Driving more efficient and reproducible workflows with Posit Academy

2025-09-03

State environmental agencies bear the crucial responsibility of ensuring the long-term health and sustainability of ecosystems within their jurisdictions. This work requires processing vast amounts of local water and air quality data using code-first tools such as R and Python. These organizations often have pockets of R and Python users with valuable skills, but these individuals are typically scattered across different teams and vary widely in skill level. This can lead to “knowledge silos” that hinder collaboration and slow the pace of development.

In an effort to break down these barriers and build a more connected community of R users, one such agency has tried traditional, condensed workshops in the past, but found the “firehose of information” approach made it difficult for team members to retain lasting skills. Seeking a more effective path forward, they turned to Posit Academy for a training solution that offers more time for learning and repeated, hands-on practice with real-world data sets under the guidance of a skilled mentor.

We followed up with a cohort of learners three months after they completed the Posit Academy Programming in R course. The results were transformative – participants reported using their new skills on a daily basis, saving 5 to 25 hours of work every week. Here’s how they did it.

Automating tedious tasks

For one Environmental Specialist, the skills they learned in the course provided a direct solution to a repetitive and time-consuming part of their job. Prior to Academy, their workflow involved manually creating and updating R Markdown documents for individual air pollutants they needed to review. After the course, everything changed.

Taking the [Programming in R] course and learning about Quarto parameterization and purrr::walk2() has been so helpful in my job… I was able to build a tool that automatically formats and outputs reports given various datasets. It has saved me so much time and made my R code much more flexible, readable, and useful!

This is the power of moving from manual work to an automated workflow—freeing up valuable time for more critical analysis.

Building robust, reproducible analyses

For a Water Quality Assessment Data Coordinator, the value came from transforming a “disorganized and confusing” legacy process in Excel into a streamlined, reliable R-based workflow. Their team is responsible for creating an annual nuisance algae assessment for a local river; using skills from the course, they created a new process for this analysis in R that is faster, more organized, and fully reproducible.

I developed a custom function in R that performs the whole analysis, completing it in under 3 minutes… I’d say this single repo and custom function will be saving ~4 weeks of work every year.

Beyond the massive time savings, the new scripted process makes their work more transparent and accessible – “we frequently have public requests for these data,” they added, “so having a scripted process to organize them is invaluable.”

Unlocking new capabilities with modern tools

The impact of the training went beyond just improving existing workflows; it empowered the team to build entirely new solutions. A Water Quality Monitoring Data Analyst is now working on a Shiny application to track budgets related to water monitoring costs—a task previously handled with a “workflow involving a SAS script and cutting/pasting results into Excel.”

Their new Shiny app automates the process and incorporates multiple skills from the course, including iteration with purrr::map() and building custom functions. This represents a huge leap forward, moving from a static, manual process to a dynamic, interactive, and reliable tool.

A new foundation for data science

These stories show that the right training does more than just teach a programming language. It changes how people work, empowering them to build more efficient, reproducible, and innovative solutions to the challenges they face every day.

By investing in a learning model that emphasizes practice, mentorship, and real-world application, this team has built a stronger, more collaborative foundation for data science that will serve their mission for years to come.