library(tidyverse)
library(tidyr)
library(leaflet)
setwd("C:/Users/ccrab/Documents/DATA110/datasets")
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_c3 <- latlong |>
filter(StateDesc != "United States") |>
filter(Data_Value_Type == "Crude prevalence") |>
filter(Year == 2016) |>
filter(StateAbbr == "AZ") |>
filter(Category == "Health Outcomes") |>
filter(CityName == "Phoenix")
head(latlong_c3)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
2 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
3 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
4 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
5 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
6 2016 AZ Arizona Phoenix Census Tract BRFSS Health Outcomes
# 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>latlong_c4 <- latlong_c3 |>
select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(latlong_c4)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
2 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
3 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
4 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
5 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
6 2016 AZ Arizona Phoenix Census Tract Health Ou… 0455000… All te…
# 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>2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
ggplot(latlong_c4, aes(x = long, y = Data_Value, color = Measure)) +
scale_color_viridis_d() +
geom_jitter(alpha = 0.6) +
labs(
title = "2016 Health Outcomes in Phoenix, AZ",
x = "Longitude",
y = "Crude Prevalence (%)",
color = "Health Measure",
caption = "Source: CDC 500 Cities Dataset"
) +
theme_bw()Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet() |>
setView(lng = -112.0740, lat = 33.4484, zoom = 11) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = latlong_c4,
lng = ~long,
lat = ~lat,
radius = ~Data_Value * 100,
color = "blue",
)4. Refine your map to include a mouse-click tooltip
Refined map chunk here “I used chatgpt to help with adjusting the lng and lat.”
leaflet(data = latlong_c4) |>
setView(lng = -112.0740, lat = 33.4484, zoom = 11) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = ~Data_Value * 1.5,
color = "purple",
fillOpacity = 0.6,
popup = ~paste(
"<strong>Measure:</strong>", Measure, "<br>",
"<strong>Value:</strong>", PopulationCount, "%<br>",
"<strong>City:</strong>", CityName, "<br>",
"<strong>Year:</strong>", Year
)
)5. Write a paragraph
The plots I created visualize the Health Outcomes for Phoenix, Arizona in 2016 using both a scatter plot and an interactive map. The scatter plot shows the crude prevalence percentages (Data_Value) of various health outcome measures along the city’s longitude, with each point color-coded by the specific measure (in this case tooth lost). The interactive map further show this by plotting each data point as a clickable circle on a real-world map using Leaflet. When a user clicks a circle, a tooltip appears showing detailed information about the health measure, its population, the city, and the year. I realize after filtering through that in specific places in Arizona adults 65 and older have tooth lost and I originally was looking at Chandler, AZ but saw that Phoenix was rather bigger but that’s due to it being the capital. I did research and saw that it’s simply due to old age but also because other factors were low income, smokers,and saying women are more likely. But it has decreased by more than 75% over the past five decades.