 Hi, my name is Emily Reuter and today I'm here to talk about the Project Manager R package to help you manage the human dependencies of your project. If we think about developer tools in R, we notice that they're very well automated and integrated with our overall workflow. For example, we have continuous integration with Travis or GitHub Actions that can help us do things like automated testing and deployment. In contrast, project management tends to be very manual and fragmented. It's spread across a ton of different channels like email and Slack or even when it's in a specified tool like Jura or Trello, it's still completely apart from our normal workflow. I wrote the Project Manager package to try to combat this and provide an easy way to conduct GitHub-based project management via R. It's integrated with the tools that we're already using. It responds and learns from our existing workflow to help organically capture and understand how our project is progressing and it automates a lot of the mundane and routine reporting tasks that we have to do for our management or our customers. So let me show you how this works. The first step is typically to use the create repo reference function to create a connection to a specific GitHub repository. For a new project, the next thing we typically do is define a project plan. This we can do in a simple YAML file. The specific details of this plan aren't very relevant, but at a high level it tells the story of building a shiny app with work done by both the design team and the development team. The YAML file defines three main milestones and seven issues. And we can push all this up to GitHub with two simple R commands. We read the plan into R and we post the plan to our repository. And we get all of that metadata, all of those issues and milestones and information up around our GitHub in simply two lines of code versus what I'd estimate to be around 75 button clicks if we were doing this through the GitHub user interface. And it looks just as we'd expect. Here are three milestones. Here are seven issues complete with labels, milestone and assignments. So at this point we could progress and just start working on our project, however we usually would. And wherever we want to provide some sort of status update, we can simply retrieve the issues from our repo and use the parse issues function to turn those into a data frame. And you can see we get a lot of fields with a lot of really rich information about each of those tasks. And we can communicate this to our customers in many different ways. With text-based reports, we can use functions like report plan or report progress to write out in a more human readable and digestible form either that initial plan we came up with or that plan enriched with more metadata about what issues we've completed, what tasks are still on the table and even link them back to those specific GitHub issues where users could go read for more context. Alternatively, project manager also provides a number of useful graphical summaries including task boards, Gantt charts and waterfall charts. But give more granularity. For example, now we've also gained a middle column to not only see what's opened and what's closed but what's currently being worked on. And this can be customized based on how you typically use your GitHub repo to signal some work is in progress. For example, we can capture in progress work based on when it was assigned to a specific user, when it was given any label or when it was given some specific label. And the package contains many other flexible verbs to help you describe what means in progress to you. Of course, there's also a lot of other really useful and interesting metadata hidden in labels and a GitHub repo. And I've shown some examples of how our OpenSci and our forwards can use labels to track what step of a process therein or what team an item belongs to. So project manager also includes functions that help extract this information very easily. For example, extracting all labels with the prefects of team. This helps create a new column in our data set which then we can usually use for more summaries and analysis. For example, seeing how many tasks each team is accountable for each milestone. There's a lot more detail about more advanced workflows and use cases on the website. So please check out EmilyReader.github.io slash project manager or get in touch for more details. Thank you.