R Week 8 Assignment
Areeba Ghumman
3/20/2026
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R Spatial Lab Assignment # 2
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task 1: Join the COVID-19 data to the NYC zip code area data (sf or sp polygons).
load("../Week7/Lab1_sf_files.RData")
ls()## [1] "nyc_retail_sf" "nys_health_facilities_sf"
## [3] "postal_areas"colnames(postal_areas)## [1] "ZIPCODE" "BLDGZIP" "PO_NAME" "POPULATION" "AREA"
## [6] "STATE" "COUNTY" "ST_FIPS" "CTY_FIPS" "URL"
## [11] "SHAPE_AREA" "SHAPE_LEN" "geometry"covid <- readr::read_csv("R-Spatial_II_Lab/tests-by-zcta_2020_04_12.csv", lazy = FALSE)## Rows: 178 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (4): MODZCTA, Positive, Total, zcta_cum.perc_pos
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(covid)## spc_tbl_ [178 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ MODZCTA : num [1:178] NA 10001 10002 10003 10004 ...
## $ Positive : num [1:178] 1934 211 539 279 23 ...
## $ Total : num [1:178] 2082 448 1024 662 59 ...
## $ zcta_cum.perc_pos: num [1:178] 92.9 47.1 52.6 42.1 39 ...
## - attr(*, "spec")=
## .. cols(
## .. MODZCTA = col_double(),
## .. Positive = col_double(),
## .. Total = col_double(),
## .. zcta_cum.perc_pos = col_double()
## .. )
## - attr(*, "problems")=<externalptr>class(postal_areas$ZIPCODE)## [1] "character"class(covid$MODZCTA)## [1] "numeric"dplyr::left_join(postal_areas %>%
dplyr::mutate(ZIPCODE=as.numeric(as.character(ZIPCODE))),
covid,
by = c('ZIPCODE' = 'MODZCTA')) -> zip_covid_merge
names(zip_covid_merge)## [1] "ZIPCODE" "BLDGZIP" "PO_NAME"
## [4] "POPULATION" "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS" "CTY_FIPS"
## [10] "URL" "SHAPE_AREA" "SHAPE_LEN"
## [13] "Positive" "Total" "zcta_cum.perc_pos"
## [16] "geometry"zip_covid_merge[1]## Simple feature collection with 263 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 913129 ymin: 120020.9 xmax: 1067494 ymax: 272710.9
## Projected CRS: NAD83 / New York Long Island (ftUS)
## First 10 features:
## ZIPCODE geometry
## 1 11436 POLYGON ((1038098 188138.4,...
## 2 11213 POLYGON ((1001614 186926.4,...
## 3 11212 POLYGON ((1011174 183696.3,...
## 4 11225 POLYGON ((995908.4 183617.6...
## 5 11218 POLYGON ((991997.1 176307.5...
## 6 11226 POLYGON ((994821.5 177865.7...
## 7 11219 POLYGON ((987286.4 173946.5...
## 8 11210 POLYGON ((995796 171110.1, ...
## 9 11230 POLYGON ((994099.3 171240.7...
## 10 11204 POLYGON ((989500.2 170730.2...task 2: Aggregate the NYC food retails store data (points) to the zip code data, so that we know how many retail stores in each zip code area. Note that not all locations are for food retail. And we need to choose the specific types according to the data.
