Production applications are often expected to emit "metrics" so that they can be monitored in real time for problems. However, traditional monitoring vendors expected developers to use their proprietary client libraries, none of which included support for R. This left R users without the option of supporting their organisation's existing vendor, and monitoring support for R applications (such as Shiny apps or Plumber APIs) has remained poor.

Over the last few years, the open-source Prometheus project has become the de facto metrics solution in the Kubernetes community, and its text-based format (now called 'OpenMetrics') has been formalised as a draft IETF standard. This effort is the closest thing in the monitoring community to a widely-accepted standard in its history.

This talk will introduce the {openmetrics} package, a client library for the OpenMetrics standard. It allows R applications to expose metrics to any Prometheus instance, or one of the growing number of open-source and commercial tools that can ingest the OpenMetrics format.

In addition to user-defined, application-specific metrics, the package also bundles general-purpose metrics and can automatically inject them into Shiny apps or Plumber APIs. This makes it easy for R users to add production-ready metrics to their applications in only a few lines of code.

As production use of R grows, expectations about production features -- such as metrics and corresponding alerts -- will grow as well.

At ixpantia we help organizations to become their most innovative and data-driven selves through personalized coaching and knowledge transfer, continuous and transparent code handover and the implementation of an efficient and cooperative data culture. To bring the results of data analysis to business processes and decision making, we usually need to automate their execution. Often this means that we need a daily process to write, for example, predicted values to a database.

The possibilities to add value to organizations through the use of tools that are available to us in R are legion. This value lies not only in advanced analytics, but also in the power of dynamic (automated) reports and pre-calculated values that combine multiple formal and informal data sources. The value of R for automating these tasks is often so high because the domain experts themselves are writing it, and can iterate at high speeds to answer to changes in their business context.

In this talk we will share some of the experiences we have had automating tasks with R. We will also present the pragmatic approach to run and monitor scheduled tasks that we have developed. This approach is based on Rmarkdown, a taskscheduler (such as cron) and a Shiny to monitor task execution and completion.

As a certified RStudio partner, we offer to deploy a full-fledged RStudio Workbench installation on your infrastructure (on-premise) or in the cloud.

In this talk we showcase the benefits of running a centralised RStudio Workbench installation and explain the added value of our containerised service compared to the default binary installation of RStudio Workbench.

TBA