Word on the street is, learning R is painful.
And the rumors aren’t entirely wrong.
Many ecology and biology students struggle to understand the scripts they find on the internet.
And thick, disorganized university classes can make everyone feel so lost.
Still, you need to learn FAST, because the leaders in our industry somehow expect everyone to already know R!
Well I’m here to change that.
The Basics of R (for ecologists) was created using key methods I learned along the way, to drastically speed up the learning curve, while helping students feel empowered.
For example, I found that only 56 functions accounted for at least 80% of the code writing in R.
So you’ll start by gaining a really clear understanding of these essentials, before moving on to more complex processes. You’ll even receive a PDF “cheat sheet” of all the key 56 functions to refer to in the future.
Plus, as an enrolled student, you’ll get physical flashcards (as a FREE bonus sent to you in the mail) to aid in your learning! Flash cards are a tried-and-true method which help “stimulate our memories and create lasting connections to the material.” (Central Penn) without needing to always be in front of a screen.
Basically, this is the course I wish I had when I started as a graduate student in ecology. I've carefully selected the key topics and functions that will help you master the basics and quickly get over the learning curve with confidence, even if you’re a complete beginner.
By the end of the course you will be able to confidently:
Install R and RStudio
Upload your data into R
Clean and prepare data for plotting and analysis
Explore your data by adding new variables, combining datasets and modifying existing variables
Plot your data using scatterplots, line graphs, and boxplots
Manage and organize entire projects with R and RStudio
Learn the best practices for writing clear and reproducible code in R
What this course does not cover:
To cover the basics of R in an effective way, I cannot cover everything. So this course does not cover:
- Data analysis or modeling
- GIS or spatial visualization
- Advanced topics in visualization such as using ggplot2
I believe that these topics are relatively easy to branch into once your foundation with the basics is in place and I plan to expand into these and other topics in future courses.
This course is a shortcut to learning R quickly but effectively. Though most content is designed and presented from my own perspective as an ecologist, all course content is also applicable to most related fields.
This course doesn’t include or require statistics. When I was in your shoes, I dove into both R and statistics, and because of the complexity and overwhelm of both, I know it set me back. Through this course, you’ll gain the ability to wrangle your data (i.e., organize it, transform it, group it, sort it, and plot it—which is often all you need!) and you can always refine those skills further. Modeling and statistically analyzing your data in R will come easy after completing this course.
Once you join the course, you can…
- Ask me and my graduate assistant questions (within the course, under each lesson) at any time.
- Access all course materials for life.
- Watch and rewatch all the material online, at your own pace
- Earn credits towards the Ecological Society of America’s Professional Ecologist Certification program
- Get an official certificate of completion
Plus, there’s no risk!
My ultimate goal is to help you feel confident with R, so it is really important to me that this course is a good fit for you. If you’re unsatisfied with your purchase, contact me in the first 30 days and I’ll give you a full refund.
Join over 1000 other students...
“There would have been a lot of tears and stress if I didn’t have your course!”
"I just wanted to send a quick email to say THANK YOU SO MUCH for what feels like saving my life with your R teaching!!! I am very grateful you decided to put together all of this content in the way that you have because it is SO useful! You’re a fantastic teacher and having your R lessons specific to ecological questions and typical datasets has been more useful than any of the books I’ve tried to get through. I recently needed to come up with some data exploration graphics FAST while I was away working in the field and there would have been a lot of tears and stress if I didn’t have your Intro to Data Viz course.
Thank you, thank you, times a gazillion! Your work is going to help me do science so much more efficiently instead of spending needless hours bashing my head against a computer!"
— Rachel S-L
Graduate Student in the Centre for Wildlife Ecology at Simon Fraser University and Research Associate at Pacific WildLife Foundation
“The way of teaching is very easy to follow and very relaxed. All the basics are thoroughly covered, which is essential to learn the more complex stuff.”
“I liked how the progression of lessons prepared me for the material in the lessons that followed. I really like Luka's teaching style. He explains things very clearly and in a way that's understandable to a beginner. He is good at explaining the concepts as well as the coding specifics, which I feel taught me how to "think" in R, giving me the foundation to work in R on my own.”
“It's very practical and hands on. I believe that learning is best done by practicing and this course has provided an environment to both, learn and apply.”
Dr. Nishanta Rajakaruna
Professor in Plant Biology,
CalPoly State University
Luka is perfect for teaching a course on R for ecologists—he has a strong theoretical background in ecology, numerous experiences in field- and greenhouse-based studies, a knack for data analyses, and a life-long passion to teach.
Dylan Stephens, ecologist and biostatistician
"Luka has been a great help to a previous project of mine in the past and was extremely patient with me as an undergraduate student who hadn't worked in ecology for very long. His extensive knowledge in R and calm demeanor makes him a great teacher for this course!"
I’m passionate about making data proficiency and good science available to as many people as possible. I intentionally charge far less than most online courses in data science, and payment plans make the course even more approachable.
But if you’re experiencing financial hardship and from a country with a low median income (less than $500 per month), and still can’t afford the course, then pre-register for the course to learn how you can apply for our scholarship program.