GTECH78520_R_Spatial_Assignment2
Elva Gao
2025-03-28
R Spatial Lab Assignment # 2
task 1
Load data (zip code and food stores)
zip_codes_sf <- st_read("/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial/Data/R-spatial_1_Lab/ZIP_CODE_040114/ZIP_CODE_040114.shp")## Reading layer `ZIP_CODE_040114' from data source
## `/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial/Data/R-Spatial_1_Lab/ZIP_CODE_040114/ZIP_CODE_040114.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 263 features and 12 fields
## 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)food_stores_sf <- st_read('/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-spatial_ll_Lab/nycFoodStore.shp')## Reading layer `nycFoodStore' from data source
## `/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-Spatial_ll_Lab/nycFoodStore.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 11300 features and 16 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -74.2484 ymin: 40.50782 xmax: -73.67061 ymax: 40.91008
## Geodetic CRS: WGS 84census_tract <- st_read("/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-spatial_ll_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/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-Spatial_ll_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)task 2
Load health facility data and turn to sf, have the CRS be 2263
health_facilities_csv <- read.csv("/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-spatial_ll_Lab/NYS_Health_Facility.csv")
health_facilities_csv <- health_facilities_csv %>% filter(between(Facility.Longitude, -79.8, -71.8) & between(Facility.Latitude, 40.0, 45.5))
health_facilities_sf <- st_as_sf(health_facilities_csv, coords = c("Facility.Longitude", "Facility.Latitude"), crs = 2263)task 3
Load covid data and clean it
covid_data <- read.csv("/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-spatial_ll_Lab/tests-by-zcta_2021_04_23.csv")
covid_data_clean <- covid_data %>% dplyr::select(MODIFIED_ZCTA, COVID_CASE_COUNT, COVID_CASE_RATE, PERCENT_POSITIVE, TOTAL_COVID_TESTS) %>% group_by(MODIFIED_ZCTA) %>% summarize(COVID_CASE_COUNT = sum(COVID_CASE_COUNT, na.rm = TRUE), COVID_CASE_RATE = mean(COVID_CASE_RATE, na.rm = TRUE), PERCENT_POSITIVE = mean(PERCENT_POSITIVE, na.rm = TRUE), TOTAL_COVID_TESTS = sum(TOTAL_COVID_TESTS, na.rm = TRUE))task 4
Clean covid data and join with zip code
covid_data_clean$MODIFIED_ZCTA <- as.character(covid_data_clean$MODIFIED_ZCTA)
zip_codes_sf <- zip_codes_sf %>%
left_join(covid_data_clean, by = c("ZIPCODE" = "MODIFIED_ZCTA"))
colnames(zip_codes_sf)## [1] "ZIPCODE" "BLDGZIP" "PO_NAME"
## [4] "POPULATION" "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS" "CTY_FIPS"
## [10] "URL" "SHAPE_AREA" "SHAPE_LEN"
## [13] "COVID_CASE_COUNT" "COVID_CASE_RATE" "PERCENT_POSITIVE"
## [16] "TOTAL_COVID_TESTS" "geometry"zip_codes_sf <- zip_codes_sf %>% left_join(covid_data_clean, by = c("ZIPCODE" = "MODIFIED_ZCTA"))task 5
Filter food stores
food_stores_filtered <- food_stores_sf %>%
filter(Oprtn_T == "Store")task 6
Check the CRS for these 3
zip_codes_sf <- st_transform(zip_codes_sf, crs = 2263)
food_stores_sf <- st_transform(food_stores_sf, crs = 2263)
health_facilities_sf <- st_transform(health_facilities_sf, crs = 2263)task 7
Check the column names and join food store with zip code
colnames(food_stores_sf)## [1] "ï__Cnty" "Lcns_Nm" "Oprtn_T" "Estbl_T" "Entty_N" "DBA_Nam"
## [7] "Strt_Nmb" "Stret_Nm" "Add_L_2" "Add_L_3" "City" "State"
## [13] "Zip_Cod" "Sqr_Ftg" "Locatin" "Coords" "geometry"food_stores_by_zip <- food_stores_sf %>% st_drop_geometry() %>% group_by(Zip_Cod) %>% summarize(store_count = n())task 8
Mutate
food_stores_by_zip <- food_stores_by_zip %>% mutate(Zip_Cod = as.character(Zip_Cod))task 9
View mapview of food stores with zip code
zip_covid_data_sf <- zip_codes_sf %>% left_join(covid_data_clean, by = c("ZIPCODE" = "MODIFIED_ZCTA"))
mapview(zip_covid_data_sf, zcol = "COVID_CASE_COUNT", legend = TRUE)task 10
Read ACS data
acs_data <- read.csv("/Users/elvagao/Desktop/Rstudio_GTECH/R-spatial_2/R-spatial_ll_Lab/ACSDP5Y2018.DP05_data_with_overlays_2020-04-22T132935.csv")task 13
Clean ACS data
acs_data_cleaned <- acs_data[, c("GEO_ID", "NAME", "DP05_0033E")]
colnames(acs_data_cleaned) <- c("GEO_ID", "Geographic_Area", "Total_Population")
head(acs_data_cleaned)## GEO_ID Geographic_Area
## 1 id Geographic Area Name
## 2 1400000US36005000100 Census Tract 1, Bronx County, New York
## 3 1400000US36005000200 Census Tract 2, Bronx County, New York
## 4 1400000US36005000400 Census Tract 4, Bronx County, New York
## 5 1400000US36005001600 Census Tract 16, Bronx County, New York
## 6 1400000US36005001900 Census Tract 19, Bronx County, New York
## Total_Population
## 1 Estimate!!RACE!!Total population
## 2 7080
## 3 4542
## 4 5634
## 5 5917
## 6 2765task 14
See column names for 2010 census tract
colnames(census_tract)## [1] "boro_code" "boro_ct201" "boro_name" "cdeligibil" "ct2010"
## [6] "ctlabel" "ntacode" "ntaname" "puma" "shape_area"
## [11] "shape_leng" "geometry"task 15
Clean ACS data and look at similar thing for zip code and ACS, if none, create one.
acs_data_cleaned$GEO_ID <- as.character(acs_data_cleaned$GEO_ID)
zip_codes_sf$ZIPCODE <- as.character(zip_codes_sf$ZIPCODE)task 16
Join ACS and zip code data
zip_acs_sf <- zip_codes_sf %>% left_join(acs_data_cleaned, by = c("ZIPCODE" = "GEO_ID"))
head(zip_acs_sf)## Simple feature collection with 6 features and 22 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 986490.1 ymin: 168910.5 xmax: 1043042 ymax: 189382.9
