library(leaflet)
library(sf)
library(tidyverse)
library(tidyr)
setwd("~/Desktop/Data Science 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(Category == "Prevention") |>
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 AL Alabama Montgomery City BRFSS Prevention
2 2017 CA California Concord City BRFSS Prevention
3 2017 CA California Concord City BRFSS Prevention
4 2017 CA California Fontana City BRFSS Prevention
5 2017 CA California Richmond Census Tract BRFSS Prevention
6 2017 FL Florida Davie Census Tract BRFSS Prevention
# 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
prevention <- latlong_clean |>
select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(prevention)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 AL Alabama Montgome… City Prevent… 151000 Choles…
2 2017 CA California Concord City Prevent… 616000 Visits…
3 2017 CA California Concord City Prevent… 616000 Choles…
4 2017 CA California Fontana City Prevent… 624680 Visits…
5 2017 CA California Richmond Census Tract Prevent… 0660620… Choles…
6 2017 FL Florida Davie Census Tract Prevent… 1216475… Choles…
# 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>md <- prevention |>
filter(StateAbbr=="MD")
head(md)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Chole…
2 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
3 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
4 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Curre…
5 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Curre…
6 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
# 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>unique(md$CityName)[1] "Baltimore"The new dataset “Prevention” is a manageable dataset now.
usa_lon <- -98.35
usa_lat <- 39.50For your assignment, work with a cleaned dataset.
1. Once you run the above code, filter this dataset one more time for any particular subset with no more than 900 observations.
Filter chunk here
new <- latlong |>
filter(StateDesc != "United States") |>
filter(Short_Question_Text == "Coronary Heart Disease") |>
filter(GeographicLevel == "City") |>
filter(Data_Value >= "4.2")|>
filter(Year == "2017")
head(new)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CA California Hayward City BRFSS Health Out…
2 2017 AL Alabama Huntsville City BRFSS Health Out…
3 2017 AZ Arizona Surprise City BRFSS Health Out…
4 2017 CA California Bellflower City BRFSS Health Out…
5 2017 CA California Garden Grove City BRFSS Health Out…
6 2017 CA California Roseville City BRFSS Health Out…
# 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>new2 <- new|>
select(-StateAbbr, -DataSource, -DataValueTypeID, -Data_Value_Footnote_Symbol, -Data_Value_Footnote, -MeasureId, -TractFIPS)
head(new2)# A tibble: 6 × 18
Year StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 California Hayward City Health Outcomes 633000 Corona…
2 2017 Alabama Huntsville City Health Outcomes 137000 Corona…
3 2017 Arizona Surprise City Health Outcomes 471510 Corona…
4 2017 California Bellflower City Health Outcomes 604982 Corona…
5 2017 California Garden Grove City Health Outcomes 629000 Corona…
6 2017 California Roseville City Health Outcomes 662938 Corona…
# ℹ 11 more variables: Data_Value_Unit <chr>, Data_Value_Type <chr>,
# Data_Value <dbl>, Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# CityFIPS <dbl>, Short_Question_Text <chr>chd <- new2
head(chd)# A tibble: 6 × 18
Year StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 California Hayward City Health Outcomes 633000 Corona…
2 2017 Alabama Huntsville City Health Outcomes 137000 Corona…
3 2017 Arizona Surprise City Health Outcomes 471510 Corona…
4 2017 California Bellflower City Health Outcomes 604982 Corona…
5 2017 California Garden Grove City Health Outcomes 629000 Corona…
6 2017 California Roseville City Health Outcomes 662938 Corona…
# ℹ 11 more variables: Data_Value_Unit <chr>, Data_Value_Type <chr>,
# Data_Value <dbl>, Low_Confidence_Limit <dbl>, High_Confidence_Limit <dbl>,
# PopulationCount <dbl>, lat <dbl>, long <dbl>, CategoryID <chr>,
# CityFIPS <dbl>, Short_Question_Text <chr>Coronary heart disease (CHD), also known as coronary artery disease (CAD) or ischemic heart disease, occurs when the coronary arteries narrow or become blocked, preventing the heart from receiving enough oxygen-rich blood
2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
ggplot(chd, aes(x=Data_Value, y=PopulationCount, color=Data_Value_Type))+
geom_point(alpha = 0.01)+
scale_color_viridis_d()+
geom_jitter()+
labs(title = "CHD % based on Population Count")+
theme_dark()
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet() |>
setView(lng = -98.35, lat = 39.50, zoom =4) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = chd,
fillOpacity = 0.1,
radius = chd$Data_Value)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
chdpop <- paste0(
"<b>City: </b>", chd$CityName, "<br>",
"<b>State: </b>", chd$StateDesc, "<br>",
"<b>Population: </b>", chd$PopulationCount, "<br>",
"<b>CHD %: </b>", chd$Data_Value, "<br>"
)leaflet() |>
setView(lng = -98.35, lat = 39.5, zoom =4) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = chd,
fillOpacity = 0.5,
popup = chdpop,
radius = sqrt(10^chd$Data_Value)*2)Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
I have cleaned the 500 cities data set down to cases of Coronary Heart diesease. I have plotted the cases and made a map of cities that have cases over 4.2 percent. The cities or areas with larger diameter circles have larger percentage of people with high coronary heart disease. The locations with the highest percentages are Gary Indiana, Largo Florida, and Hemet California, all with 9% or higher.