st_crs(zip_covid_merge)## Coordinate Reference System:
## User input: NAD83 / New York Long Island (ftUS)
## wkt:
## PROJCRS["NAD83 / New York Long Island (ftUS)",
## BASEGEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]],
## CONVERSION["SPCS83 New York Long Island zone (US survey foot)",
## METHOD["Lambert Conic Conformal (2SP)",
## ID["EPSG",9802]],
## PARAMETER["Latitude of false origin",40.1666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8821]],
## PARAMETER["Longitude of false origin",-74,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8822]],
## PARAMETER["Latitude of 1st standard parallel",41.0333333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8823]],
## PARAMETER["Latitude of 2nd standard parallel",40.6666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8824]],
## PARAMETER["Easting at false origin",984250,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8826]],
## PARAMETER["Northing at false origin",0,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8827]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## USAGE[
## SCOPE["Engineering survey, topographic mapping."],
## AREA["United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk."],
## BBOX[40.47,-74.26,41.3,-71.8]],
## ID["EPSG",2263]]st_crs(nyc_retail_sf)## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## ENSEMBLE["World Geodetic System 1984 ensemble",
## MEMBER["World Geodetic System 1984 (Transit)"],
## MEMBER["World Geodetic System 1984 (G730)"],
## MEMBER["World Geodetic System 1984 (G873)"],
## MEMBER["World Geodetic System 1984 (G1150)"],
## MEMBER["World Geodetic System 1984 (G1674)"],
## MEMBER["World Geodetic System 1984 (G1762)"],
## MEMBER["World Geodetic System 1984 (G2139)"],
## MEMBER["World Geodetic System 1984 (G2296)"],
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]],
## ENSEMBLEACCURACY[2.0]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]nyc_retail_sf <- st_transform(nyc_retail_sf, st_crs(zip_covid_merge))
zip_food_join <- sf::st_join(zip_covid_merge, nyc_retail_sf, join = sf::st_contains)
names(zip_food_join)## [1] "ZIPCODE" "BLDGZIP" "PO_NAME"
## [4] "POPULATION" "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS" "CTY_FIPS"
## [10] "URL" "SHAPE_AREA" "SHAPE_LEN"
## [13] "Positive" "Total" "zcta_cum.perc_pos"
## [16] "County" "License.Number" "Operation.Type"
## [19] "Establishment.Type" "Entity.Name" "DBA.Name"
## [22] "Street.Number" "Street.Name" "Address.Line.2"
## [25] "Address.Line.3" "City" "State"
## [28] "Zip.Code" "Square.Footage" "Location"
## [31] "Coords" "geometry"unique(nyc_retail_sf$Establishment.Type[!is.na(nyc_retail_sf$Establishment.Type)])## [1] "JAC" "A" "JACD" "JACDK" "JAD" "JABCHK" "JACHK" "JABC"
## [9] "JAZ" "JABCK" "JACK" "JACDHK" "JABH" "JACH" "JACDE" "JABCH"
## [17] "JABCDH" "JABK" "JABHK" "JABCD" "JACG" "JACDH" "JADHK" "JKA"
## [25] "JADK" "JAB" "JAHK" "JABCDK" "JACZ" "JAK" "JADO" "JDA"unique(nyc_retail_sf$Operation.Type[!is.na(nyc_retail_sf$Operation.Type)])## [1] "Store"food_count <- zip_food_join %>% dplyr::group_by(ZIPCODE) %>% dplyr::summarise(num_food_store = sum(!is.na(Operation.Type)))
zip_food_count <- sf::st_join(zip_covid_merge, food_count, by = "ZIPCODE")task 3: Aggregate the NYC health facilities (points) to the zip code data. Similarly, choose appropriate subtypes such as nursing homes from the facilities.
names(nys_health_facilities_sf)## [1] "Facility ID" "Facility Name"
