library(tidyverse)
library(tidyr)
library(webshot2)
setwd("~/Desktop/Data 110")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")
data(cities500)Healthy Cities GIS Assignment
Load the libraries and set the working directory
The GeoLocation variable has (lat, long) format
Split GeoLocation (lat, long) into two columns: lat and long
latlong <- cities500|>
mutate(GeoLocation = str_replace_all(GeoLocation, "[()]", ""))|>
separate(GeoLocation, into = c("lat", "long"), sep = ",", convert = TRUE)
head(latlong)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CA California Hawthorne Census Tract BRFSS Health Outcom…
2 2017 CA California Hawthorne City BRFSS Unhealthy Beh…
3 2017 CA California Hayward City BRFSS Health Outcom…
4 2017 CA California Hayward City BRFSS Unhealthy Beh…
5 2017 CA California Hemet City BRFSS Prevention
6 2017 CA California Indio Census Tract BRFSS Health Outcom…
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
# DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
# Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
# Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>Filter the dataset
Remove the StateDesc that includes the United Sates, select Prevention as the category (of interest), filter for only measuring crude prevalence and select only 2017.
latlong_clean <- latlong |>
filter(StateDesc != "United States") |>
filter(Data_Value_Type == "Crude prevalence") |>
filter(Year == 2017) |>
filter(StateAbbr == "CT") |>
filter(Category == "Unhealthy Behaviors")
head(latlong_clean)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CT Connecticut Bridgeport Census Tract BRFSS Unhealthy B…
2 2017 CT Connecticut Danbury City BRFSS Unhealthy B…
3 2017 CT Connecticut Norwalk Census Tract BRFSS Unhealthy B…
4 2017 CT Connecticut Bridgeport Census Tract BRFSS Unhealthy B…
5 2017 CT Connecticut Hartford Census Tract BRFSS Unhealthy B…
6 2017 CT Connecticut Waterbury Census Tract BRFSS Unhealthy B…
# ℹ 18 more variables: UniqueID <chr>, Measure <chr>, Data_Value_Unit <chr>,
# DataValueTypeID <chr>, Data_Value_Type <chr>, Data_Value <dbl>,
# Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
# Data_Value_Footnote_Symbol <chr>, Data_Value_Footnote <chr>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>What variables are included? (can any of them be removed?)
names(latlong_clean) [1] "Year" "StateAbbr"
[3] "StateDesc" "CityName"
[5] "GeographicLevel" "DataSource"
[7] "Category" "UniqueID"
[9] "Measure" "Data_Value_Unit"
[11] "DataValueTypeID" "Data_Value_Type"
[13] "Data_Value" "Low_Confidence_Limit"
[15] "High_Confidence_Limit" "Data_Value_Footnote_Symbol"
[17] "Data_Value_Footnote" "PopulationCount"
[19] "lat" "long"
[21] "CategoryID" "MeasureId"
[23] "CityFIPS" "TractFIPS"
[25] "Short_Question_Text" Remove the variables that will not be used in the assignment
latlong_clean2 <- latlong_clean |>
select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(latlong_clean2)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CT Connecticut Bridgep… Census Tract Unhealt… 0908000… Obesit…
2 2017 CT Connecticut Danbury City Unhealt… 918430 Obesit…
3 2017 CT Connecticut Norwalk Census Tract Unhealt… 0955990… Obesit…
4 2017 CT Connecticut Bridgep… Census Tract Unhealt… 0908000… Curren…
5 2017 CT Connecticut Hartford Census Tract Unhealt… 0937000… Obesit…
6 2017 CT Connecticut Waterbu… Census Tract Unhealt… 0980000… Obesit…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>The new dataset “Prevention” is a manageable dataset now.
For your assignment, work with a cleaned dataset.
1. Once you run the above code and learn how to filter this complicated dataset, perform your own investigation by filtering this dataset however you choose so that you have a subset with no more than 900 observations.
