Stéphane Guillou

Processing the wealth of Bugzilla data the LibreOffice generates can be done in R, and presenting it in Shiny is a good fit for it. Making a useful dashboard for the community is not only about pretty graphs and an avalanche of stats, it's also about directing potential QA contributors to relevant information and specific tasks, and prioritising some of the most pressing onces.

Learn how to use a variety of R packages to get and visualise OSM data.

As an interactive, open-source, high-level programming language designed for data science, R is a great fit for dealing with OpenStreetMap data and introducing beginners to reproducible scientific computing. In this workshop, you will learn how you can use a variety of R packages that are ideal to: * acquire relevant OSM data in a vector format * clean and process the data * display it in static and interactive visualisations * share a visualisation with others * export the data to other programs for further editing Most data acquisition, cleaning, processing, visualising and exporting can be done writing an R script. For further refinement of an output, other point-and-click programs (like QGIS) might be required, but storing the bulk of the process in a script allows us to easily reapply the same process on an updated dataset, share the process with others so they can learn from it, and provide supporting evidence when publishing results. Basic knowledge of R is preferred to follow along this workshop. If you want to run the commands on your own computer and learn by doing, please install both [R](https://cran.r-project.org/) and [RStudio](https://www.rstudio.com/products/rstudio/download/#download) prior to attending, as well as the necessary libraries for spatial data processing (see [OS-specific instructions](https://r-spatial.github.io/sf/#installing)).