R

When designing visualizations, choosing the right colors can greatly enhance readability, convey the correct message, and make your plots aesthetically pleasing. Here are some guidelines along with code examples to help you choose color palettes effectively.

A chill and practical session on how to tell better stories using data, especially for students in science and bioinformatics.

Welcome! This is my first R Shiny project—a collection of simple, interactive apps designed to support biologists working in biodiversity monitoring and species distribution mapping. These apps: Allow users to upload and explore biodiversity datasets Generate interactive maps using leaflet Provide an accessible starting point for visualizing species distribution patterns You can explore the code and try it yourself here:

This article will include code chunks on how I analyze bacterial growth of OD600 data

Hi guys, biocomp is a repository for my bioinformatics and computational biology learning journey. This repository includes scripts and data primarily focused on computational biology, covering a wide range of topics such as:

Welcome! This is a friendly and beginner-accessible open-source book created to help you explore the fascinating world of data visualization—one chart at a time. Through this project, I share: My personal contributions to the 30 Day Chart Challenge Step-by-step R tutorials Clean, reproducible code Practical tips for making compelling and effective charts Whether you’re just starting your data journey or looking to sharpen your data storytelling skills, this book is designed to guide you in an approachable, hands-on way.

A hands-on learning module on computational microbiology, focused on microbial taxonomy, classification, and phylogenetic analysis using R.