R Week 8 Assignment
Onel Galagedara
04/01/2025
R Spatial Lab Assignment # 1
task 1:
setwd("C:/Users/ogalagedara/Documents/week 8 p2")
coviddata <- read.csv('tests-by-zcta_2021_04_23.csv')
covidshapefile <- st_as_sf(coviddata,coords = c('lon','lat'), crs=4326)
nyc_zips <- st_read("C:/Users/ogalagedara/Documents/week 8 p2/ZIP_CODE_040114.shp")## Reading layer `ZIP_CODE_040114' from data source
## `C:\Users\ogalagedara\Documents\week 8 p2\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)zipcodes_sf <- st_transform(nyc_zips,4326)
zipcodesmerged <- st_join(zipcodes_sf,covidshapefile)
plot(zipcodesmerged['COVID_CASE_COUNT'])
task 2:
setwd("C:/Users/ogalagedara/Documents/week 8 p2")
nyc_foods <- st_read('nycFoodStore.shp')## Reading layer `nycFoodStore' from data source
## `C:\Users\ogalagedara\Documents\week 8 p2\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 84nycfoodsshp <- st_transform(nyc_foods,4326)
zipcodesfinal <-zipcodesmerged %>% st_join(nycfoodsshp)%>%group_by(geometry)%>%mutate(n_food_stores=n())
plot(zipcodesfinal["n_food_stores"])
task 3
setwd("C:/Users/ogalagedara/Documents/week 8 p2")
nychealth<-read_csv("NYS_Health_Facility.csv")## Rows: 3990 Columns: 36
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (28): Facility Name, Short Description, Description, Facility Open Date,...
## dbl (8): Facility ID, Facility Phone Number, Facility Fax Number, Facility ...
##
## 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.missing_long <- is.na(nychealth$`Facility Longitude`)
missing_lat <- is.na(nychealth$`Facility Latitude`)
missing_coords <- missing_long | missing_lat
nychealth_clean <- nychealth[!missing_coords, ]
nychealthsf <- st_as_sf(nychealth_clean, coords = c("Facility Longitude", "Facility Latitude"), crs = 4326)
zipcode_health<-zipcodesmerged %>%
st_join(nychealthsf) %>%
group_by(geometry)%>%
mutate(n_health_fac=n())
zipcode_health<-zipcode_health %>%
filter(Description == "Hospital") %>%
group_by(geometry)%>%
mutate(n_hospital_fac=n())
zipcode_health <- zipcode_health %>%
rename("Number of Hospitals" = "n_hospital_fac")
plot(zipcode_health['Number of Hospitals'])
Task 4
setwd("C:/Users/ogalagedara/Documents/week 8 p2")
census_trts <- st_read('geo_export_1dc7b645-647b-4806-b9a0-7b79660f120a.shp')## Reading layer `geo_export_1dc7b645-647b-4806-b9a0-7b79660f120a' from data source `C:\Users\ogalagedara\Documents\week 8 p2\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)census_data_acs <- read.csv('ACSDP5Y2018.DP05_data_with_overlays_2020-04-22T132935.csv')
require(dplyr);
ct <- census_trts %<>% dplyr::mutate(cntyFIPS = case_when(
boro_name == 'Bronx' ~ '005',
boro_name == 'Brooklyn' ~ '047',
boro_name == 'Queens' ~ '081',
boro_name == 'Manhattan' ~ '061',
boro_name == 'Staten Island' ~ '085'), tractsFIPS = paste(cntyFIPS, ct2010, sep='')
)
ACS_cleaned <- census_data_acs %<>% 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) %>%
mutate(censusCode = str_sub(GEO_ID,-9,-1))
ACS_cleaned %>% magrittr::extract(1:10,)## GEO_ID totPop
## 1 id Estimate!!SEX AND AGE!!Total population
## 2 1400000US36005000100 7080
## 3 1400000US36005000200 4542
## 4 1400000US36005000400 5634
## 5 1400000US36005001600 5917
## 6 1400000US36005001900 2765
## 7 1400000US36005002000 9409
## 8 1400000US36005002300 4600
## 9 1400000US36005002400 172
## 10 1400000US36005002500 5887
## elderlyPop
## 1 Estimate!!SEX AND AGE!!Total population!!65 years and over
## 2 51
## 3 950
## 4 710
## 5 989
## 6 76
## 7 977
## 8 648
## 9 0
## 10 548
## malePop
## 1 Estimate!!SEX AND AGE!!Total population!!Male
## 2 6503
## 3 2264
## 4 2807
## 5 2365
## 6 1363
## 7 4119
## 8 2175
## 9 121
## 10 2958
## femalePop
## 1 Estimate!!SEX AND AGE!!Total population!!Female
## 2 577
## 3 2278
## 4 2827
## 5 3552
## 6 1402
## 7 5290
## 8 2425
## 9 51
## 10 2929
## whitePop
## 1 Estimate!!RACE!!Total population!!One race!!White
## 2 1773
## 3 2165
## 4 2623
## 5 2406
## 6 585
## 7 3185
## 8 479
## 9 69
## 10 903
## blackPop
## 1 Estimate!!RACE!!Total population!!One race!!Black or African American
## 2 4239
## 3 1279
## 4 1699
## 5 2434
## 6 1041
## 7 4487
## 8 2122
## 9 89
## 10 1344
## asianPop
## 1 Estimate!!Race alone or in combination with one or more other races!!Total population!!Asian
## 2 130
## 3 119
## 4 226
## 5 68
## 6 130
## 7 29
## 8 27
## 9 14
## 10 68
## hispanicPop
## 1 Estimate!!HISPANIC OR LATINO AND RACE!!Total population!!Hispanic or Latino (of any race)
## 2 2329
## 3 3367
## 4 3873
## 5 3603
## 6 1413
## 7 5905
## 8 2674
## 9 0
## 10 4562
## adultPop
## 1 Estimate!!SEX AND AGE!!Total population!!18 years and over
## 2 6909
## 3 3582
## 4 4507
## 5 4416
## 6 2008
## 7 6851
## 8 3498
## 9 131
## 10 4237
## citizenAdult
## 1 Estimate!!CITIZEN, VOTING AGE POPULATION!!Citizen, 18 and over population
## 2 6100
## 3 2952
## 4 4214
## 5 3851
## 6 1787
## 7 6170
## 8 3056
## 9 42
## 10 2722
## censusCode
## 1 id
## 2 005000100
## 3 005000200
## 4 005000400
## 5 005001600
## 6 005001900
## 7 005002000
## 8 005002300
## 9 005002400
## 10 005002500populationplot <- merge(ct,ACS_cleaned,by.x = 'tractsFIPS', by.y = 'censusCode')
plot(populationplot['blackPop'])
Task 5
setwd("C:/Users/ogalagedara/Documents/week 8 p2")
totalpop <- st_transform(populationplot, 4326)
ACS_cleaned <- zipcodes_sf %>% st_join(totalpop) %>% group_by(ZIPCODE)
plot(ACS_cleaned['totPop'])
