library(tidyverse)
library(tidyr)
library(leaflet)
library(sf)
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)
head(latlong_clean)# 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 Unhealthy Beh…
4 2017 CA California Indio Census Tract BRFSS Health Outcom…
5 2017 CA California Inglewood Census Tract BRFSS Health Outcom…
6 2017 CA California Lakewood City BRFSS Unhealthy Beh…
# 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 CA California Hawthorne Census Tract Health … 0632548… Arthri…
2 2017 CA California Hawthorne City Unhealt… 632548 Curren…
3 2017 CA California Hayward City Unhealt… 633000 Obesit…
4 2017 CA California Indio Census Tract Health … 0636448… Arthri…
5 2017 CA California Inglewood Census Tract Health … 0636546… Diagno…
6 2017 CA California Lakewood City Unhealt… 639892 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 in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.
Filter chunk here
sad<- latlong_clean2 |>
filter( Measure == "Mental health not good for >=14 days among adults aged >=18 Years" , StateDesc == "Louisiana") 2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
P1 <- ggplot(sad, aes(x = CityName, y = Data_Value)) +
geom_col(fill= "#cfc4c0") +
theme_bw() +
labs(
title = "Poor Mental Health for More Than or Equal to 14 Days in Louisiana",
x = "City",
y = "Percentage (%)",
caption = "Source: CDC")
P1Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_col()`).
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet() |>
setView(lng = -91.5209, lat = 30.5191, zoom =6.25) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = sad,
color = "#ad232e",
radius = sqrt(2^sad$Data_Value)*3)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
popupsad <- paste0(
"<b>City: </b>", sad$CityName, "<br>",
"<b>Percentage: </b>", sad$Data_Value, "<br>",
"<b>Population : </b>", sad$PopulationCount, "<br>")leaflet() |>
setView(lng = -91.5209, lat = 30.5191, zoom =6.25) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = sad,
color = "#ad232e",
radius = sqrt(2^sad$Data_Value)*3,
popup = popupsad )Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
My plot’s data comes from a questionnaire and statistics for the 500 biggest cities in the U.S provided by the CDC. From this dataset, I chose to focus on the mental health section, specifically looking at the percentage of adults ages 18 and older who reported experiencing poor mental health for 14 or more days. I decided to focus on Louisiana because, when you search “unhappiest state in the US,” Louisiana is the first results that appears.
For the visualization, I used the World Street Map option from the Leaflet package. The circles on the plot represent the percentage of people in each area who fall into the the bad mental health category. I chose a red color for the circles using Google’s color picker so they were able to stand out clearly against the map background. I also adjusted the radius of the circles to make the differences between areas more visually distinct.
To enhance the user experience, I added tooltips that display specific information such as the city name, population, and the percentage of people with poor mental health. One challenge I faced was getting the zoom level just right. I tried zoom levels 6.5 and 7, but they were too close, making it hard to view the data clearly. Eventually, I found that a zoom level of 6.25 was the sweet spot for displaying Louisiana effectively.