sf VS tidycensus

sf

Before learning about geostatistics, do you feel comfortable working with and exploring geospatial data? If not, I'd highly recommend getting comfortable with the sf package in R. It's an implementation of the OpenGIS standard in R tidyverse. The OpenGIS standard defines specific data types and functions for geospatial data, which means that you can read e.g. PostGIS documentation and use the same functions in R.

Tools used: various packages in R (tidycensus, dplyr, ggplot2, sf)

R supports working with spatial data really well; you should look into the sf-package: https://r-spatial.github.io/sf/

A lot of the books are out if date because geospatial has been rewritten from the ground up to be dramatically improved. I would focus on the sf package: https://r-spatial.github.io/sf/. I would also find a PostGIS book which sf shares many functions and learn to the the database when appropriate.

Are you using the sf package? I envision running a loop that creates a vector feature for each cell of the raster grid, intersects that feature with the underlying shapefile, multiplies the area of each intersected portion by its value, and assigns the raster the mean of those values. Kind of a lot to set up, but I'm not a master at this so maybe someone else knows a more straightforward method

I found this: https://github.com/r-spatial/sf/issues/1595

Now that I explained the concept, I will show some R code using the sf library to achieve this. sf stands for simple features and it's a very nice library for working with geospatial data.

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/).