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I have an existing vector shapefile with all the counties in the United States (called counties.shp) I also have a dataset that contains many species-county presence data points (species.csv) which looks something like this:

 GEOID  county    state         Species 
1: 21007 Ballard Kentucky     plant1
2: 21007 Ballard Kentucky     plant2
3: 21007 Ballard Kentucky     plant3
4: 21007 Ballard Kentucky     plant4 
5: 21017 Bourbon Kentucky     plant5

I'd like to create a new polygon for each species that includes all of the counties that they are paired with in the species.csv file. Each row is a unique species-county combination, so both counties and species are repeated throughout that file, yet I'd like to get a single polygon file for each species. There are several thousand species, so I'd like to have a way to do this all at once and compile the resulting polygons in a relatively neat way so I can easily use them for zonal statistics.

I'm imagining this is a somewhat simple task, but I'm completely new to GIS in R.

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  • what do you mean for "new polygon", a multipolygon of the counties? a convex hull? a new file?
    – Elio Diaz
    Apr 16, 2021 at 2:05
  • Do you know the basics of spatial data in R, using the sf package? eg how to read shapefiles, plot them, inspect them etc?
    – Spacedman
    Apr 16, 2021 at 7:51
  • The first two rows you listed don't look a "unique species-county" - is row 2 meant to say "plant2"?
    – Spacedman
    Apr 16, 2021 at 7:52
  • @ElioDiaz a multipolygon is what I'm after.
    – pfadenhw
    Apr 16, 2021 at 12:12
  • @Spacedman I've been using rgdal instead of sf but I've been able to read shapefiles and rasters and plot them using that. And yes, that was a typo, thanks for the catch I've corrected the original.
    – pfadenhw
    Apr 16, 2021 at 12:12

1 Answer 1

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This approach filters the county polygons and them puts them together using lapply to iterate for each plant

library(sf)
library(dplyr)
library(tmap)

nc = st_read(system.file("shape/nc.shp", package="sf"))

plants = c("p1", "p2", "p3", "p4")
# sample the counties twice to get more than one plant by county
plants_by_county = data.frame(county = sample(nc$NAME, 200, replace = T), plant =  sample(plants, 200, replace = T))

# data frame looks like this
print(plants_by_county)
          county plant
1         Yancey    p4
2          Davie    p2
3       Buncombe    p2
4       Alamance    p1

plant_multipolygons = lapply(plants, function(x) {
  nc %>%
    filter(NAME %in% plants_by_county$county[plants_by_county$plant == x]) %>% # filter counties
    select(geometry) %>%
    summarise() %>% # comment out summarise to get single polygons
    mutate(plant = x) })

plant_multipolygons = do.call(rbind, plant_multipolygons) # bind them into a data.frame

tm_shape(plant_multipolygons) + tm_polygons() + tm_facets("plant")

enter image description here

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  • 1
    you must transform your spatialPolygons object to sf with st_as_sf(your_polygon_object)
    – Elio Diaz
    Apr 16, 2021 at 16:44
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    pass dplyr::select() to instantiate properly
    – Elio Diaz
    Apr 16, 2021 at 16:55
  • 1
    you have 2 options: stick with sf which calculates st_area from a EPSG:4326 CRS and returns ellipsoidal area in m²; or use a projected CRS such as UTM or lambert
    – Elio Diaz
    Apr 16, 2021 at 17:37
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    I think sticking with this process should work as long as I can figure out how to make sure my rasters are in the same CRS. Thank you for all your help!
    – pfadenhw
    Apr 16, 2021 at 17:41
  • 1
    I just updated R (now version 4.1.0) and all of a sudden this code is running VERY SLOWLY - it used to take about 3 minutes to run, now it takes upwards of 2 hours... Any idea why this happened?
    – pfadenhw
    Jun 30, 2021 at 14:50

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