Tutorials

Here you can find our collection of coding, data science and statistics tutorials with examples in R, Python, JavaScript and Python. As you click through, you'll notice that some tutorials have ribbons on their logos - they are part of our free and self-paced online course Data Science for Ecologists and Environmental Scientists! Yellow for the Stats from Scratch stream, blue for Wiz of Data Viz and purple for Mastering Modelling. Learn more about the course and how to sign up here!

We regularly post tutorials, which you can complete in-person at one of our workshops in Edinburgh or online in your own time. If you are keen to write a tutorial and have it published on the Coding Club website, check out this page for more information.

R basics

Introduction to R: Part 1

Getting started with R and R studio

Introduction to R: Part 2

Troubleshooting and how to find help

Functions and loops in R

Save yourself lots of copying and pasting

Data manipulation

Basic data manipulation

Subset, modify and shape your data

Efficient data manipulation

Use pipes to streamline your code

Advanced data manipulation

Creative use of diverse dplyr functions

Web scraping

Retrieving useful information from web pages

Data visualisation

Data visualisation: Part 1

Making beautiful and informative graphs

Data visualisation: Part 2

Customising your figures

Qualitative data

Survey data, Likert scales, and text mining

Shiny

Making interactive graphs and web apps

Data synthesis

Efficient and beautiful data synthesis

Taking your tidyverse skills to the next level

Working efficiently with large datasets

Quantifying population change and visualising species occurrence

Visualisation workflows in the tidyverse

Working with many models & functional programming

Modelling

From distributions to linear models

Getting comfortable with the basics of statistical modelling

Transforming and scaling data

Understand the fundamental concepts of manipulating data distributions for modelling and visualization

ANOVA from A to (XY)Z

Tidying data, running a one-way ANOVA and making a beautiful data visualisation

Introduction to model design

How to turn a research question into a model

Linear mixed models

Getting to grips with fixed and random effects

Bayesian modelling using the brms package

From research question to final report, unleashing the full potential of brms

Meta-analysis & Bayesian statistics

Using the MCMCglmm package

Introduction to Stan

Getting started with Bayesian modelling in Stan

Generalised linear models in Stan

Using the rstanarm and brms packages

Hierarchical modelling of spatial data

Spatial modelling using R-INLA

Modelling using INLA

Accounting for spatial autocorrelation

Quantifying and visualising population trends

Making maps of populations and population change

Introduction to ordination

Finding patterns in your data

Time series data

Forecasting and decomposing temporal trends

Machine learning in R

Using the K-nn algorithm

Data clustering

Finding patterns in your data

Spatial data

Analysis with rasters and remote-sensing data

Using multispectral data, raster stacks, and k-means clustering

Geospatial vector data in R with sf

Creating static and interactive maps using osmdata, sf, ggplot2 and tmap

Species occurrence and density maps

Using GBIF and Flickr data to visualise species occurrence

Manipulation and visualisation of occurrence data

Cleaning occurrence data and customising graphs and maps

Basic maps and spatial analysis

Using R as a GIS software tool to create informative maps

Reproducible research

R Markdown

Using R Markdown to save and share code

Write your dissertation in Rmarkdown

Using Rmarkdown to create complex pdf documents

Introduction to Github for version control

Keeping track of your code and its many versions

Setting up a GitHub repository for your lab

Version control & code management with GitHub

Transferring quantitative skills among scientists

Turning your code into online tutorials

Following a coding etiquette

Make your code portable and widely readable

Creating an R package

Collect functions into a portable reproducible package

Google Earth Engine

Analysing remote-sensing data using the Google Earth Engine

Quantifying forest cover change in protected areas

Python

Python crash course

How to get started with Python

Working with data in Python

Reading and plotting scientific data

Numbers in Python with NumPy

Working with array of numbers

Python for data analysis and visualisation

Using Pandas and Matplotlib

Introduction to Iris

Analysing Earth science and climate data with Python's Iris toolkit

Time series analysis in Python

Manipulating and plotting time series data using pandas

Topic modelling in Python

Unsupervised machine learning to find Tweet topics

Fortran

Introduction to Fortran

Writing a simple Fortran program

COMING SOON:

Intro to mapping (choropleth, hex, point, bubble maps)
Transforming and scaling data
Bootstrapping