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R Spatial Lab Assignment # 3

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

task 1:

nyc_covid_data_testing <- read_csv("C:/Users/dwvil/Documents/SPRING 2025/R LANGUAGE/R-Spatial/R-Spatial/Data/Section_08/R-Spatial_II_Lab/R-Spatial_II_Lab/tests-by-zcta_2020_04_12.csv")
## 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.
nyc_postal <- st_read("C:/Users/dwvil/Documents/SPRING 2025/R LANGUAGE/R-Spatial/R-Spatial/Data/R-Spatial_I_Lab/ZIP_CODE_040114/ZIP_CODE_040114.shp")
## Reading layer `ZIP_CODE_040114' from data source 
##   `C:\Users\dwvil\Documents\SPRING 2025\R LANGUAGE\R-Spatial\R-Spatial\Data\R-Spatial_I_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)
NYS_Health_Facility <- read_csv("C:/Users/dwvil/Documents/SPRING 2025/R LANGUAGE/R-Spatial/R-Spatial/Data/R-Spatial_I_Lab/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.
NYS_Retail_Food_Stores <- read_csv(
  "C:/Users/dwvil/Documents/SPRING 2025/R LANGUAGE/R-Spatial/R-Spatial/Data/R-Spatial_I_Lab/nys_retail_food_Store_xy.csv", 
  locale = locale(encoding = "Latin1")
)
## Rows: 29389 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): ï..County, Operation.Type, Establishment.Type, Entity.Name, DBA.Na...
## dbl  (4): License.Number, Zip.Code, Y, X
## num  (1): Square.Footage
## lgl  (2): Address.Line.2, Address.Line.3
## 
## 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.
nyc_covid_data_testing <- nyc_covid_data_testing %>%
  mutate(MODZCTA = as.character(MODZCTA))

nyc_postal_Joined <- nyc_postal %>%
  left_join(nyc_covid_data_testing, by = c("ZIPCODE" = "MODZCTA"))  # Fixed missing parenthesis

#task 1
library(ggplot2)
library(sf)

# Plot COVID-19 positive test rate
ggplot(nyc_postal_Joined) +
  geom_sf(aes(fill = Positive / Total), color = NA) +
  scale_fill_viridis_c(option = "magma", name = "Positive Rate") +
  labs(title = "NYC COVID-19 Positive Test Rate (April 2020)") +
  theme_minimal() +
  theme(axis.text = element_blank()) +
  coord_sf(datum = st_crs(4326))  # Add graticule

task 2:

Quarto markdown is different from R markdown in terms of chunk options. See chunk options at Quarto website.

#task 2
nycNursingHome <- NYS_Health_Facility %>%
  dplyr::filter(`Short Description` == "NH") %>%
  dplyr::group_by(`Facility Zip Code`)

nyc_postal_nursing_homes <- nyc_postal %>%
  left_join(nycNursingHome, by = c("ZIPCODE" = "Facility Zip Code"))
## Warning in sf_column %in% names(g): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 6 of `x` matches multiple rows in `y`.
## ℹ Row 6 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.
plot(nyc_postal_nursing_homes["Short Description"], 
     main = "Nursing Homes in NYC ZIP Codes",
     pal = c("red"), 
     key.pos = 1, 
     graticule = TRUE)

task 3:

library(tmap)
## Warning: package 'tmap' was built under R version 4.4.3
library(sf)

# First, calculate positive rate as a new column
nyc_postal_Joined <- nyc_postal_Joined %>% 
  mutate(Positive_Rate = (Positive / Total) * 100)

# Set tmap to interactive mode
tmap_mode("view")
## ℹ tmap mode set to "view".
# Create interactive map
nyc_covid_map <- tm_shape(nyc_postal_Joined) +
  tm_polygons(
    col = "Positive",  # Color by case count
    palette = "YlOrRd",
    style = "quantile",
    n = 5,
    alpha = 0.7,
    border.col = "white",
    id = "ZIPCODE",  # Show ZIP code on hover
    popup.vars = c(
      "COVID-19 Cases" = "Positive",
      "Total Tests" = "Total",
      "Positive Rate (%)" = "Positive_Rate"  # Use pre-calculated column
    ),
    title = "COVID-19 Cases (April 2020)"
  ) +
  tm_basemap("OpenStreetMap") +
  tm_view(bbox = st_bbox(nyc_postal_Joined))  # Force NYC view
## 
## ── tmap v3 code detected ───────────────────────────────────────────────────────
## [v3->v4] `tm_polygons()`: instead of `style = "quantile"`, use fill.scale =
## `tm_scale_intervals()`.
## ℹ Migrate the argument(s) 'style', 'n', 'palette' (rename to 'values') to
##   'tm_scale_intervals(<HERE>)'[v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
## (instead of 'col'), and 'col' for the outlines (instead of 'border.col').[v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
## visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'
# Display and save
nyc_covid_map
## [cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "YlOrRd" is named
## "brewer.yl_or_rd"
## Multiple palettes called "yl_or_rd" found: "brewer.yl_or_rd", "matplotlib.yl_or_rd". The first one, "brewer.yl_or_rd", is returned.
tmap_save(nyc_covid_map, "nyc_covid_interactive.html")
## [cols4all] color palettes: use palettes from the R package cols4all. Run
## `cols4all::c4a_gui()` to explore them. The old palette name "YlOrRd" is named
## "brewer.yl_or_rd"
## Multiple palettes called "yl_or_rd" found: "brewer.yl_or_rd", "matplotlib.yl_or_rd". The first one, "brewer.yl_or_rd", is returned.
## 
## Interactive map saved to C:\Users\dwvil\Documents\SPRING 2025\R LANGUAGE\R-Spatial\R-Spatial\Section_08\nyc_covid_interactive.html