## [3] "Short Description" "Description"
## [5] "Facility Open Date" "Facility Address 1"
## [7] "Facility Address 2" "Facility City"
## [9] "Facility State" "Facility Zip Code"
## [11] "Facility Phone Number" "Facility Fax Number"
## [13] "Facility Website" "Facility County Code"
## [15] "Facility County" "Regional Office ID"
## [17] "Regional Office" "Main Site Name"
## [19] "Main Site Facility ID" "Operating Certificate Number"
## [21] "Operator Name" "Operator Address 1"
## [23] "Operator Address 2" "Operator City"
## [25] "Operator State" "Operator Zip Code"
## [27] "Cooperator Name" "Cooperator Address"
## [29] "Cooperator Address 2" "Cooperator City"
## [31] "Cooperator State" "Cooperator Zip Code"
## [33] "Ownership Type" "Facility Location"
## [35] "geometry"unique(nys_health_facilities_sf$`Short Description`[!is.na(nys_health_facilities_sf$`Short Description`)])## [1] "HSPC" "NH" "DTC" "CHHA" "HOSP-EC" "DTC-EC" "HOSP-SB"
## [8] "HOSP" "ADHCP" "LTHHCP"unique(nys_health_facilities_sf$`Facility County`[!is.na(nys_health_facilities_sf$`Facility County`)])## [1] "Broome" "Oneida" "Wyoming" "Allegany"
## [5] "Erie" "Nassau" "New York" "Madison"
## [9] "Kings" "Suffolk" "Orange" "Franklin"
## [13] "Cattaraugus" "Monroe" "Clinton" "Westchester"
## [17] "Albany" "Bronx" "Chemung" "Otsego"
## [21] "Dutchess" "Niagara" "Queens" "Jefferson"
## [25] "Rensselaer" "Richmond" "Ontario" "Livingston"
## [29] "Wayne" "Columbia" "Putnam" "Oswego"
## [33] "Essex" "Ulster" "Onondaga" "Saint Lawrence"
## [37] "Rockland" "Washington" "Montgomery" "Steuben"
## [41] "Schuyler" "Warren" "Genesee" "Tompkins"
## [45] "Saratoga" "Fulton" "Chautauqua" "Schenectady"
## [49] "Orleans" "Cortland" "Herkimer" "Schoharie"
## [53] "Cayuga" "Sullivan" "Yates" "Delaware"
## [57] "Lewis" "Chenango" "Tioga" "Greene"
## [61] "Seneca" "Hamilton"nyc_health_facilities <- nys_health_facilities_sf %>% dplyr::filter(`Facility County` %in% c("New York", "Kings", "Bronx", "Queens", "Richmond"))
st_crs(zip_food_count)## Coordinate Reference System:
## User input: NAD83 / New York Long Island (ftUS)
## wkt:
## PROJCRS["NAD83 / New York Long Island (ftUS)",
## BASEGEOGCRS["NAD83",
## DATUM["North American Datum 1983",
## ELLIPSOID["GRS 1980",6378137,298.257222101,
## LENGTHUNIT["metre",1]]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## ID["EPSG",4269]],
## CONVERSION["SPCS83 New York Long Island zone (US survey foot)",
## METHOD["Lambert Conic Conformal (2SP)",
## ID["EPSG",9802]],
## PARAMETER["Latitude of false origin",40.1666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8821]],
## PARAMETER["Longitude of false origin",-74,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8822]],
## PARAMETER["Latitude of 1st standard parallel",41.0333333333333,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8823]],
## PARAMETER["Latitude of 2nd standard parallel",40.6666666666667,
## ANGLEUNIT["degree",0.0174532925199433],
## ID["EPSG",8824]],
## PARAMETER["Easting at false origin",984250,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8826]],
## PARAMETER["Northing at false origin",0,
## LENGTHUNIT["US survey foot",0.304800609601219],
## ID["EPSG",8827]]],
## CS[Cartesian,2],
## AXIS["easting (X)",east,
## ORDER[1],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## AXIS["northing (Y)",north,
## ORDER[2],
## LENGTHUNIT["US survey foot",0.304800609601219]],
## USAGE[
## SCOPE["Engineering survey, topographic mapping."],
## AREA["United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk."],
## BBOX[40.47,-74.26,41.3,-71.8]],
## ID["EPSG",2263]]st_crs(nyc_health_facilities)## Coordinate Reference System:
## User input: EPSG:4326
## wkt:
## GEOGCRS["WGS 84",
## ENSEMBLE["World Geodetic System 1984 ensemble",
## MEMBER["World Geodetic System 1984 (Transit)"],
## MEMBER["World Geodetic System 1984 (G730)"],
## MEMBER["World Geodetic System 1984 (G873)"],
## MEMBER["World Geodetic System 1984 (G1150)"],
## MEMBER["World Geodetic System 1984 (G1674)"],
## MEMBER["World Geodetic System 1984 (G1762)"],
## MEMBER["World Geodetic System 1984 (G2139)"],
## MEMBER["World Geodetic System 1984 (G2296)"],
## ELLIPSOID["WGS 84",6378137,298.257223563,
## LENGTHUNIT["metre",1]],
## ENSEMBLEACCURACY[2.0]],
## PRIMEM["Greenwich",0,
## ANGLEUNIT["degree",0.0174532925199433]],
## CS[ellipsoidal,2],
## AXIS["geodetic latitude (Lat)",north,
## ORDER[1],
## ANGLEUNIT["degree",0.0174532925199433]],
## AXIS["geodetic longitude (Lon)",east,
## ORDER[2],
## ANGLEUNIT["degree",0.0174532925199433]],
## USAGE[
## SCOPE["Horizontal component of 3D system."],
## AREA["World."],
## BBOX[-90,-180,90,180]],
## ID["EPSG",4326]]nyc_health_facilities <- st_transform(nyc_health_facilities, st_crs(zip_food_count))
nyc_hosp <- nyc_health_facilities %>% dplyr::filter(`Short Description` == "HOSP")