## Projected CRS: NAD83 / New York Long Island (ftUS)
## ZIPCODE BLDGZIP PO_NAME POPULATION AREA STATE COUNTY ST_FIPS CTY_FIPS
## 1 11436 0 Jamaica 18681 22699295 NY Queens 36 081
## 2 11213 0 Brooklyn 62426 29631004 NY Kings 36 047
## 3 11212 0 Brooklyn 83866 41972104 NY Kings 36 047
## 4 11225 0 Brooklyn 56527 23698630 NY Kings 36 047
## 5 11218 0 Brooklyn 72280 36868799 NY Kings 36 047
## 6 11226 0 Brooklyn 106132 39408598 NY Kings 36 047
## URL SHAPE_AREA SHAPE_LEN COVID_CASE_COUNT.x
## 1 http://www.usps.com/ 0 0 1888
## 2 http://www.usps.com/ 0 0 5166
## 3 http://www.usps.com/ 0 0 7182
## 4 http://www.usps.com/ 0 0 3833
## 5 http://www.usps.com/ 0 0 6199
## 6 http://www.usps.com/ 0 0 7279
## COVID_CASE_RATE.x PERCENT_POSITIVE.x TOTAL_COVID_TESTS.x COVID_CASE_COUNT.y
## 1 9419.96 17.57 11082 1888
## 2 7996.75 13.72 38560 5166
## 3 9709.74 15.64 47319 7182
## 4 6664.50 11.62 33709 3833
## 5 8377.49 13.93 45884 6199
## 6 7476.75 13.33 56287 7279
## COVID_CASE_RATE.y PERCENT_POSITIVE.y TOTAL_COVID_TESTS.y Geographic_Area
## 1 9419.96 17.57 11082 <NA>
## 2 7996.75 13.72 38560 <NA>
## 3 9709.74 15.64 47319 <NA>
## 4 6664.50 11.62 33709 <NA>
## 5 8377.49 13.93 45884 <NA>
## 6 7476.75 13.33 56287 <NA>
## Total_Population geometry
## 1 <NA> POLYGON ((1038098 188138.4,...
## 2 <NA> POLYGON ((1001614 186926.4,...
## 3 <NA> POLYGON ((1011174 183696.3,...
## 4 <NA> POLYGON ((995908.4 183617.6...
## 5 <NA> POLYGON ((991997.1 176307.5...
## 6 <NA> POLYGON ((994821.5 177865.7...task 17
Check for NA
zip_acs_sf %>% filter(is.na(Geographic_Area) | is.na(Total_Population))## Simple feature collection with 263 features and 22 fields
