It's easier than ever to write and distribute an R package to implement your new method. But it's much harder to make sure your new R package is bug-free, easy to use, and easy for you to maintain and extend. This workshop will teach you some tools and strategies for making your R package the best it can be.

We'll cover how to:

• Design and implement a simple and intuitive user interface
• Plan the internal architecture of your package code so it's easy to extend and maintain
• Create high-quality documentation, including vignettes, a website and forum
• Write good unit and integration tests to find and fix bugs
• Set up automated package testing, code style-checking, and documentation spell-checking

This will be a hands-on workshop. You'll gain experience reviewing other people's packages and working together to design new package interfaces. You're welcome to bring along your own package, work on it during the workshop, and get feedback on how to improve it.

Required background:

• Experience writing functions in R
• Interest in creating R packages
• Opinions about what you like/dislike about the R packages you use

Nick and Saras are ecologists, research software engineers, and R obsessives. They have written over 20 R packages (some wonderful, some dreadful) and reviewed more than 30 more for journals like Methods in Ecology and Evolution, and Journal of Open Source Software.

Multivariate analysis of abundance or presence/absence data in ecology is a challenging problem, for which analysis techniques have been developing rapidly in recent years. Historically these sorts of data were analysed using algorithms based on pairwise dissimilarity metrics, but a modern approach involves specifying a joint statistical model for the data, sometimes called a joint species distribution model. This approach has a number of advantages, including in statistical properties, interpretability, and functionality. This short course will give an introduction to a range of tools that have recently been developed for multivariate data in ecology, including methods for hypothesis testing, ordination, trait modelling, prediction, classification, and studying causes of co-occurrence. Packages discussed include mvabund, HMSC, gllvm, SpeciesMix and ecoCopula.

The presenters are developers of new statistical approaches to multivariate analysis in ecology, whose multivariate software is having increasing impact in ecology.

In this course we will learn how to develop spatial geostatistical models using the R-INLA package to predict species distribution, estimate disease risk, and quantify risk factors. We will also learn how to create data visualizations such as static and interactive maps, and introduce presentation options such as interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. We will work through several fully reproducible examples of ecology and disease mapping applications using real-world data such as sloths in Latin America and malaria in The Gambia. The examples will provide clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. We will cover the following topics:

• Manipulate and transform geostatistical and raster data using spatial packages
• Query and collect species occurrence data from several sources including the Global Biodiversity Information Facility (GBIF) and the Atlas of Living Australia (ALA) using the spocc package
• Retrieve high resolution spatially referenced environmental data using the raster package
• Model species distribution, disease risk, and risk factors in different settings
• Fit and interpret spatial models using Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches
• Create static and interactive visualizations using leaflet and ggplot2
• Communicate results with reproducible R Markdown reports, interactive dashboards and Shiny web applications

The course materials are based on the book 'Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny' by Paula Moraga (2019, Chapman & Hall/CRC Biostatistics Series).

Paula Moraga (@Paula_Moraga_) is a Lecturer in the Department of Mathematical Sciences at the University of Bath, UK. She develops innovative statistical methods and open-source software for disease surveillance including R packages for spatio-temporal modeling, detection of clusters, and travel-related spread of disease. Her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.