Filter chunk here (you may need multiple chunks)
subset_data <- latlong_clean2 %>%
filter(StateAbbr == "CT" & Short_Question_Text == "Binge Drinking")
subset_data# A tibble: 228 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CT Connectic… Waterbu… Census Tract Unhealt… 0980000… Binge …
2 2017 CT Connectic… Norwalk Census Tract Unhealt… 0955990… Binge …
3 2017 CT Connectic… Stamford Census Tract Unhealt… 0973000… Binge …
4 2017 CT Connectic… Danbury Census Tract Unhealt… 0918430… Binge …
5 2017 CT Connectic… Bridgep… Census Tract Unhealt… 0908000… Binge …
6 2017 CT Connectic… Stamford Census Tract Unhealt… 0973000… Binge …
7 2017 CT Connectic… Danbury Census Tract Unhealt… 0918430… Binge …
8 2017 CT Connectic… Stamford City Unhealt… 973000 Binge …
9 2017 CT Connectic… Stamford Census Tract Unhealt… 0973000… Binge …
10 2017 CT Connectic… Hartford Census Tract Unhealt… 0937000… Binge …
# ℹ 218 more rows
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>city_only <- subset_data %>%
filter(GeographicLevel == "City")2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
# non map plot
health_insurance_plot <- ggplot(city_only, aes(CityName, Data_Value, color = CityName)) +
geom_boxplot(shape = 17, size = 3) +
scale_color_brewer(palette = "Pastel1") +
labs(
x = "City Name",
y = "Value (%)",
title = "Unhealthy Behaviors by City in Connecticut (2017)",
subtitle = "Each boxes represents the level of Unhealthy Behaviors by city in Connecticut.",
color = "City Name"
) +
theme(
plot.background = element_rect(fill = "lightgrey"),
panel.background = element_rect(fill = "grey"),
axis.title = element_text(face = 2),
legend.background = element_rect(fill = "lightgrey"),
legend.title = element_text(color = "black", size = 12),
legend.text = element_text(color = "black", size = 11),
legend.key.size = unit(0.75, units = "cm"),
panel.grid = element_line(color = "darkgrey"),
axis.text.x = element_text(angle = 45, hjust = 1)
)
health_insurance_plot
3. Now create a map of your subsetted dataset.
First map chunk here
# leaflet()
library(leaflet)
library(knitr)
library(sf)Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUEmap_plot1 <- leaflet(data = city_only) |>
setView(lat = 41.6032, lng = -73.0877, zoom = 7) |> # Connecticut coordonates
addProviderTiles("OpenStreetMap") |>
addCircles(
lat = ~lat,
lng = ~long,
radius = ~sqrt(10^(Data_Value / 30)) * 5,
color = "green",
)
map_plot14. Refine your map to include a mouse-click tooltip
Refined map chunk here
# Define the popup content
map_popup1 <- paste0(
"<b>City: </b>", city_only$CityName, "<br>",
"<b>Census Tract: </b>", city_only$UniqueID, "<br>",
"<b>Data Value (%): </b>", city_only$Data_Value, "<br>",
"<strong>Population: </strong>", city_only$PopulationCount, "<br>"
)
# Create the map with popups
map_w_popup <- leaflet() |>
setView(lat = 41.6032, lng = -73.0877, zoom = 7) |>
addProviderTiles("OpenStreetMap") |>
addCircles(
lat = ~lat,
lng = ~long,
data = city_only,
radius = sqrt(10^(city_only$Data_Value/30)) * 5,
color = "yellow",
popup = map_popup1
)
map_w_popup5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
The plots I created display unhealthy behavior prevalence data across cities and census tracts in Connecticut for 2017. The scatter plot shows the percentage of unhealthy behaviors by city, with each point representing a different city. The map plot provides a geographic view, where circle sizes reflect the severity of unhealthy behaviors in each location. Each marker includes a popup with additional details such as city name, census tract, data value, and population. Together, these visualizations highlight regional differences in health patterns across the state.