task 1:
nys_health_sf <- st_read("nys_health_sf.gpkg", quiet = TRUE)
covid_data <- st_read("covidPopZipNYC.gpkg", quiet = TRUE)
str(covid_data)
## Classes 'sf' and 'data.frame': 180 obs. of 20 variables:
## $ ZIPCODE : chr "10001" "10002" "10003" "10004" ...
## $ PO_NAME : chr "New York" "New York" "New York" "New York" ...
## $ POPULATION : num 22413 81305 55878 2187 8107 ...
## $ COUNTY : chr "New York" "New York" "New York" "New York" ...
## $ NEIGHBORHOOD_NAME : chr "Chelsea/NoMad/West Chelsea" "Chinatown/Lower East Side" "East Village/Gramercy/Greenwich Village" "Financial District" ...
## $ COVID_CASE_COUNT : num 1542 5902 2803 247 413 ...
## $ TOTAL_COVID_TESTS : num 20158 48197 41076 3599 6102 ...
## $ COVID_DEATH_COUNT : num 35 264 48 2 0 1 4 118 37 62 ...
## $ FoodStoreNum : int 57 192 87 10 10 4 24 84 52 100 ...
## $ healthfacilities_NUM: int 7 20 16 1 1 1 2 5 12 13 ...
## $ totPop : int 19146 74310 53487 NA 8809 4639 15510 58683 28129 56433 ...
## $ adultPop : int 17658 64499 49620 NA 7853 4096 12954 52081 25531 51006 ...
## $ malePctg : num 51.2 48.4 50.3 NA 42.8 ...
## $ femalePop : num 48.8 51.6 49.7 NA 57.2 ...
## $ elderlyPop : int 2500 15815 6296 NA 209 66 2134 8907 3469 8848 ...
## $ asianPop : int 4837 32149 8027 NA 1974 1195 6040 8503 4702 7301 ...
## $ blackPop : int 1092 5969 3130 NA 185 52 1008 5745 1159 3220 ...
## $ hispanicPop : int 2435 20065 4375 NA 504 507 1096 14672 2434 6828 ...
## $ whitePop : int 12485 24033 40503 NA 6419 3130 7912 35952 21438 43728 ...
## $ geom :sfc_MULTIPOLYGON of length 180; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:110, 1:2] 981959 981980 981988 981993 982052 ...
## ..- 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 [1:19] "ZIPCODE" "PO_NAME" "POPULATION" "COUNTY" ...
hist(covid_data$COVID_CASE_COUNT,
main = "Histogram of New COVID-19 Cases",
col = "skyblue",
border = "white")
breaks_j <- classIntervals(c(min(covid_data$COVID_CASE_COUNT) - .00001,
covid_data$COVID_CASE_COUNT), n = 7, style = "jenks")
covid_data <- covid_data %>%
mutate(covid_data_cut = cut(COVID_CASE_COUNT, breaks_j$brks,
include.lowest = TRUE, dig.lab = 4))
clean_labels <- sapply(1:(length(breaks_j$brks) - 1), function(i) {
paste0(
format(breaks_j$brks[i], big.mark = ",", scientific = FALSE, trim = TRUE),
" – ",
format(breaks_j$brks[i + 1], big.mark = ",", scientific = FALSE, trim = TRUE)
)
})
ggplot(covid_data) +
geom_sf(aes(fill = covid_data_cut)) +
scale_fill_brewer(palette = "Purples",
name = ' Covid Cases per Zipcode',
labels = clean_labels) +
labs(
x = 'Longitude', y = 'Latitude',
title = 'New York City Covid Cases in 2021',
caption = 'Data Source: The City of New York'
) +
theme(
plot.caption = element_text(
hjust = 0.5,
color = "grey60",
size = 8,
margin = margin(t = 5)
)
)
breaks_j_pop <- classIntervals(c(min(covid_data$elderlyPop) - .00001,
covid_data$elderlyPop), n = 5, style = "jenks")
## Warning in classIntervals(c(min(covid_data$elderlyPop) - 1e-05,
## covid_data$elderlyPop), : var has missing values, omitted in finding classes
covid_data <- covid_data %>%
mutate(elderlyPop_cut = cut(elderlyPop, breaks_j_pop$brks,
include.lowest = TRUE, dig.lab = 4))
