---

title: “R Week 7 Assignment” author: “Genesis Santos date:”3/15/26” output: html_document

R Spatial Lab Assignment # 1

Don’t use a single chunk for the entire assignment. Break it into multiple chunks.

In the following R code chunk, load_packages is the code chunk name. include=FALSE suggests that the code chunk will run, but the code itself and its outputs will not be included in the rendered HTML. echo=TRUE in the following code chunk suggests that the code and results from running the code will be included in the rendered HTML.

task 1: Read and Process NYS health facilities, create sf objects from geographic locations

# You R code comes here

nys_health <- read_csv("NYS_Health_Facility.csv", show_col_types = FALSE)

nys_health <- nys_health %>%
  clean_names()

glimpse(nys_health)

## Rows: 3,990
## Columns: 36
## $ facility_id                  <dbl> 204, 620, 654, 1156, 2589, 3455, 3853, 42…
## $ facility_name                <chr> "Hospice at Lourdes", "Charles T Sitrin H…
## $ short_description            <chr> "HSPC", "NH", "NH", "NH", "NH", "NH", "DT…
## $ description                  <chr> "Hospice", "Residential Health Care Facil…
## $ facility_open_date           <chr> "06/01/1985", "02/01/1989", "02/01/1989",…
## $ facility_address_1           <chr> "4102 Old Vestal Road", "2050 Tilden Aven…
## $ facility_address_2           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ facility_city                <chr> "Vestal", "New Hartford", "Syracuse", "Wa…
## $ facility_state               <chr> "New York", "New York", "New York", "New …
## $ facility_zip_code            <chr> "13850", "13413", "13205", "14569", "1489…
## $ facility_phone_number        <dbl> 6077985692, 3157973114, 3154751641, 58578…
## $ facility_fax_number          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 631465653…
## $ facility_website             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ facility_county_code         <dbl> 3, 32, 33, 60, 2, 14, 29, 14, 29, 7093, 2…
## $ facility_county              <chr> "Broome", "Oneida", "Onondaga", "Wyoming"…
## $ regional_office_id           <dbl> 3, 3, 3, 1, 1, 1, 7, 1, 7, 5, 7, 3, 5, 3,…
## $ regional_office              <chr> "Central New York Regional Office", "Cent…
## $ main_site_name               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "NYU …
## $ main_site_facility_id        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1463,…
## $ operating_certificate_number <chr> "0301501F", "3227304N", "3301326N", "6027…
## $ operator_name                <chr> "Our Lady of Lourdes Memorial Hospital In…
## $ operator_address_1           <chr> "169 Riverside Drive", "Box 1000 Tilden A…
## $ operator_address_2           <chr> NA, NA, NA, NA, NA, NA, NA, NA, "Suite 11…
## $ operator_city                <chr> "Binghamton", "New Hartford", "Syracuse",…
## $ operator_state               <chr> "New York", "New York", "New York", "New …
## $ operator_zip_code            <chr> "13905", "13413", "13205", "14569", "1489…
## $ cooperator_name              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ cooperator_address           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ cooperator_address_2         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ cooperator_city              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ cooperator_state             <chr> "New York", "New York", "New York", "New …
## $ cooperator_zip_code          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ ownership_type               <chr> "Not for Profit Corporation", "Not for Pr…
## $ facility_latitude            <dbl> 42.09710, 43.05497, NA, 42.73898, 42.1264…
## $ facility_longitude           <dbl> -75.97524, -75.22883, NA, -78.12867, -77.…
## $ facility_location            <chr> "(42.097095, -75.975243)", "(43.05497, -7…

summary(nys_health[, c("facility_latitude", "facility_longitude")])

