1 Reading layer nc_county_2020_pop' from data source ##https://drive.google.com/uc?export=download&id=1g9sGIikgOEubqoj97fUVoCYAKlBDVX5a’

2 using driver `GeoJSON’

3 Simple feature collection with 100 features and 4 fields

4 Geometry type: MULTIPOLYGON

5 Dimension: XY

6 Bounding box: xmin: -84.32182 ymin: 33.75288 xmax: -75.40012 ymax: 36.58814

7 Geodetic CRS: NAD83

durham_schools <- st_read(“https://drive.google.com/uc?export=download&id=1ajKBLeKpFMW8z0HCbnLi4C44tgryCsRt”)

8 Reading layer durham_schools' from data source ##https://drive.google.com/uc?export=download&id=1ajKBLeKpFMW8z0HCbnLi4C44tgryCsRt’

9 using driver `GeoJSON’

10 Simple feature collection with 74 features and 28 fields

11 Geometry type: POINT

12 Dimension: XY

13 Bounding box: xmin: -78.95519 ymin: 35.93524 xmax: -78.81451 ymax: 36.05743

14 Geodetic CRS: WGS 84

tm_shape(nc_counties) + tm_polygons(fill = “POP2020”, fill.scale = tm_scale_intervals(values = “bu_pu”))

tm_shape(nc_counties) + tm_polygons(fill = “POP2020”, fill.scale = tm_scale_intervals(values = “bu_pu”, style = “quantile”))

15 make an interactive map

tmap_mode(mode = “view”)

tm_shape(nc_counties) + tm_polygons(fill = “POP2020”, fill.scale = tm_scale_intervals(values = “bu_pu”, style = “quantile”))

tm_shape(nc_counties) + tm_polygons( fill = “POP2020”, fill.scale = tm_scale_intervals( values = “bu_pu”, style = “quantile”, n = 5 ), fill_alpha = 0.3, fill.legend = tm_legend( position = tm_pos_in(“left”, “bottom”), frame = TRUE, frame.r = 6, bg.color = “white”, item.height = 0.55, item.width = 0.55 ) ) + tm_title(“2020 Population by NC County”, size = 2) + tm_basemap(“OpenStreetMap”) + tm_layout( text.fontfamily = “serif”, frame = TRUE, frame.r = 15 )

acs_tract_nc <- st_read(“https://drive.google.com/uc?export=download&id=1xjkxq0KaIMAd8ag0e_m2B1O_he2CSyVR”) |> filter(COUNTYFP %in% c(“135”, “183”, “063”))