hoopR VS tidycensus

Thankfully, I've made use of the hoopR package which lets me get data that is up to date, primarily by pulling info from ESPN's APIs. While that does most of the work, there's tweaking that I need to do (most particularly for streamed game broadcasts that aren't ESPN3/ESPN+) but it usually goes fairly swimmingly.

hoopR (college and pro basketball)

https://github.com/saiemgilani/hoopR Digging around through here will probably help you find a csv which is easy to convert to excel

Next, we’ll look at a few measures of performance on the court to see whether certain signs are better or worse suited to particular aspects of the game. For each of these, I took player box score statistics (sourced from https://github.com/saiemgilani/hoopR), grouped them by the player’s astrological sign, and calculated their average numbers through the 2020-21 season. First, we’ll look at scoring -- the chart below shows each sign’s average points per game.

You could use the tidycensus package to get the information you need

the census (accessible using tidycensus or you can download bulk data straight from their website)

Data sources: Minnesota Secretary of State website, American Community Survey via tidycensus

If you happen to use R the tidycensus package is absolutely fantastic.

Thanks! I used the tidycensus package in RStudio https://walker-data.com/tidycensus/

R has a few packages for this and the first that comes to mind is tidycensus (https://walker-data.com/tidycensus/).

If you have some facility with R, then the 'tidycensus' package is your answer here. You can get the latest ACS (2019) data at the block group level with that. Alternatively, NHGIS from the IPUMS folks offers access to census tabular and spatial data in a straightforward format.

This indicates the polygons are different - I would ASSUME that the shape for a US county does not change too much (or ever) so I feel like its an issue with the package [tidycensus](https://walker-data.com/tidycensus/).