##  facility_latitude facility_longitude
##  Min.   :-75.46    Min.   :-79.63    
##  1st Qu.: 40.77    1st Qu.:-75.91    
##  Median : 41.37    Median :-73.96    
##  Mean   : 41.75    Mean   :-74.77    
##  3rd Qu.: 42.92    3rd Qu.:-73.84    
##  Max.   : 44.98    Max.   : 43.21    
##  NA's   :142       NA's   :142

nys_health_clean <- nys_health %>%
  mutate(
    facility_open_date = mdy(facility_open_date),
    facility_latitude = as.numeric(facility_latitude),
    facility_longitude = as.numeric(facility_longitude)
  ) %>%
  filter(
    !is.na(facility_latitude),
    !is.na(facility_longitude),
    facility_latitude >= -90, facility_latitude <= 90,
    facility_longitude >= -180, facility_longitude <= 180
  )


nys_health_sf <- st_as_sf(
  nys_health_clean,
  coords = c("facility_longitude", "facility_latitude"),
  crs = 4326,           # WGS 84 geographic coordinates
  remove = FALSE
)



plot(st_geometry(nys_health_sf), main = "NYS Health Facilities")

The default chunk option will show the code as well as the output from the code.

Using, eval, echo, and include, we can control whether run the code, show the code, and include the results. See more details about chunk options at https://rmarkdown.rstudio.com/lesson-3.html and https://yihui.org/knitr/options/.

The default option will run the code and show both code and results. In most cases, the default option is good for the assignments.

task 2: Read and process NYS retail food stores data. Create sf objects from geographic coordinates for NYC

The following is a Quarto markdown code chunk (yes, we can also use Quarto in a RMarkdown document). Quarto markdown is different from R markdown in terms of chunk options.

Modern R Markdown (and Quarto) allows for a more organized way to define chunk options using the “hash-pipe” (#|) syntax. This keeps the logic of the code separate from the configuration of the document. See chunk options at Quarto website.

# your R code comes here.

food_stores <- read_csv("NYS_Retail_Food_Stores.csv", show_col_types = FALSE)

food_stores <- food_stores %>%
  clean_names()

glimpse(food_stores)

## Rows: 29,389
## Columns: 15
## $ county             <chr> "Albany", "Albany", "Albany", "Albany", "Albany", "…
## $ license_number     <chr> "733149", "704590", "727909", "720557", "015890", "…
## $ operation_type     <chr> "Store", "Store", "Store", "Store", "Store", "Store…
## $ establishment_type <chr> "A", "JAC", "JAC", "JAC", "A", "JAC", "JAC", "JAC",…
## $ entity_name        <chr> "SPEEDWAY LLC", "1250 SELKIRK INC", "RED-KAP SALES …
## $ dba_name           <chr> "12110", "1250 SELKIRK", "1667 GENERAL STORE", "19 …
## $ street_number      <chr> "719", "1250", "1667", "315", "8", "477", "873", "1…
## $ street_name        <chr> "NEW LOUDON RD", "RTE 9W & 396", "WESTERN AVENUE", …
## $ address_line_2     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ address_line_3     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ city               <chr> "LATHAM", "SELKIRK", "ALBANY", "WATERVLIET", "ALBAN…
## $ state              <chr> "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY", "NY…
## $ zip_code           <dbl> 12110, 12158, 12203, 12189, 12210, 12209, 12110, 12…
## $ square_footage     <dbl> 300, 3000, 2000, 1200, 1800, 0, 0, 200, 0, 2000, 40…
## $ location           <chr> "719 NEW LOUDON RD\nLATHAM, NY 12110\n(42.739618, -…

head(food_stores$location)

## [1] "719 NEW LOUDON RD\nLATHAM, NY 12110\n(42.739618, -73.761949)"  
## [2] "1250 RTE 9 W\nSELKIRK, NY 12158\n(42.547591, -73.8073)"        
## [3] "1667 WESTERN AVENUE\nALBANY, NY 12203\n(42.686553, -73.854665)"
## [4] "315 19TH STREET\nWATERVLIET, NY 12189\n(42.73063, -73.703443)" 
## [5] "8 CENTRAL AVE\nALBANY, NY 12210\n(42.657136, -73.763712)"      
## [6] "477 DELAWARE AVE\nALBANY, NY 12209\n(42.639931, -73.784962)"

food_stores_clean <- food_stores %>%
  mutate(
    county = str_trim(county),
    entity_name = str_squish(entity_name),
    dba_name = str_squish(dba_name),
    city = str_squish(city),
    street_name = str_squish(street_name),
    square_footage = as.numeric(str_remove_all(square_footage, ",")),
    
  
    latitude = str_match(location, "\\(([-0-9.]+),\\s*([-0-9.]+)\\)")[,2],
    longitude = str_match(location, "\\(([-0-9.]+),\\s*([-0-9.]+)\\)")[,3],
    
    latitude = as.numeric(latitude),
    longitude = as.numeric(longitude)
  ) %>%
  filter(
    county %in% c("Bronx", "Kings", "New York", "Queens", "Richmond"),
    !is.na(latitude),
    !is.na(longitude)
  )

nyc_food_sf <- st_as_sf(
  food_stores_clean,
  coords = c("longitude", "latitude"),
  crs = 4326,
  remove = FALSE
)

print(nyc_food_sf)