## 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 BLDGZIP PO_NAME POPULATION AREA STATE COUNTY ST_FIPS CTY_FIPS
## 1 11436 0 Jamaica 18681 22699295 NY Queens 36 081
## 2 11213 0 Brooklyn 62426 29631004 NY Kings 36 047
## 3 11212 0 Brooklyn 83866 41972104 NY Kings 36 047
## 4 11225 0 Brooklyn 56527 23698630 NY Kings 36 047
## 5 11218 0 Brooklyn 72280 36868799 NY Kings 36 047
## 6 11226 0 Brooklyn 106132 39408598 NY Kings 36 047
## 7 11219 0 Brooklyn 92561 42002738 NY Kings 36 047
## 8 11210 0 Brooklyn 67067 47887023 NY Kings 36 047
## 9 11230 0 Brooklyn 80857 49926703 NY Kings 36 047
## 10 11204 0 Brooklyn 77354 43555185 NY Kings 36 047
## URL SHAPE_AREA SHAPE_LEN COVID_CASE_COUNT.x
## 1 http://www.usps.com/ 0 0 1888
## 2 http://www.usps.com/ 0 0 5166
## 3 http://www.usps.com/ 0 0 7182
## 4 http://www.usps.com/ 0 0 3833
## 5 http://www.usps.com/ 0 0 6199
## 6 http://www.usps.com/ 0 0 7279
## 7 http://www.usps.com/ 0 0 8429
## 8 http://www.usps.com/ 0 0 5380
## 9 http://www.usps.com/ 0 0 11044
## 10 http://www.usps.com/ 0 0 7331
## COVID_CASE_RATE.x PERCENT_POSITIVE.x TOTAL_COVID_TESTS.x COVID_CASE_COUNT.y
## 1 9419.96 17.57 11082 1888
## 2 7996.75 13.72 38560 5166
## 3 9709.74 15.64 47319 7182
## 4 6664.50 11.62 33709 3833
## 5 8377.49 13.93 45884 6199
## 6 7476.75 13.33 56287 7279
## 7 9356.97 15.64 55444 8429
## 8 8190.36 15.60 35070 5380
## 9 12424.46 19.11 59585 11044
## 10 9509.97 17.29 43449 7331
## COVID_CASE_RATE.y PERCENT_POSITIVE.y TOTAL_COVID_TESTS.y Geographic_Area
## 1 9419.96 17.57 11082 <NA>
## 2 7996.75 13.72 38560 <NA>
## 3 9709.74 15.64 47319 <NA>
## 4 6664.50 11.62 33709 <NA>
## 5 8377.49 13.93 45884 <NA>
## 6 7476.75 13.33 56287 <NA>
## 7 9356.97 15.64 55444 <NA>
## 8 8190.36 15.60 35070 <NA>
## 9 12424.46 19.11 59585 <NA>
## 10 9509.97 17.29 43449 <NA>
## Total_Population geometry
## 1 <NA> POLYGON ((1038098 188138.4,...
## 2 <NA> POLYGON ((1001614 186926.4,...
## 3 <NA> POLYGON ((1011174 183696.3,...
## 4 <NA> POLYGON ((995908.4 183617.6...
## 5 <NA> POLYGON ((991997.1 176307.5...
## 6 <NA> POLYGON ((994821.5 177865.7...
## 7 <NA> POLYGON ((987286.4 173946.5...
## 8 <NA> POLYGON ((995796 171110.1, ...
## 9 <NA> POLYGON ((994099.3 171240.7...
## 10 <NA> POLYGON ((989500.2 170730.2...task 18
The sum of the NA
sum(is.na(zip_acs_sf$Total_Population))## [1] 263task 19
Clean ACS with zip code for Total population
zip_acs_sf_cleaned <- zip_acs_sf[!is.na(zip_acs_sf$Total_Population), ]task 20
How is it class
class(zip_acs_sf$Total_Population)## [1] "character"task 21
Zip code with ACS for total population as a numerically value
zip_acs_sf$Total_Population <- as.numeric(zip_acs_sf$Total_Population)task 22
The sum of the total population having data with zip code
zip_acs_sf$Total_Population <- as.numeric(zip_acs_sf$Total_Population)
sum(is.na(zip_acs_sf$Total_Population))## [1] 263task 24
See the summary of zip code with acs
summary(zip_acs_sf$Total_Population)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## NA NA NA NaN NA NA 263table(is.na(zip_acs_sf$Total_Population))##
## TRUE
## 263task 25
See again the column names and head
colnames(zip_acs_sf)## [1] "ZIPCODE" "BLDGZIP" "PO_NAME"
## [4] "POPULATION" "AREA" "STATE"
## [7] "COUNTY" "ST_FIPS" "CTY_FIPS"
## [10] "URL" "SHAPE_AREA" "SHAPE_LEN"
## [13] "COVID_CASE_COUNT.x" "COVID_CASE_RATE.x" "PERCENT_POSITIVE.x"
## [16] "TOTAL_COVID_TESTS.x" "COVID_CASE_COUNT.y" "COVID_CASE_RATE.y"
## [19] "PERCENT_POSITIVE.y" "TOTAL_COVID_TESTS.y" "Geographic_Area"
## [22] "Total_Population" "geometry"head(zip_acs_sf)## Simple feature collection with 6 features and 22 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 986490.1 ymin: 168910.5 xmax: 1043042 ymax: 189382.9
