R Week 9 Assignment

(To start): Load Zip Code level ACS and Covid data from lab 8
Task 1: Plot at least two high-quality static maps with one using the COVID-19 data and one using a related factor.
Task 2: Use ggplot2 and other ggplot-compatible packages to create a multi-map figure illustrating the possible relationship between COVID-19 confirmed cases or rate and another factor
Task 3: Create a web-based interactive map for COIVD-19 data using tmap, mapview, or leaflet package and save it as a HTML file.

Steven A. Assigment 9 R-Spatial

R Spatial Lab Assignment # 2

(To start): Load Zip Code level ACS and Covid data from lab 8

setwd('C:/Users/steve/Downloads/G385_R_Data_Viz/R_Spatial')

# Load mega sf file from lab 8 
mega <-st_read("nyc_mega_covid_acs_rf_nh_data.gpkg")

## Reading layer `nyc_mega_covid_acs_rf_nh_data' from data source 
##   `C:\Users\steve\Downloads\G385_R_Data_Viz\R_Spatial\nyc_mega_covid_acs_rf_nh_data.gpkg' 
##   using driver `GPKG'
## Simple feature collection with 248 features and 35 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 913129 ymin: 120020.9 xmax: 1067494 ymax: 272710.9
## Projected CRS: NAD83 / New York Long Island (ftUS)

str(mega)

## Classes 'sf' and 'data.frame':   248 obs. of  36 variables:
##  $ ZIPCODE.x        : chr  "00083" "10001" "10002" "10003" ...
##  $ pop_total        : num  3 19146 74310 53487 NA ...
##  $ elder65_pop      : num  0 2500 15815 6296 NA ...
##  $ male_pop         : num  0 9799 35957 26930 NA ...
##  $ malePctg         : num  0 51.2 48.4 50.3 NA ...
##  $ female_pop       : num  3 9347 38353 26557 NA ...
##  $ white_pop        : num  1 12485 24033 40503 NA ...
##  $ black_pop        : num  2 1092 5969 3130 NA ...
##  $ hispanic_pop     : num  1 2435 20065 4375 NA ...
##  $ adult_pop        : num  2 17658 64499 49620 NA ...
##  $ citizen_adult    : num  2 13800 52528 42971 NA ...
##  $ ZIPCODE.y        : chr  NA "10001" "10002" "10003" ...
##  $ BLDGZIP          : chr  NA "0" "0" "0" ...
##  $ PO_NAME          : chr  NA "New York" "New York" "New York" ...
##  $ POPULATION       : num  NA 22413 81305 55878 NA ...
##  $ AREA             : num  NA 17794941 26280129 15538376 NA ...
##  $ STATE            : chr  NA "NY" "NY" "NY" ...
##  $ COUNTY           : chr  NA "New York" "New York" "New York" ...
##  $ ST_FIPS          : chr  NA "36" "36" "36" ...
##  $ CTY_FIPS         : chr  NA "061" "061" "061" ...
##  $ URL              : chr  NA "http://www.usps.com/" "http://www.usps.com/" "http://www.usps.com/" ...
##  $ SHAPE_AREA       : num  NA 0 0 0 NA 0 0 0 0 0 ...
##  $ SHAPE_LEN        : num  NA 0 0 0 NA 0 0 0 0 0 ...
##  $ NEIGHBORHOOD_NAME: chr  NA "Chelsea/NoMad/West Chelsea" "Chinatown/Lower East Side" "East Village/Gramercy/Greenwich Village" ...
##  $ BOROUGH_GROUP    : chr  NA "Manhattan" "Manhattan" "Manhattan" ...
##  $ label            : chr  NA "10001, 10118" "10002" "10003" ...
##  $ lat              : num  NA 40.8 40.7 40.7 NA ...
##  $ lon              : num  NA -74 -74 -74 NA ...
##  $ COVID_CASE_COUNT : num  NA 1542 5902 2803 NA ...
##  $ COVID_CASE_RATE  : num  NA 5584 7836 5193 NA ...
##  $ POP_DENOMINATOR  : num  NA 27613 75323 53978 NA ...
##  $ COVID_DEATH_COUNT: num  NA 35 264 48 NA 0 1 4 118 37 ...
##  $ COVID_DEATH_RATE : num  NA 126.8 350.5 88.9 NA ...
##  $ PERCENT_POSITIVE : num  NA 7.86 12.63 6.93 NA ...
##  $ TOTAL_COVID_TESTS: num  NA 20158 48197 41076 NA ...
##  $ geom             :sfc_MULTIPOLYGON of length 248; first list element: List of 1
##   ..$ :List of 1
##   .. ..$ : num [1:216, 1:2] 998310 998283 998251 998178 998050 ...
##   ..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
##  - attr(*, "sf_column")= chr "geom"
##  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "names")= chr [1:35] "ZIPCODE.x" "pop_total" "elder65_pop" "male_pop" ...

