library(tidyverse)
library(tidyr)
library(leaflet)
setwd("/Applications/DATA110")
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 “latlong_clean2” is a manageable dataset now.
For your assignment, work with a cleaned dataset where you perform your own cleaning and filtering.
1. Once you run the above code and 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 through some inclusion/exclusion criteria.
Filter chunk here (you may need multiple chunks)
latlong_clean3 <- latlong_clean2 |>
filter(GeographicLevel == "Census Tract") 2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
plot1 <- ggplot(latlong_clean3, aes(x = Data_Value, y = PopulationCount, color = Short_Question_Text)) +
geom_point(alpha = 0.70) +
labs(x = "Prevalance (%)", y = "Population Count", title = "Unhealthy Behavior Prevalence in Connecticut (2017)", caption = "Source: CDC.gov") +
facet_wrap(~Short_Question_Text) +
theme_bw(base_family = "Times") +
scale_color_manual(name = "Health Issue", values = c("#4d7f7f","#da98a0","#704f53", "#f0c29c"))
plot1
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet(latlong_clean3) |>
setView(lng = -73.1, lat = 41.4, zoom = 8.5) |>
addTiles() |>
addCircles(
data = latlong_clean3,
radius = latlong_clean3$PopulationCount/4,
color = c("#4d7f7f","#da98a0","#704f53", "#f0c29c")
)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
popup <- paste0(
"<b>City Name: </b>", latlong_clean3$CityName, "<br>",
"<b>Unhealthy Behavior: </b>", latlong_clean3$Short_Question_Text, "<br>",
"<b>Prevalence (%): </b>", latlong_clean3$Data_Value, "<br>",
"<strong>Population: </strong>", latlong_clean3$PopulationCount, "<br>"
)
colors <- colorFactor(palette = c("#4d7f7f","#da98a0","#704f53", "#f0c29c"),
levels = c("Binge Drinking", "Current Smoking", "Obesity", "Physical Inactivity"))leaflet(latlong_clean3) |>
setView(lng = -73.1, lat = 41.4, zoom = 8.5) |>
addTiles() |>
addCircles(
data = latlong_clean3,
radius = (latlong_clean3$Data_Value)*15,
weight = 2,
fillColor = ~colors(Short_Question_Text),
fillOpacity = 0.9,
color = "#5f3e20",
popup = popup
)Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
For my map, I used the Short_Question_Text to categorize the four unhealthy behaviors into specific colors in my graph and map that correspond to a specific color. In the map I gave them a radius of the data_value (%) * 15 to symbolize the prevalence of the unhealthy behavior, but I found that many long/lats were the same which ended up to leaving the circles looking like an archery target. I would argue that this is a good visualization and helps with the pop up tool tip since you can click on a circle inside of a circle that shows a different behavior in color and text. For these inner/outer circles, the population may be the same, it’s just the radius depending on the % that changes.