## Projected CRS: NAD83 / New York Long Island (ftUS)
## ZIPCODE BLDGZIP PO_NAME POPULATION AREA STATE COUNTY ST_FIPS CTY_FIPS
## 1 11436 0 Jamaica 18681 22699295 NY Queens 36 081
## 2 11213 0 Brooklyn 62426 29631004 NY Kings 36 047
## 3 11212 0 Brooklyn 83866 41972104 NY Kings 36 047
## 4 11225 0 Brooklyn 56527 23698630 NY Kings 36 047
## 5 11218 0 Brooklyn 72280 36868799 NY Kings 36 047
## 6 11226 0 Brooklyn 106132 39408598 NY Kings 36 047
## URL SHAPE_AREA SHAPE_LEN COVID_CASE_COUNT.x
## 1 http://www.usps.com/ 0 0 1888
## 2 http://www.usps.com/ 0 0 5166
## 3 http://www.usps.com/ 0 0 7182
## 4 http://www.usps.com/ 0 0 3833
## 5 http://www.usps.com/ 0 0 6199
## 6 http://www.usps.com/ 0 0 7279
## COVID_CASE_RATE.x PERCENT_POSITIVE.x TOTAL_COVID_TESTS.x COVID_CASE_COUNT.y
## 1 9419.96 17.57 11082 1888
## 2 7996.75 13.72 38560 5166
## 3 9709.74 15.64 47319 7182
## 4 6664.50 11.62 33709 3833
## 5 8377.49 13.93 45884 6199
## 6 7476.75 13.33 56287 7279
## COVID_CASE_RATE.y PERCENT_POSITIVE.y TOTAL_COVID_TESTS.y Geographic_Area
## 1 9419.96 17.57 11082 <NA>
## 2 7996.75 13.72 38560 <NA>
## 3 9709.74 15.64 47319 <NA>
## 4 6664.50 11.62 33709 <NA>
## 5 8377.49 13.93 45884 <NA>
## 6 7476.75 13.33 56287 <NA>
## Total_Population geometry
## 1 NA POLYGON ((1038098 188138.4,...
## 2 NA POLYGON ((1001614 186926.4,...
## 3 NA POLYGON ((1011174 183696.3,...
## 4 NA POLYGON ((995908.4 183617.6...
## 5 NA POLYGON ((991997.1 176307.5...
## 6 NA POLYGON ((994821.5 177865.7...task 26
Rename ‘POPULATION’ to ‘Total_Population’
zip_acs_sf$Total_Population <- zip_acs_sf$POPULATIONtask 27
Clean zip code with ACS
zip_acs_sf_cleaned <- zip_acs_sf[!is.na(zip_acs_sf$Total_Population), ]task 28
The header of the new clean version
head(zip_acs_sf_cleaned)## Simple feature collection with 6 features and 22 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: 986490.1 ymin: 168910.5 xmax: 1043042 ymax: 189382.9