## Simple feature collection with 11301 features and 17 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -74.2484 ymin: 40.50782 xmax: -73.67061 ymax: 40.91008
## Geodetic CRS:  WGS 84
## # A tibble: 11,301 × 18
##    county license_number operation_type establishment_type entity_name  dba_name
##  * <chr>  <chr>          <chr>          <chr>              <chr>        <chr>   
##  1 Bronx  734149         Store          JAC                7 ELEVEN FO… <NA>    
##  2 Bronx  606221         Store          JAC                1001 SAN MI… 1001 SA…
##  3 Bronx  606228         Store          JAC                1029 FOOD P… 1029 FO…
##  4 Bronx  723375         Store          JAC                1078 DELI G… 1078 DE…
##  5 Bronx  724807         Store          JAC                1086 LUNA D… 1086 LU…
##  6 Bronx  712943         Store          JAC                109 AJ DELI… 109 AJ …
##  7 Bronx  703060         Store          JAC                10 NEIGHBOR… 10 NEIG…
##  8 Bronx  609065         Store          JAC                1105 TINTON… 1105 TI…
##  9 Bronx  722972         Store          A                  1150 WEBSTE… 1150 WE…
## 10 Bronx  609621         Store          JAC                1158 GROCER… 1158 GR…
## # ℹ 11,291 more rows
## # ℹ 12 more variables: street_number <chr>, street_name <chr>,
## #   address_line_2 <lgl>, address_line_3 <lgl>, city <chr>, state <chr>,
## #   zip_code <dbl>, square_footage <dbl>, location <chr>, latitude <dbl>,
## #   longitude <dbl>, geometry <POINT [°]>

st_crs(nyc_food_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]]

head(nyc_food_sf)

## Simple feature collection with 6 features and 17 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -73.91961 ymin: 40.8251 xmax: -73.83188 ymax: 40.86916
## Geodetic CRS:  WGS 84
## # A tibble: 6 × 18
##   county license_number operation_type establishment_type entity_name   dba_name
##   <chr>  <chr>          <chr>          <chr>              <chr>         <chr>   
## 1 Bronx  734149         Store          JAC                7 ELEVEN FOO… <NA>    
## 2 Bronx  606221         Store          JAC                1001 SAN MIG… 1001 SA…
## 3 Bronx  606228         Store          JAC                1029 FOOD PL… 1029 FO…
## 4 Bronx  723375         Store          JAC                1078 DELI GR… 1078 DE…
## 5 Bronx  724807         Store          JAC                1086 LUNA DE… 1086 LU…
## 6 Bronx  712943         Store          JAC                109 AJ DELI … 109 AJ …
## # ℹ 12 more variables: street_number <chr>, street_name <chr>,
## #   address_line_2 <lgl>, address_line_3 <lgl>, city <chr>, state <chr>,
## #   zip_code <dbl>, square_footage <dbl>, location <chr>, latitude <dbl>,
## #   longitude <dbl>, geometry <POINT [°]>

plot(st_geometry(nyc_food_sf), main = "NYC Retail Food Stores")

Task 3: Spatial Data Exploration (Example)

Use this section to begin your spatial analysis.

# Example: Create and map a simple spatial point
st_point(c(-73.96, 40.77)) %>% 
  st_sfc(crs = 4326) %>%
  mapview()

nys_health_sf <- st_transform(nys_health_sf, 4326)
nyc_food_sf <- st_transform(nyc_food_sf, 4326)

mapview(nys_health_sf, layer.name = "NYS Health Facilities")

mapview(nyc_food_sf, layer.name = "NYC Retail Food Stores")