zip_food_health <- sf::st_join(zip_food_count, nyc_hosp, join = sf::st_contains)
names(zip_food_health)## [1] "ZIPCODE.x" "BLDGZIP"
## [3] "PO_NAME" "POPULATION"
## [5] "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS"
## [9] "CTY_FIPS" "URL"
## [11] "SHAPE_AREA" "SHAPE_LEN"
## [13] "Positive" "Total"
## [15] "zcta_cum.perc_pos" "ZIPCODE.y"
## [17] "num_food_store" "Facility ID"
## [19] "Facility Name" "Short Description"
## [21] "Description" "Facility Open Date"
## [23] "Facility Address 1" "Facility Address 2"
## [25] "Facility City" "Facility State"
## [27] "Facility Zip Code" "Facility Phone Number"
## [29] "Facility Fax Number" "Facility Website"
## [31] "Facility County Code" "Facility County"
## [33] "Regional Office ID" "Regional Office"
## [35] "Main Site Name" "Main Site Facility ID"
## [37] "Operating Certificate Number" "Operator Name"
## [39] "Operator Address 1" "Operator Address 2"
## [41] "Operator City" "Operator State"
## [43] "Operator Zip Code" "Cooperator Name"
## [45] "Cooperator Address" "Cooperator Address 2"
## [47] "Cooperator City" "Cooperator State"
## [49] "Cooperator Zip Code" "Ownership Type"
## [51] "Facility Location" "geometry"health_count <- zip_food_health %>% dplyr::group_by(ZIPCODE.x) %>% dplyr::summarise(num_hosp = sum(!is.na(`Short Description`)))
zip_food_health_by_hospitals <- sf::st_join(zip_food_count, health_count, by= "ZIPCODE")
names(zip_food_health_by_hospitals)## [1] "ZIPCODE.x.x" "BLDGZIP" "PO_NAME"
## [4] "POPULATION" "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS" "CTY_FIPS"
## [10] "URL" "SHAPE_AREA" "SHAPE_LEN"
## [13] "Positive" "Total" "zcta_cum.perc_pos"
## [16] "ZIPCODE.y" "num_food_store" "ZIPCODE.x.y"
## [19] "num_hosp" "geometry"task 4: Join the Census ACS population, race, and age data to the NYC Planning Census Tract Data.
nycCensus <- sf::st_read(
"./R-Spatial_II_Lab/2010 Census Tracts/geo_export_1dc7b645-647b-4806-b9a0-7b79660f120a.shp"
)## Reading layer `geo_export_1dc7b645-647b-4806-b9a0-7b79660f120a' from data source `/Users/areeba/Desktop/R/R-spatial/Week8/R-Spatial_II_Lab/2010 Census Tracts/geo_export_1dc7b645-647b-4806-b9a0-7b79660f120a.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 2165 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -74.25559 ymin: 40.49612 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS: WGS84(DD)str(nycCensus)## Classes 'sf' and 'data.frame': 2165 obs. of 12 variables:
## $ boro_code : chr "5" "1" "1" "1" ...
## $ boro_ct201: chr "5000900" "1009800" "1010000" "1010200" ...
## $ boro_name : chr "Staten Island" "Manhattan" "Manhattan" "Manhattan" ...
## $ cdeligibil: chr "E" "I" "I" "I" ...
## $ ct2010 : chr "000900" "009800" "010000" "010200" ...
## $ ctlabel : chr "9" "98" "100" "102" ...
## $ ntacode : chr "SI22" "MN19" "MN19" "MN17" ...
## $ ntaname : chr "West New Brighton-New Brighton-St. George" "Turtle Bay-East Midtown" "Turtle Bay-East Midtown" "Midtown-Midtown South" ...
## $ puma : chr "3903" "3808" "3808" "3807" ...
## $ shape_area: num 2497010 1906016 1860938 1860993 1864600 ...
## $ shape_leng: num 7729 5534 5692 5688 5693 ...
## $ geometry :sfc_MULTIPOLYGON of length 2165; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:28, 1:2] -74.1 -74.1 -74.1 -74.1 -74.1 ...
## ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:11] "boro_code" "boro_ct201" "boro_name" "cdeligibil" ...nycCensus %<>% dplyr::mutate(cntyFIPS = case_when(
boro_name == 'Bronx' ~ '005',
boro_name == 'Brooklyn' ~ '047',
boro_name == 'Manhattan' ~ '061',
boro_name == 'Queens' ~ '081',
boro_name == 'Staten Island' ~ '085'),
tractFIPS = paste(cntyFIPS, ct2010, sep='')
)
acsData <- readLines("./R-Spatial_II_Lab/ACSDP5Y2018.DP05_data_with_overlays_2020-04-22T132935.csv") %>%
magrittr::extract(-2) %>%
textConnection() %>%
read.csv(header=TRUE, quote= "\"") %>%
dplyr::select(GEO_ID,
totPop = DP05_0001E, elderlyPop = DP05_0024E, # >= 65
malePop = DP05_0002E, femalePop = DP05_0003E,
whitePop = DP05_0037E, blackPop = DP05_0038E,
asianPop = DP05_0067E, hispanicPop = DP05_0071E,
adultPop = DP05_0021E, citizenAdult = DP05_0087E) %>%
dplyr::mutate(censusCode = stringr::str_sub(GEO_ID, -9,-1));
acsData %>%
magrittr::extract(1:10,)## GEO_ID totPop elderlyPop malePop femalePop whitePop blackPop
## 1 1400000US36005000100 7080 51 6503 577 1773 4239
## 2 1400000US36005000200 4542 950 2264 2278 2165 1279
## 3 1400000US36005000400 5634 710 2807 2827 2623 1699
## 4 1400000US36005001600 5917 989 2365 3552 2406 2434
## 5 1400000US36005001900 2765 76 1363 1402 585 1041
## 6 1400000US36005002000 9409 977 4119 5290 3185 4487
## 7 1400000US36005002300 4600 648 2175 2425 479 2122
## 8 1400000US36005002400 172 0 121 51 69 89
## 9 1400000US36005002500 5887 548 2958 2929 903 1344
## 10 1400000US36005002701 2868 243 1259 1609 243 987
## asianPop hispanicPop adultPop citizenAdult censusCode
## 1 130 2329 6909 6100 005000100
## 2 119 3367 3582 2952 005000200
## 3 226 3873 4507 4214 005000400
## 4 68 3603 4416 3851 005001600
## 5 130 1413 2008 1787 005001900
## 6 29 5905 6851 6170 005002000
## 7 27 2674 3498 3056 005002300
## 8 14 0 131 42 005002400
## 9 68 4562 4237 2722 005002500
## 10 0 1985 1848 1412 005002701popData <- merge(nycCensus, acsData, by.x ='tractFIPS', by.y = 'censusCode')
sum(popData$totPop)## [1] 8443713names(popData)## [1] "tractFIPS" "boro_code" "boro_ct201" "boro_name" "cdeligibil"
## [6] "ct2010" "ctlabel" "ntacode" "ntaname" "puma"
## [11] "shape_area" "shape_leng" "cntyFIPS" "GEO_ID" "totPop"
## [16] "elderlyPop" "malePop" "femalePop" "whitePop" "blackPop"
## [21] "asianPop" "hispanicPop" "adultPop" "citizenAdult" "geometry"task 5: Aggregate the ACS census data to zip code area data.
popNYC <- sf::st_transform(popData, st_crs(zip_covid_merge))
popData %>% magrittr::extract('elderlyPop') %>% plot(breaks = 'jenks')
str(zip_food_health_by_hospitals)## Classes 'sf' and 'data.frame': 7744 obs. of 20 variables:
## $ ZIPCODE.x.x : num 11436 11436 11436 11436 11436 ...
## $ BLDGZIP : chr "0" "0" "0" "0" ...
## $ PO_NAME : chr "Jamaica" "Jamaica" "Jamaica" "Jamaica" ...
## $ POPULATION : num 18681 18681 18681 18681 18681 ...
## $ AREA : num 22699295 22699295 22699295 22699295 22699295 ...
## $ STATE : chr "NY" "NY" "NY" "NY" ...
## $ COUNTY : chr "Queens" "Queens" "Queens" "Queens" ...
## $ ST_FIPS : chr "36" "36" "36" "36" ...
## $ CTY_FIPS : chr "081" "081" "081" "081" ...
## $ URL : chr "http://www.usps.com/" "http://www.usps.com/" "http://www.usps.com/" "http://www.usps.com/" ...
## $ SHAPE_AREA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SHAPE_LEN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Positive : num 269 269 269 269 269 269 269 269 269 269 ...
