My R Journey: Rika Gorn

Rika Gorn is the Director of Data Analytics & Reporting at Covenant House International, a privately funded agency that provides shelter, immediate crisis care, and other services, to homeless and trafficked youth in the United States, Canada, and Latin America. Her work focuses on providing statistical analysis, data visualization, and reporting support to 21 sites across the agency.

Previously, she worked on quality assurance for a mobile mental health team at Coordinated Behavioral Care, strategic management and evaluation consulting at TCC Group, and program analysis at the Vera Institute of Justice. Rika received her Bachelors in Political Science from Hunter College and her Masters in Public Administration at the NYU Wagner School of Public Service.

Why did you decide to learn R?

I started learning R as an attempt to mitigate mistakes at work. I was working with more and more spreadsheets and no matter how careful I was or how much time I took, I kept making errors. In addition, my work just took a very long time to complete. I’ve always been interested in making systems and processes work more efficiently, and I found that hard to do when I had hundreds of data sheets to analyze by hand.

I’ve always been interested in making systems and processes work more efficiently, and I found that hard to do when I had hundreds of data sheets to analyze by hand.

It was disheartening, and I felt like I was doing very rote almost menial work and was ashamed to call myself an “analyst”. It took me a long time to recognize (and I’m still trying to accept this) that spreadsheet errors are human and inevitable. And not what we humans should be prioritizing our time on! Vicki Boykis has a wonderful blog post on this that made me go YES THIS and kinda tear up a little actually when I read it.

How easy or difficult was learning R? What resources were most helpful?

I’m obviously still learning R and will likely continue to do so forever. I started learning it about 2 years ago when I took a week-long bootcamp called Data Science Dojo for data engineering and they had the option of using R or Python. At that time, I was like “I don’t care about ML because no organization I’ve ever worked with even has big data, so I’ll learn R because statistics is my jam.” I’ve changed my tune since then, but that was the impetus to learn R.

The most challenging part of learning R for me was that I was completely alone at the beginning. After the bootcamp week, I bought R for Data Science and just started reading and trying things out. Looking back, I really wish I had a physical person I could just turn to and ask, “Am I doing this right?” It would have saved me many many hours of banging my head against a wall. Honestly, even now that I’ve been introduced to this thriving, wonderful, online R community, I wish I had more people around me or at my job that actually used R.  

Looking back, I really wish I had a physical person I could just turn to and ask, “Am I doing this right?” It would have saved me many many hours of banging my head against a wall.

Other challenging parts of learning R include just getting the hang of the syntax, which takes time and working with git, which can be a pain. I’m also a big believer in really understanding the statistics and theory behind your R code which takes time and patience. It’s easy to run something and see good p-values and think that the work is over. I strongly discourage everyone from doing that! Really understanding your data and its limitations is incredibly important.

Another challenging part of learning R has been simply recognizing that getting into data science doesn’t mean that you need to also be a data engineer or computer programmer. I took Visual Basic in high school but other than that or maybe enjoying doing logic puzzles since I was a kid, I did not have any coding experience. My stance is that every industry has tons and tons of data and learning how to work with it and learn from it happens everywhere, regardless of what your position is called. My previous experience doing quality assurance at a healthcare agency for example, gave me remarkable insight into how data can be used to improve patient care.

Another challenging part of learning R has been simply recognizing that getting into data science doesn’t mean that you need to also be a data engineer or computer programmer.

The “easiest” parts of learning R has definitely been accessing the thousands of resources available online, and well as joining the community. Specifically R4DS and R-Ladies, but in general, there are so many wonderful people giving their time and energy to help folks learn R.

In terms of resources, I started with swirl and R4DS but if I can give one piece of advice, it’s to start working on your own project or dataset. Try something, post it as an .rmd on github and let the world see your code. I think I may need to follow my own advice more often, but my knowledge of R only truly advanced when I started incorporating it into my own projects as opposed to doing problem sets or reading books.

In what ways has learning R changed your work?

My work has gotten a lot more efficient and a lot faster. I’m currently thinking a lot about automation for reporting. I have more time to think about the actual data and how it affects users and clients.

Also, its opened up a lot of career opportunities that I didn’t have available to me as a Political Science graduate. I have soft and hard skills that can be used in different industries and so I’m confident about exploring and growing into new places and positions. I’ve also recently joined the R-Ladies NYC board and am super excited to give back and maybe do some trainings/tutorials which is a crazy learning accelerant.

What do you think people considering learning R might not appreciate about it?

There’s a lot of jargon at first especially if you’re looking at places like Stack Overflow/Stack Exchange/Cross-Validated. Also, learning syntax and statistical theory at the same time is just plain hard. But know that it’s hard for literally everyone.

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