.
GitHub
Code Check
Loops
library(tidyverse) library(tictoc) This tutorial will introduce a few ways you can iterate your code. We’ll use the {tictoc} package to time each method to show how they differ. Task specifics Let’s say you want to do a power analysis by simulation.
2022-06-10 9 min read rstats
Applied Data Skills
This book provides an overview of the basic skills needed to turn raw data into informative summaries and visualisations presented in professional reports and presentations.
Cite Project Book Code DOI
Applied Data Skills
Data Simulation Workshops
This workshop will cover power analyses across a wide range of statistical tests, including linear mixed models.
2022-04-27 09:00 — 15:00 York University
Data Simulation Workshops
30 Day Chart Challenge
30-Day Chart Challenge 2022
Book Code
30 Day Chart Challenge
Data from Images
I saw this a few days ago and sympathised, because I’ve often wanted to use data that is trapped in images or PDFs. If it’s not too much, I’ll manually transcribe it, but that’s so tedious!
Last updated on 2021-10-17 8 min read rstats
Shiny
Shiny lets you make web applications that do anything you can code in R. For example, you can share your data analysis in a dynamic way with people who don’t use R, collect and visualize data, or even make data aRt.
Book Demo Apps
Shiny
Mixed effects models for designs with randomly sampled stimuli
How and why to use linear mixed effects models.
2021-10-05 16:00 — 18:00 Princeton University
Slides Code Paper
Mixed effects models for designs with randomly sampled stimuli
Faux
R functions for simulating factorial datasets.
CRAN Tutorials DOI
Faux