clean_labels_pop <- sapply(1:(length(breaks_j_pop$brks) - 1), function(i) {
paste0(
format(breaks_j_pop$brks[i], big.mark = ",", scientific = FALSE, trim = TRUE),
" – ",
format(breaks_j_pop$brks[i + 1], big.mark = ",", scientific = FALSE, trim = TRUE))
}
)
ggplot(covid_data) +
geom_sf(aes(fill = elderlyPop_cut)) +
scale_fill_brewer(palette = "OrRd",
name = 'Elderly Population',
labels = clean_labels_pop) +
labs(
x = 'Longitude', y = 'Latitude',
title = 'Elderly Population in New York City',
caption = 'Data Source: American Community Survey'
) +
theme(
plot.caption = element_text(
hjust = 0.5,
color = "grey60",
size = 8,
margin = margin(t = 5)
)
)
task 2:
g1 <- ggplot(covid_data) +
geom_sf(aes(fill = covid_data_cut), show.legend = TRUE) +
scale_fill_brewer(palette = "OrRd", name = 'Covid Cases per Zipcode', labels = clean_labels) +
coord_sf(
xlim = c(-74.26, -73.70),
ylim = c(40.48, 40.92),
default_crs = sf::st_crs(4326)
) +
geom_sf_label(data = covid_data %>% dplyr::filter(COVID_CASE_COUNT > 10000),
aes(label = COVID_CASE_COUNT),
label.size = .07,
size = 2) +
labs(
x = 'Longitude',
y = 'Latitude',
title = 'New York City Covid Cases in 2021',
caption = 'Data Source: The City of New York'
) +
theme(
legend.position = "bottom",
legend.justification = "center",
legend.box.just = "center",
legend.title = element_text(hjust = 1),
plot.caption = element_text(
hjust = 0,
color = "grey60",
size = 8,
margin = margin(t = 5)
)) +
guides(fill = guide_legend(
title.position = "top",
))
g2 <- ggplot(nys_health_sf) +
geom_sf(colour='blue', size=0.1) +
scale_fill_brewer(palette = "OrRd", name='Noise Complaints') +
coord_sf(xlim = c(-74.26, -73.70),
ylim = c(40.48, 40.92), default_crs = sf::st_crs(4326)) +
labs(x='Longitude', y='Latitude',
title='NYC Health Facilities')
g1 + g2 + plot_layout(ncol = 2, nrow = 1)
task 3:
covid_data <- st_transform(covid_data, 4326)
pal_fun <- colorQuantile("Purples", NULL, n = 5)
p_popup <- paste0(
"<strong>Covid Cases: </strong>", covid_data$COVID_CASE_COUNT,
"<br/>",
"<strong>Covid Deaths: </strong>", covid_data$COVID_DEATH_COUNT
)
polyHighlightOption <- leaflet::highlightOptions(opacity = 0.8, fillColor = 'grey')
polyLabelOption <- leaflet::labelOptions(opacity = 0.6)
htmlMap <- leaflet(covid_data) %>%
addTiles(group = "OSM") %>%
addProviderTiles("CartoDB.DarkMatter", group = "Carto") %>%
addPolygons(
stroke = FALSE,
fillColor = ~pal_fun(COVID_CASE_COUNT),
fillOpacity = 0.8,
group = "Covid Cases",
smoothFactor = 0.5,
popup = p_popup,
highlightOptions = polyHighlightOption,
labelOptions = polyLabelOption
) %>%
addCircleMarkers(radius = 0.03, weight = 1,
data = nys_health_sf %>%
sf::st_set_crs(4326) %>% sf::st_cast("POINT"),
group = "Health Facilities")%>%
addLegend(
"bottomright",
colors = brewer.pal(7, "Purples"),
labels = paste0("up to ", format(breaks_j$brks[-1], digits = 1)),
title = "Covid Cases Per Zipcode"
) %>%
addLayersControl(baseGroups = c("OSM", "Carto"),
overlayGroups = c("Covid Cases", "Health Facilities")) %>%
hideGroup(c("Health Facilities")) %>%
setView(lng = -73.93, lat = 40.73, zoom = 10)
htmlMap
htmlwidgets::saveWidget(htmlMap,
file = "nyc_covid_map.html",
selfcontained = TRUE)