## Projected CRS: NAD83 / New York Long Island (ftUS)
## ZIPCODE BLDGZIP PO_NAME POPULATION AREA STATE COUNTY ST_FIPS CTY_FIPS
## 1 11436 0 Jamaica 18681 22699295 NY Queens 36 081
## 2 11213 0 Brooklyn 62426 29631004 NY Kings 36 047
## 3 11212 0 Brooklyn 83866 41972104 NY Kings 36 047
## 4 11225 0 Brooklyn 56527 23698630 NY Kings 36 047
## 5 11218 0 Brooklyn 72280 36868799 NY Kings 36 047
## 6 11226 0 Brooklyn 106132 39408598 NY Kings 36 047
## URL SHAPE_AREA SHAPE_LEN COVID_CASE_COUNT.x
## 1 http://www.usps.com/ 0 0 1888
## 2 http://www.usps.com/ 0 0 5166
## 3 http://www.usps.com/ 0 0 7182
## 4 http://www.usps.com/ 0 0 3833
## 5 http://www.usps.com/ 0 0 6199
## 6 http://www.usps.com/ 0 0 7279
## COVID_CASE_RATE.x PERCENT_POSITIVE.x TOTAL_COVID_TESTS.x COVID_CASE_COUNT.y
## 1 9419.96 17.57 11082 1888
## 2 7996.75 13.72 38560 5166
## 3 9709.74 15.64 47319 7182
## 4 6664.50 11.62 33709 3833
## 5 8377.49 13.93 45884 6199
## 6 7476.75 13.33 56287 7279
## COVID_CASE_RATE.y PERCENT_POSITIVE.y TOTAL_COVID_TESTS.y Geographic_Area
## 1 9419.96 17.57 11082 <NA>
## 2 7996.75 13.72 38560 <NA>
## 3 9709.74 15.64 47319 <NA>
## 4 6664.50 11.62 33709 <NA>
## 5 8377.49 13.93 45884 <NA>
## 6 7476.75 13.33 56287 <NA>
## Total_Population geometry
## 1 18681 POLYGON ((1038098 188138.4,...
## 2 62426 POLYGON ((1001614 186926.4,...
## 3 83866 POLYGON ((1011174 183696.3,...
## 4 56527 POLYGON ((995908.4 183617.6...
## 5 72280 POLYGON ((991997.1 176307.5...
## 6 106132 POLYGON ((994821.5 177865.7...task 29
Aggregating COVID-19 data (assuming ‘zip_acs_sf_cleaned’ contains the necessary columns)
zip_covid_agg <- zip_acs_sf_cleaned %>% group_by(ZIPCODE) %>% summarise(total_cases = sum(COVID_CASE_COUNT.x, na.rm = TRUE), total_tests = sum(TOTAL_COVID_TESTS.x, na.rm = TRUE), case_rate = mean(COVID_CASE_RATE.x, na.rm = TRUE), percent_positive = mean(PERCENT_POSITIVE.x, na.rm = TRUE))task 30
Join with zip code aggregated and clean
zip_acs_sf_cleaned <- st_join(zip_acs_sf_cleaned, zip_covid_agg, by = "ZIPCODE")task 31
The clean zip with ACs with dropped NA values
zip_acs_sf_cleaned <- drop_na(zip_acs_sf_cleaned, Total_Population)task 32
summary(zip_acs_sf_cleaned)## ZIPCODE.x BLDGZIP PO_NAME POPULATION
