Faster matrix math in R on macOS

Update (October 2021): macOS 10.14 “Big Sur” and later do not ship with Accelerate BLAS dynamic libraries in the filesystem, so this trick only works up to macOS 10.13 “High Sierra”

If you want faster matrix operations in R on your Mac, you can use Apple’s BLAS (Basic Linear Algebra Subprograms) library from their Accelerate framework instead of the library which comes with the R binary that you get from CRAN. (Unless you built R from source yourself.) CRAN recommends against this, saying:

My recipe for the best breakfast potatoes (and terrific bacon)

Everyone I treat with these bomb-ass potatoes always tells me how amazing they are and it’s a bit of an elaborate process to describe, so I decided to write it up here. There are actually two recipes in this post and one is (kind of) a prerequisite for the other, but if you’re vegetarian/vegan or don’t eat pork for religious (or other) reasons, feel free to skip to the second stage.

Data Analyst vs Data Scientist: Industry Perspectives

Both “Data Analyst” (DA) and “Data Scientist” (DS) are titles that vary greatly between industries and even amongst individual organizations within industries. As the roles behind titles change over time, it is natural for some teams to ask themselves the following questions: should we have distinct roles or just stick to one? How would we differentiate the roles in a way that fulfills our organization’s needs and is generally consistent with similar organizations? Do we want to consider a DS to be equivalent to a Sr. DA, the only difference being the title? Answering these questions not only establishes clear responsibilities and expectations, but enables hiring managers and recruiters to communicate clearly with potential applicants in the future (in job postings, for example).

Resources for learning to visualize data with R/ggplot2

I’m currently learning visualisation with R/ggplot2 and was wondering whether you could share tips/links/videos/books/resources that helped you in your journey :-)

Sure! Here ya go:

Tips

The only tip I’ll give is that you should strive to make every chart look exactly how you want it to look and say exactly what you want it to say. You will learn in the process of doing. When it’s time to visualize the data and you have an idea for a very specific look and story, don’t give it up or compromise on your vision just because you don’t know how to do it. Trust me, there is so much documentation out there and so many posts on Stack Overflow that you will be able to figure it out. (But also it’s totally fine to get 90-95% of the way there and call it done if that last 5-10% sprint is driving you bonkers.)

The journey so far…

I recently received an email which said, “I’m interested in learning more about you and your journey to where you are today,” so I thought I’d describe how I went from studying visual arts to analyzing data at Wikimedia Foundation (WMF).

Growing up I excelled in visual arts and mathematics at school, and they continued to be my strongest subjects. My parents and I immigrated to US from Russia when I was 10, and I spent the first few years focused on learning English – which was especially difficult because I was the only Russian-speaking person at my school. I was okay at English when I entered 6th grade, having learned a lot of it from The Simpsons of all things. That was also the year I joined band and started learning trombone, but that wouldn’t last.