Tidyverse for Ecologists
Master the Tidyverse and Tidymodels in R through hands-on ecological data science projects in this 40-hour live online course.
Duration: 40 Hours
Format: Recorded ‘on-demand’ Format
£450Registration Fee
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5.0
from 200+ reviews
- Overview
- Instructors
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Course Description
This course comprehensively introduces the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming, core Tidyverse packages, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology.
What You’ll Learn
During the course will cover the following:
- Understand the fundamentals of R programming for data analysis.
- Be proficient in using core Tidyverse packages to clean, transform, and visualise data.
- Gain an introduction to basic machine learning concepts through the Tidymodels framework.
- Learn to preprocess, build, evaluate, and interpret models using Tidymodels.
- Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects.
Course Format
Flexible Learning Structure
Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.
Access Anytime, Anywhere
All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.
Independent Exploration with Support
Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.
Comprehensive Learning Resources
Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.
Work With Your Own Data, On Your Terms
Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.
Continued Guidance and Resource Access
Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.
Who Should Attend / Intended Audiences
This course is intended for academics and postgraduate students working on data science–related projects, as well as data scientists and applied researchers in the public or private sectors who wish to integrate advanced R programming into their workflows. It will also benefit professionals seeking to make greater use of tidyverse packages, and ecologists who want to apply advanced R programming principles in their research. No prior quantitative knowledge is required for this module. The first day will introduce the basics of R, though some familiarity with any other programming language will be advantageous.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary R packages will be introduced and installed during the workshop.
Dr. Gabriel Palmer
Session 1 – 04:00:00 – R essentials: This section focuses on R syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
Session 2 – 04:00:00 – Data Structures and File Handling in R: This section emphasises understanding data structures (e.g., vectors, data frames, lists) and handling files by reading/writing data (e.g., CSVs) for manipulation and analysis.
Session 3 – 04:00:00 – Data Manipulation I: This section covers the basics of data manipulation using `dplyr` functions such as `filter()`, `select()`, `mutate()`, `arrange()`, and `summarise ()`. Participants will learn how to clean, transform, and prepare datasets for analysis.
Session 4 – 04:00:00 – Data Visualisation I: This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots, bar charts, and line graphs while exploring the grammar of graphics.
Session 5 – 04:00:00 – Data Manipulation II: This section extends the use of `dplyr` by introducing more complex operations such as joins, grouping with `group_by()`, and working with pipelines using `%>%`. Finally, additional packages will be presented to enhance data manipulation programming.
Session 6 – 04:00:00 – Data Visualisation II: Participants will explore advanced visualisation techniques using extensions of `ggplot2`, such as creating animated plots with the `gganimate` package and interactive visualisations with additional tools.
Session 7 – 04:00:00 – Introduction to regression: This section focuses on regression modelling using Tidymodels. Participants will learn to implement linear regression models, evaluate model performance, and interpret results.
Session 8 – 04:00:00 – Introduction to Classification: This section introduces techniques such as support vector machines and neural networks using Tidymodels. Participants will also explore methods for assessing the performance of classification models.
Session 9 – 04:00:00 – The data science workflow: The workflow will be illustrated based on the core packages introduced. The book “R for Data Science” will serve as a base literature for this day
Session 10 – 04:00:00 – Hands-on project: Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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