How R Has Transformed the Work of the Michigan Fitness Foundation
When staff at the Michigan Fitness Foundation (MFF) reached out to R for the Rest of Us about custom training, they wanted to figure out how best to connect R (which they assumed they’d use to clean and analyze their data) and Tableau (which they assumed would be their tool for reporting).
This type of request is common: an organization has some familiarity with R (they were right that it is a great tool to clean and analyze data) but also makes some assumptions about its limitations that turn out not to be 100% accurate. In this case, it was the idea that we’d need to use Tableau for reporting. In the time since we started working together, Michigan Fitness Foundation staff has learned to efficiently clean and analyze their data AND how to use R to share results with their stakeholders.
The mission of Michigan Fitness Foundation is to “improve health through increased access to physical activity opportunities, healthy food, and related education and events.” MFF staff helps to conduct evaluations of programs throughout the state, helping organizations to improve the health of the people of Michigan.
Working with multiple organizations throughout the state offers logistical challenges. For example, MFF collects survey data from partner organizations. MFF staff need to efficiently clean and analyze this data, and share results back with each organization. They knew that R could help them, but they didn’t know quite how.
That’s why the Michigan Fitness Foundation hired R for the Rest of Us for custom training. MFF staff began by taking several courses offered by R for the Rest of Us. From there, we conducted regular project support calls. These calls consist of demonstrations of particular topics of interest as well as free time for MFF staff to ask questions that have come up in their R work.
In a bit over a year of work, MFF has come incredibly far. They pull survey data directly from Qualtrics, skipping several steps involving downloading and importing data. They do nearly all of their data cleaning and analysis in R. They produce reports and even websites in R. They are even working on developing an internal package to automate their work.
Switching to R has totally transformed MFF’s work. Says Evaluation Specialist Kerri Vasold, “I had big dreams of how R could improve my workflow – one platform that could do data cleaning, stats and make well-designed charts and tables sounded mythical. I had no idea all that R could offer us in workflow and forward progress in how we manage and use data to tell the story of the programs we support.”
When the team at the Michigan Fitness Foundation reached out, they had a vague sense of what R could do. Now, having used it consistently for over a year, they appreciate the full range of capabilities R has. And they have become convinced it’s the right tool for them. Says Data Specialist Michelle LaFleche, ” R’s ability to create efficient workflows – from importing, wrangling, reporting, and sharing data – is a total game changer for us.”
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