Comments after being instructor about r-geospatial-fundamentals-legacy HOT 2 OPEN

Hi Irene,
Re: line 261, I have several thoughts on this. First, spatial features != geospatial features and there are some users of the sf package who don't work with geo space. Second, understanding how to construct a geospatial feature is really helpful for debugging problems that occur at a low level. But this example should be more built out by (1) creating a spatial object (2) adding a CRS to it and (3) adding attributes as a simple demonstration.

As for base R vs. dpylr, either works. The dplyr route assumes someone knows R already, but many folks come to R Geo knowing desktop GIS and a tiny bit of R but not the tidyverse.

As for write.csv, I don't know if it can extract the geometry from an sf object and add it to a csv file the way sf_write can. You may be able to do it for points but not more complex geometries and there are reasons one may want to write these geometries to a CSV file. For example if I save a CSV file with complex polygon geometries as WKT plus lots of other attributes then I can read it into excel or google sheets, do some calculations on the numeric columns and then save & import into QGIS. Lke a geojson file, I would only have one file and not a bunch like a shapefile, and a csv file is highly compressible. Anyway there are reasons, but they may be too complex for discussing in this workshop and that's a good reason to cut from the workshop.

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Hi Patty,

Thanks so much for your comments.

Re: dplyr, I agree. I guess for some other base R seemed to confuse them, but I guess that picking either base R or dplyr is wiser than having both examples all the time and actually learning base R is a good skill to have.

Thanks for clarifying about sf_write and exporting the geometry for more complex geometries. I guess that adding this explanation could be a good idea, because it's an important clarification!

Thanks again for taking the time to respond.

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