names(mega)

##  [1] "ZIPCODE.x"         "pop_total"         "elder65_pop"      
##  [4] "male_pop"          "malePctg"          "female_pop"       
##  [7] "white_pop"         "black_pop"         "hispanic_pop"     
## [10] "adult_pop"         "citizen_adult"     "ZIPCODE.y"        
## [13] "BLDGZIP"           "PO_NAME"           "POPULATION"       
## [16] "AREA"              "STATE"             "COUNTY"           
## [19] "ST_FIPS"           "CTY_FIPS"          "URL"              
## [22] "SHAPE_AREA"        "SHAPE_LEN"         "NEIGHBORHOOD_NAME"
## [25] "BOROUGH_GROUP"     "label"             "lat"              
## [28] "lon"               "COVID_CASE_COUNT"  "COVID_CASE_RATE"  
## [31] "POP_DENOMINATOR"   "COVID_DEATH_COUNT" "COVID_DEATH_RATE" 
## [34] "PERCENT_POSITIVE"  "TOTAL_COVID_TESTS" "geom"

Task 1: Plot at least two high-quality static maps with one using the COVID-19 data and one using a related factor.

# plot - covid percent positive 
ggplot(data = mega) +
  geom_sf(aes(fill = PERCENT_POSITIVE)) +
  scale_fill_viridis_c(option = "plasma", na.value = "lightgrey") +
  labs(title = "COVID-19 Percent Positive by ZIP Code",
       fill = "Percent Positive") +
  theme_minimal()

# plot - hispanic population estimate 
ggplot(data = mega) +
  geom_sf(aes(fill = hispanic_pop)) +
  scale_fill_viridis_c(option = "plasma", na.value = "lightgrey") +
  labs(title = "ACS Hispanic Population by ZIP Code",
       fill = "Hispanic Population Estimate") +
  theme_minimal()

Task 2: Use ggplot2 and other ggplot-compatible packages to create a multi-map figure illustrating the possible relationship between COVID-19 confirmed cases or rate and another factor

The maps should be side by side on one single page.
Add graticule to at least one of those maps
label some of the feature on the map where applicable and appropriate.

p1 <- ggplot(data = mega) +
  geom_sf(aes(fill = COVID_CASE_RATE)) +
  scale_fill_viridis_c(option = "plasma", na.value = "lightgrey") +
  labs(title = "COVID-19 Case Rate (per 100,000 people) by Zip Code",
       fill = "Case Rate") +
  theme_minimal()

# define second plot - elderly population estimate 
p2 <- ggplot(data = mega) +
  geom_sf(aes(fill = elder65_pop)) +
  scale_fill_viridis_c(option = "plasma", na.value = "lightgrey") +
  labs(title = "ACS Estimate of Elderly Population (65+ years old) by Zip Code",
       fill = "Elderly Population") +
  theme_minimal() 
#library to make them side by side, I tried using gridExtra but its hard to set equal ratio
library(patchwork)

## Warning: package 'patchwork' was built under R version 4.4.3

side_by_side <- p1 + p2 + plot_layout(ncol = 2)
side_by_side

Task 3: Create a web-based interactive map for COIVD-19 data using tmap, mapview, or leaflet package and save it as a HTML file.

# library for interactive maps 
library(tmap)

## Warning: package 'tmap' was built under R version 4.4.3

#?tmap
#?tm_shape
#?tm_fill
#?tm_scale
#?tm_borders

# set to view for interactive or plot for static
tmap_mode("view")

## ℹ tmap mode set to "view".

#define data used for map 
covid_death_rate <- tm_shape(mega) +
  tm_fill("COVID_DEATH_RATE", # map layer for polyogns, fill (fill color) 
          fill.scale = tm_scale(values = "brewer.yl_or_rd"), # "Visual variable that determines the fill color" - from help
          fill.legend = tm_legend(title = "Death Rate")) +  # legend title name
  tm_borders(fill_alpha = 0.5) + # transparency of borders
  tm_title("COVID-19 Death Rate (per 100,000 people) by ZIP Code")

covid_death_rate

tmap_save(covid_death_rate, filename = "covid_death_rate.html")

## Interactive map saved to C:\Users\steve\Downloads\G385_R_Data_Viz\R_Spatial\week9\covid_death_rate.html

#cols4all::c4a_gui()

R Week 9 Assignment

Steven Aarons

5/15/2025

Steven A. Assigment 9 R-Spatial

R Spatial Lab Assignment # 2

(To start): Load Zip Code level ACS and Covid data from lab 8

Task 2: Use ggplot2 and other ggplot-compatible packages to create a multi-map figure illustrating the possible relationship between COVID-19 confirmed cases or rate and another factor

Task 3: Create a web-based interactive map for COIVD-19 data using tmap, mapview, or leaflet package and save it as a HTML file.