- Language: English

- Duration: 120mn

- N° Participants: 100

- Level: Beginner

"This tutorial will cover the introduction to ggplot2 and its main functions. We will cover how to make visualization of one variable, two variables, and three or more variables, how to lay out multiple plots, the use of ggstats for statistical visualizations, how to make interactive graphs using plotly and gganimate, some extensions of ggplot2. Finally, we will show you how to enhance the quality of your graphs by changing the theme or adding a logo and how to export your graph. We will share the code on the github repository of R-Ladies Tunis."

- Language: English

- Duration: 150 mn

- N° Participants: 60

- Level: Intermediate

Additive Bayesian Networks (ABN) have been developed to disentangle complex relationships of highly correlated datasets as frequently encountered in risk factor analysis studies. ABN is an efficient approach to sort out direct and indirect relationships among variables which is surprisingly common in systemic epidemiology. After the tutorial, you will run the particular steps within an ABN analysis with real-world data. You will be able to contrast this approach with standard regression (linear, logistic, Poisson regression, and multinomial models) used for classical risk factor analysis.

Towards the end, we also cover Bayesian Model Averaging in the context of an ABN, which is useful to assess the validity of the learned model and more advanced inference on the network.

- Language: English

- Duration: 120 mn

- N° Participants: 40

- Level: Beginner

This tutorial covers the basic use of R for creating maps. Useful tools, as well as data sources, are both presented. Concerning tools, the focus is on the packages osmplotr, tmap, and raster.

In the first part of the tutorial, you will learn how to use OpenStreetMap data. Geocoding and creation of bounding boxes will be presented as well as the use of shapefiles to create thematic maps and color-coding in R. After this introduction to the basic concepts and functionalities of mapping with R, you will go through a prototypical data analysis workflow: import, wrangling, exploration, (basic) analysis, reporting. You will have the opportunity to create your own maps during the workshop. A GitHub repo on the course will be shared.

- Language: English

- Duration: 120 mn

- N° Participants: 50

- Level: Advanced

During the last decade, several advanced approaches have been proposed to address computational issues of larger and larger multivariate space-time datasets. These can essentially be categorized as (i) construct "simpler" models or (e.g., low-rank models, composite likelihood methods, predictive process models) (ii) approximate the models (e.g., with Gaussian Markov random fields, compactly supported covariance function). In this tutorial, we discuss this last point by using sparse covariance matrix approximations. There is seemingly no limit to the sample size with the possibility of working with long vectors jointly with 64bit handling algorithms. However, the

devil is in the details and to avoid encountering negative surprises we provide best practices, strategies, and tricks when modeling huge spatial data.

- Language: English

- Duration: 180 mn

- N° Participants: 30

- Level: Intermediate to Advanced.

Did you always want to contribute to (base) R but don't know how? Come to our Tutorial!

We will show cases where and how users have contributed actively to (base) R, by submitting bug reports with minimal reproducible examples, how testing, reading source code, and providing patches to the R source code has helped making R better.

Depending on the participants willingness and level of sophistication, we will look into doing things right now, for currently non-resolved issues and bug reports.

- Language: English

- Duration: 150

- N° Participants: 24

- Level: Beginner

Multivariate categorical data present unique data visualization challenges. This tutorial provides two options to meet such challenges: mosaic plots and alluvial diagrams. First, we will focus on how to choose the best graph for given data types and communication goals. You will then learn how to get the underlying data in the correct shape to make each graph and then create both graph types using the vcd and ggalluvial packages. We will use engaging datasets and aim to equip you with the skills to make these graphs (and the choice whether to use them) on your own.