Date: Wednesday, March 18, 2026
Hello! I’m Christian Martinez, instructor with Evaluation + Learning Consulting, faculty at Brooklyn College (CUNY), founder of Angles Analytics. In my graduate courses and DCAS trainings, I focus on one core goal: helping build reproducible data skills using R.
When I first learned R, it was love at first sight. But for many learners, R can feel like learning to juggle upside down. Early last semester, I noticed my students were struggling — not because of coding syntax, but because of disconnection. In the beginning, I was teaching R using the classic mtcars dataset that comes preloaded, but none of my students cared about cars. Why would they stay motivated while learning something abstract with data that felt irrelevant?
That week changed my approach.
We often assume rigor comes first and engagement follows. In my experience, the opposite is true. When learners care about the data, they push themselves to learn the methods.
I shifted from generic datasets to real civic data from the NYC Open Data Portal — data about housing, transportation, sanitation, and everything in-between. Suddenly, students were analyzing neighborhoods they grew up in. They were asking deeper questions. Reproducibility stopped being an abstract concept and became a tool for investigating real issues.
When people see themselves in the data, learning accelerates.
One reason many people hesitate to use open data is technical friction. Pulling data from APIs can feel intimidating for beginners, requiring its own lesson entirely.
To reduce that barrier, I created a lightweight R package, nycOpenData. This tool allows users to access NYC Open Data directly inside R without needing to first learn API mechanics. By removing that initial hurdle, learners can focus on analysis and interpretation before diving into technical infrastructure.
The key is sequencing. Teach meaning first. Teach plumbing later.
In my DCAS trainings, participants work across NYC agencies — transportation, sanitation, health, and more. Near the end of each training, I show them how they can use R to explore their own agency’s data.
Almost every time, someone says, “Wait… all of this data is online and free?”
That moment matters.
Instead of ending class with a syntax recap, I end with exploration. I encourage them to go home and “play” with data that connects to their work. If homework feels like discovery, not obligation, people continue learning long after the training ends.
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