I'm trying to calculate density within a shapefile, but I'm fairly confident I'm doing it wrong. The idea is to figure out which geographical regions there have been the most sales by density.
Here is a link to the file that I use (testdata.shp)
library(sf)
sample <- st_read("testdata.shp")
sample$area <- st_area(sample$geometry)
density_calc <-sample %>% st_buffer(0) %>% group_by(areas) %>% summarise(`Sales (density)` = sum(sales)/sum(area))
Here are the details of the shapefile:
Geometry set for 2106 features
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -120.0065 ymin: 35.00184 xmax: -114.0396 ymax: 42.00221
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
I guess my issue is, I don't really know what is right and wrong, so I have no clue if I did it correctly.
Sorry if it's not the most extensive question, I just don't remember my high school geometry that well!
the raster
package helps make this calculation very easy and just like working with a data.frame in R
:
library(raster)
list.files(workDir)
test_shp <- shapefile(file.path(workDir, 'testdata.shp'))
names(test_shp)
#[1] "distrct" "sbdstrc" "terrtry"
#[4] "region" "turf" "sales"
#[7] "leads" "cnvrsns" "areas"
sum(is.na(test_shp$sales)) #note that 346 polygons have no sales data
#get the area as square kilometers
test_shp$km2 <- area(test_shp) / 10000
#calc the sales density
test_shp$sales_density <- test_shp$sales / test_shp$km2
#calculate the 25th, 50th, and 75th percentile of all polygons
quartiles <- quantile(test_shp$sales_density, probs=c(0.25, 0.5, 0.75), na.rm=TRUE)
#plot the result, coloring by which percentile the sales density is for a given polygon
plot(test_shp, col=ifelse(is.na(test_shp$sales_density), 'gray', ifelse(test_shp$sales_density >= quartiles[3], 'dark green', ifelse(test_shp$sales_density >= quartiles[2], 'light green', ifelse(test_shp$sales_density >= quartiles[1], 'yellow', 'red')))), border='transparent') (eg. >75th, 50-75th, etc.)
#add the legend
legend('bottomleft', legend=c('Q4', 'Q3', 'Q2', 'Q1', 'No data'), pch=15, col=c('dark green', 'light green', 'yellow', 'red', 'gray'))
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