## Length:1240 Length:1240 Length:1240 Min. : 0
## Class :character Class :character Class :character 1st Qu.: 17513
## Mode :character Mode :character Mode :character Median : 37743
## Mean : 40557
## 3rd Qu.: 62075
## Max. :109069
##
## AREA STATE COUNTY ST_FIPS
## Min. : 3155 Length:1240 Length:1240 Length:1240
## 1st Qu.: 11395111 Class :character Class :character Class :character
## Median : 29593164 Mode :character Mode :character Mode :character
## Mean : 40086886
## 3rd Qu.: 53463275
## Max. :473985727
##
## CTY_FIPS URL SHAPE_AREA SHAPE_LEN
## Length:1240 Length:1240 Min. :0 Min. :0
## Class :character Class :character 1st Qu.:0 1st Qu.:0
## Mode :character Mode :character Median :0 Median :0
## Mean :0 Mean :0
## 3rd Qu.:0 3rd Qu.:0
## Max. :0 Max. :0
##
## COVID_CASE_COUNT.x COVID_CASE_RATE.x PERCENT_POSITIVE.x TOTAL_COVID_TESTS.x
## Min. : 164 Min. : 3413 Min. : 5.50 Min. : 2441
## 1st Qu.: 1917 1st Qu.: 6308 1st Qu.: 9.67 1st Qu.:18417
## Median : 3469 Median : 8500 Median :14.93 Median :28077
## Mean : 4260 Mean : 8332 Mean :13.62 Mean :30998
## 3rd Qu.: 6199 3rd Qu.:10096 3rd Qu.:16.87 3rd Qu.:43954
## Max. :11581 Max. :16212 Max. :21.10 Max. :72559
## NA's :195 NA's :195 NA's :195 NA's :195
## COVID_CASE_COUNT.y COVID_CASE_RATE.y PERCENT_POSITIVE.y TOTAL_COVID_TESTS.y
## Min. : 164 Min. : 3413 Min. : 5.50 Min. : 2441
## 1st Qu.: 1917 1st Qu.: 6308 1st Qu.: 9.67 1st Qu.:18417
## Median : 3469 Median : 8500 Median :14.93 Median :28077
## Mean : 4260 Mean : 8332 Mean :13.62 Mean :30998
## 3rd Qu.: 6199 3rd Qu.:10096 3rd Qu.:16.87 3rd Qu.:43954
## Max. :11581 Max. :16212 Max. :21.10 Max. :72559
## NA's :195 NA's :195 NA's :195 NA's :195
## Geographic_Area Total_Population ZIPCODE.y total_cases
## Length:1240 Min. : 0 Length:1240 Min. : 0
## Class :character 1st Qu.: 17513 Class :character 1st Qu.: 1314
## Mode :character Median : 37743 Mode :character Median : 2971
## Mean : 40557 Mean : 3690
## 3rd Qu.: 62075 3rd Qu.: 5707
## Max. :109069 Max. :12746
##
## total_tests case_rate percent_positive geometry
## Min. : 0 Min. : 3413 Min. : 5.50 POLYGON :1240
## 1st Qu.:12107 1st Qu.: 6308 1st Qu.: 9.67 epsg:2263 : 0
## Median :24610 Median : 8500 Median :14.93 +proj=lcc ...: 0
## Mean :26898 Mean : 8329 Mean :13.61
## 3rd Qu.:41474 3rd Qu.:10096 3rd Qu.:16.87
## Max. :91518 Max. :16212 Max. :21.10
## NA's :193 NA's :193task 33
Plot of covid in ZIP CODE
ggplot(zip_acs_sf_cleaned) + geom_sf(aes(fill = COVID_CASE_RATE.x)) + scale_fill_viridis_c() + theme_minimal() + labs(title = "COVID-19 Case Rate by ZIP Code")