ONLINE COURSE – Tidyverse for Ecologists (TIDY01) This course will be delivered live
16 June 2025 - 20 June 2025
£480.00
Event Date
Monday, June 16th, 2025
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland (GMT) local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
Course Details
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.
- By the end of the course, participants should be able to:
- 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.
Intended Audiences
- Academics and post-graduate students working on data science-related projects.
- Data scientists and applied researchers in public or private sectors who need to integrate
advanced R programming language into their project workflows. - Professionals looking to integrate tidyverse packages into their workflows or enhance their
understanding of R programming language. - Ecologists looking to understand the basic principles of advanced R programming language and implement them in their research.
Venue
Delivered remotely
Course Information
Time zone – Ireland (GMT) local time
Availability – 25
Duration – 5 days, 8 hours per day
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session.
Assumed quantitative knowledge
No quantitative knowledge is required for this module.
Assumed computer background
Day one will cover the basics of R for the module, however, some familiarity with any other programming language is welcome.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Monday 16th
Day 1 – Classes from 09:30 – 17:30
A Short Course in R Basics
This day provides participants with the foundational R skills required for working with Tidyverse and
Tidymodels. It is designed for beginners or those needing a refresher in R programming.
- R essentials: This section focuses on R syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
- 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.
Tuesday 17th
Day 2 – Classes from 09:30 – 17:30
Fundamentals of Tidyverse I
This day introduces participants to the foundational concepts of Tidyverse packages and their
applications to data science projects.
- 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.
- 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.
Wednesday 18th
Day 3 – Classes from 09:30 – 17:30
Fundamentals of Tidyverse II
This day builds on the foundations established in Day 2 and dives deeper into advanced data
manipulation and visualisation techniques.
- 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. - 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.
Thursday 19th
Day 4 – Classes from 09:30 – 17:30
Applying Tidyverse Fundamentals to Data Modelling
This day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning.
- 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. - 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.
Friday 20th
Day 5 – Classes from 09:30 – 17:30
Data Science Workflow with Tidyverse
On the final day, participants will apply all their newly acquired skills to solve real-world problems
inspired by ecological datasets.
- 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 - Hands-on project: Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results.
Course Instructor
Dr. Gabriel Palma
Gabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University, Ireland, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling, machine vision, machine learning, and applications to ecology and entomology. His personal webpage can be found here