## $ Total : num 412 412 412 412 412 412 412 412 412 412 ...
## $ zcta_cum.perc_pos: num 65.3 65.3 65.3 65.3 65.3 ...
## $ ZIPCODE.y : num 11420 11420 11420 11420 11420 ...
## $ num_food_store : int 3 3 3 3 3 3 0 0 0 0 ...
## $ ZIPCODE.x.y : num 11420 11430 11433 11434 11435 ...
## $ num_hosp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ geometry :sfc_POLYGON of length 7744; first list element: List of 1
## ..$ : num [1:159, 1:2] 1038098 1038142 1038171 1038280 1038521 ...
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:19] "ZIPCODE.x.x" "BLDGZIP" "PO_NAME" "POPULATION" ...covidPopZipNYC <- sf::st_join(zip_covid_merge,
popNYC %>% sf::st_centroid(),
join = st_contains) %>%
group_by(ZIPCODE, PO_NAME, POPULATION, COUNTY, Positive, Total) %>%
summarise(totPop = sum(totPop),
malePctg = sum(malePop)/totPop*100,
asianPop = sum(asianPop),
blackPop = sum(blackPop),
hispanicPop = sum(hispanicPop),
whitePop = sum(whitePop)) ## Warning: st_centroid assumes attributes are constant over geometries## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by ZIPCODE, PO_NAME, POPULATION, COUNTY,
## Positive, and Total.
## ℹ Output is grouped by ZIPCODE, PO_NAME, POPULATION, COUNTY, and Positive.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(ZIPCODE, PO_NAME, POPULATION, COUNTY, Positive,
## Total))` for per-operation grouping (`?dplyr::dplyr_by`) instead.covidPopZipNYC %>% head()## Simple feature collection with 6 features and 12 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 971132.6 ymin: 188447.3 xmax: 998309.7 ymax: 230942.5
## Projected CRS: NAD83 / New York Long Island (ftUS)
## # A tibble: 6 × 13
## # Groups: ZIPCODE, PO_NAME, POPULATION, COUNTY, Positive [6]
## ZIPCODE PO_NAME POPULATION COUNTY Positive Total totPop malePctg asianPop
## <dbl> <chr> <dbl> <chr> <dbl> <dbl> <int> <dbl> <int>
## 1 83 Central Park 25 New Y… NA NA 3 0 0
## 2 10001 New York 22413 New Y… 211 448 19146 51.2 4837
## 3 10002 New York 81305 New Y… 539 1024 74310 48.4 32149
## 4 10003 New York 55878 New Y… 279 662 53487 50.3 8027
## 5 10004 New York 2187 New Y… 23 59 NA NA NA
## 6 10005 New York 8107 New Y… 38 116 8809 42.8 1974
## # ℹ 4 more variables: blackPop <int>, hispanicPop <int>, whitePop <int>,
## # geometry <GEOMETRY [US_survey_foot]>sum(covidPopZipNYC$totPop, na.rm = T)## [1] 8395306covidPopZipNYC %>% dplyr::filter(is.na(totPop))## Simple feature collection with 68 features and 12 fields
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: 971132.6 ymin: 151085.5 xmax: 1067494 ymax: 263362
## Projected CRS: NAD83 / New York Long Island (ftUS)
## # A tibble: 68 × 13
## # Groups: ZIPCODE, PO_NAME, POPULATION, COUNTY, Positive [68]
## ZIPCODE PO_NAME POPULATION COUNTY Positive Total totPop malePctg asianPop
## * <dbl> <chr> <dbl> <chr> <dbl> <dbl> <int> <dbl> <int>
## 1 10004 New York 2187 New York 23 59 NA NA NA
## 2 10020 New York 0 New York NA NA NA NA NA
## 3 10041 New York 0 New York NA NA NA NA NA
## 4 10043 New York 0 New York NA NA NA NA NA
## 5 10045 New York 0 New York NA NA NA NA NA
## 6 10047 New York 0 New York NA NA NA NA NA
## 7 10048 New York 0 New York NA NA NA NA NA
## 8 10055 New York 12 New York NA NA NA NA NA
## 9 10075 New York 25203 New York 262 564 NA NA NA
## 10 10080 New York 0 New York NA NA NA NA NA
## # ℹ 58 more rows
## # ℹ 4 more variables: blackPop <int>, hispanicPop <int>, whitePop <int>,
## # geometry <GEOMETRY [US_survey_foot]>plot(covidPopZipNYC["Positive"], breaks='jenks')
plot(covidPopZipNYC["asianPop"], breaks='jenks')