Hello! My name is Susan and I make clear, to-the-point coding videos and guides.

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Guides

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Laravel PHP VSCode Git Apache Nginx MySQL MariaDB Database YouTube API Herd Vue.js Command Line Composer SSH Server

Data Science

R RStudio jsPsych PsychoPy Python Anaconda

Software

DaVinci Resolve Photoshop Screenflow
Goodbye RStudio, Hello Positron - Positron is Posit’s next-generation data science IDE, combining the familiar RStudio workflow with the modern, extensible foundation of VS Code. Built for both R and Python, it offers a streamlined interface and deep AI integration designed to support contemporary data science workflows.
R Error in setwd() - cannot change working directory - Troubeshoot the R error 'cannot change working directory' when using R’s setwd command.
R Pipe Operator for Easier to Understand Code - Introduces the pipe operator in R and shows how it helps make data-analysis code easier to read and reason about.
R Vector Operations and Loops - Introduces the concept of vector operations in R and contrasts them with traditional for loops.
R Data Types & Structures - Learn about data types and data structures in R, with clear examples of each.
Custom Functions in R - How to create and use custom functions in R.
Z Scores — What They Are and How to Calculate Them in R - Learn what a z score is, why it’s useful, and how to calculate it in R.
Git Version Control in RStudio (Github, Mac) - Setting up Git version control in RStudio, connecting with GitHub, and managing R projects. Covers git setup, RStudio configuration, SSH keys with Github.com, repository setup, and basic workflow.
R Replace NA values with 0 - Quick tip on replacing NA values with 0 in R using is.na and subsetting.
R Error - Could not find function - Explains what the “could not find function” error in R means and how to fix it.
R for Data Science - Data Transformation (4) - Learn about Data Transformation in R via the book R for Data Science.
R for Data Science - Workflow Basics (3) - Learn the fundamentals of R programming via the book R for Data Science. Covers basic syntax including math calculations, creating objects, writing comments, working with functions, and best practices.
R for Data Science - Data Visualization (2) - Learn the basics of creating data visualizations with ggplot2.
R for Data Science - Introduction (1) - This introduction sets the stage for the goals of R for Data Science: teaching you how to import, tidy, transform, visualize, model, and communicate data.
Exploring and Visualizing Data (R Simplified) - Overview of exploratory data analysis in R including generating visualizations, addressing outliers and looking for correlations.
Reshaping Data Wide / Long Format (R Simplified) - Explanation of wide and long format data and how to convert between the two in R.
RStudio - Clear individual lines in the console - How to clear individual lines of output from the RStudio console. Alternative to the broom icon which will clear the entire console.
Import and Clean Data (R Simplified) - Steps to starting a new RStudio project, importing your data, studying its contents and structure, then cleaning it up and preparing it for analysis.
R / RStudio Sessions - Best Practices for Reproducibility (R Simplified) - Guide to three key components of a R session: the working directory, workspace, and loaded packages. Understanding and managing these elements properly enhances reproducibility, efficiency, and organization.
Why You Should Be Using RStudio Projects - RStudio Projects allow you to keep files and scripts organized, avoid working directory confusion, and make work reproducible and portable.
R Quick Start - This quick start guide will get you up and running with R and RStudio fast.
Run ChatGPT in RStudio with chattr - How to integrate a ChatGPT window directly into RStudio using the R package chattr.
Completely delete RStudio from Mac - How to completely delete RStudio as well as any of its settings and configurations from a Mac computer.
Simple setup for R in VSCode instead of RStudio (Mac) - How to configure VSCode as a development environment for R, integrating features such as code completion, linting, and syntax highlighting. Offers a more versatile and performance-focussed alternative to R Studio.