I am familiar with the ggplot2, dplyr, tidyr package for the data analysis with R software.I conduct all my research analysis in R, although I rarely manage my GitHub (something I plan to improve!). However, I do have two open-source repositories available on GitHub: 30 Days Map Challenge and R_MyFirstLesson. Feel free to check them out if you're interested. Also, if anyone is interested in collaborating on open-source code projects for teaching purposes, please reach out—I’d love to get involved!
This challenge was established by Topi Tjukanov in 2019. The aim of this challenge is somewhat whimsical; it’s just a gathering of enthusiastic people using their tools to create various maps. Each day in November, participants produce a map that can be simple or informative, based on a different topic. 2023 was my first year to attend the challenge. However, in 2024, I will select a few topic to present:
Want to be a challenge competitor? Please refer to 30DayMapChallenge offical website: https://30daymapchallenge.com/
Or you just want to know what i did last year in 2023, here is my source code and my map on github: https://github.com/jjakon11/30DayMapChallenge
This is an unofficial university course at National Chung-Hsing University—a one-hour lab session that introduced the basics of R programming in 2023. The datasets used are primarily open data, and most students have a background in ecology. This lab was designed to make climate, spatial, and tree data accessible and engaging.
The repository name emphasizes that this was my first R class of the semester, as well as my first time teaching R at the university level. My goal is to show that R is not difficult to learn and to inspire students. I hope that each student will feel equipped to continue learning R independently with open-source resources
[2] Solutions Manual (and Beyond) for ggplot2: Elegant Graphics for Data Analysis: https: //aditya-dahiya.github.io/ggplot2book3e/
[1] Tidy Modeling with R: https://www.tmwr.org/