library(tidyverse)
library(tidyr)
library(ggplot2)
library(dplyr)
library(leaflet)
setwd("~/Downloads")
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)
latlong_clean3 <- latlong |>
filter(CityName != "Los Angeles") |>
filter(Year == 2016) |>
filter(StateDesc %in% c("Texas", "Florida", "New York", "Pennsylvania", "Illinois")) |>
filter(GeographicLevel == "City") |>
filter(Short_Question_Text == "Sleep <7 hours") |>
select(CityName, StateDesc, Data_Value,lat, long)2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
ggplot(latlong_clean3, aes(x = Data_Value, y = reorder(CityName, Data_Value), color = StateDesc)) +
geom_point(size = 3) +
labs(
title = "Sleep Deprivation by City in 5 Largest States (2016)",
x = "% of Adults Sleeping < 7 Hours",
y = "City",
color = "State"
) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet(latlong_clean3) |>
addTiles() |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = 5,
color = ~colorNumeric("#32a836", domain = latlong_clean3$Data_Value)(Data_Value),
stroke = FALSE,
fillOpacity = 0.8,
label = ~paste0(CityName, ", ", StateDesc, ": ", Data_Value, "% sleep-deprived")) |>
addLegend(
position = "bottomright",
pal = colorNumeric("#32a836", domain = latlong_clean3$Data_Value),
values = latlong_clean3$Data_Value,
title = "% Sleeping < 7 Hours")4. Refine your map to include a mouse-click tooltip
Refined map chunk here
leaflet(latlong_clean3) |>
addTiles() |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = ~Data_Value/5,
color = ~colorNumeric("#32a836", domain = latlong_clean3$Data_Value)(Data_Value),
stroke = FALSE,
fillOpacity = 0.8,
label = ~paste0(CityName, ", ", StateDesc, ": ", Data_Value, "% sleep-deprived"),
popup = ~paste0("<b>", CityName, ", ", StateDesc, "</b><br>",
"Sleep <7 hours: ", Data_Value, "%<br>",
"Coordinates: ", round(lat, 4), ", ", round(long, 4))) |>
addLegend(
position = "bottomright",
pal = colorNumeric("#32a836", domain = latlong_clean3$Data_Value),
values = latlong_clean3$Data_Value,
title = "% Sleeping < 7 Hours") |>
addControl(html = "<strong>Click markers for details</strong>",
position = "topright")5. Write a paragraph
This map is an interactive geographic visualization displaying sleep deprivation rates across major U.S. cities. The map focuses on five populous states—Texas, Florida, New York, Pennsylvania, and Illinois—to highlight the percentage of adults reporting fewer than 7 hours of sleep. Each city is represented by a proportionally sized green circle marker, where larger circles indicate higher sleep deprivation rates. The map includes dynamic features like hover tooltips showing city names and sleep statistics, while clickable popups provide detailed breakdowns including geographic coordinates. A color-coded legend contextualizes the data ranges, and a text box prompts users to interact with the markers. This visualization effectively transforms tabular public health data into an engaging spatial analysis tool.