[R-sig-eco] ONLINE COURSE – Tidyverse for Ecologists (TIDY01)

[Oliver Hooker]

ONLINE COURSE – Tidyverse for Ecologists (TIDY01)

https://www.prstats.org/course/tidyverse-for-ecologists-tidy01/

We only have 3 places left on next week's course, Tidyverse for Ecologists!

16th - 20th June

Please feel free to share!

COURSE OVERVIEW - 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:
 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.

Please email oliverhooker using prstatistics.com with any questions

Day 1: A Short Course in R Basics (9:30 - 17:30)
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.
 Section 1 (R Essentials): This section focuses on R syntax, variables,
data types, conditionals (`if`,
`else`, `elif`), loops (`for`, `while`), and writing reusable code using
functions.
 Section 2 (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.

Day 2: Fundamentals of Tidyverse I (9:30 - 17:30)
This day introduces participants to the foundational concepts of Tidyverse
packages and their
applications to data science projects.
 Section 3 (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.
 Section 4 (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.

Day 3: Fundamentals of Tidyverse II (9:30 - 17:30)
This day builds on the foundations established in Day 2 and dives deeper
into advanced data
manipulation and visualisation techniques.
 Section 5 (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.
 Section 6 (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.

Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 - 17:30)
This day introduces participants to machine learning concepts using core
libraries for statistical modelling
and deep learning.
 Section 7 (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.
 Section 8 (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.

Day 5: Data Science Workflow with Tidyverse (9:30 - 17:30)
On the final day, participants will apply all their newly acquired skills
to solve real-world problems
inspired by ecological datasets.
 Section 9 (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
 Section 10 (Hands-on project): Participants will work through a complete
data science workflow,
including data cleaning, transformation, visualisation, modelling, and
communication of results.

-- 
Oliver Hooker PhD.
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