JuliaConnectoR VS sf

Awesome resource!! On my side, I found the opposite R package to use Julia directly in R (https://github.com/stefan-m-lenz/JuliaConnectoR). In your opinion, what would be the most efficient?

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.