map

I’ve been focusing on python recently to become a bi-lingual data scientist. Probably my least favorite thing about python is its plotting libraries - there are too many options built on top of matplotlib which pre-dates pandas dataframes. This makes for some clunky code and blurry boundaries (both “is that a seaborn, pandas, or matplotlib function?” and situations with 3 equally messy solutions but in very different ways). In my opinion, ggplot2’s deep interplay with dataframes makes a lot more sense and ggplot’s layers make it easy to change plot type (just switch the geom_), add facets, and tweak aesthetics.

When I lived in Tacoma, I was running quite a bit. Since I moved away my training has become much more irregular, but I thought it would be interesting to take the Tacoma data from my current Garmin Forerunner 220 a take a look. Data Prep The Garmin stores data in .fit format, but gpsbabel can translate to a nicely structured GPX file, which is what I’ll start with here.

Recently some diversity stats have been circulated around the College of Idaho, and as new Idahoan I wondered about the general diversity (or lack thereof) in Idaho. I remembered seeing this post a while back about mapping in R, so I went to work. Shapefiles First, we need shapefiles for both the Idaho country boundaries and census tracts, which will give finer detail for data. These can be downloaded from the [US Census Bureau] (https://www.