library(tidyverse)
library(tidyr)
library(leaflet)
setwd("C:/Users/enomc/OneDrive - montgomerycollege.edu/Documents/Data Science")
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) dont slice
latlong_CA <- latlong |>
filter(StateDesc == "California") |>
filter(Data_Value_Type == "Age-adjusted prevalence") |>
filter(Year == 2017) |>
filter(Category == "Unhealthy Behaviors")
head(latlong_CA)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CA California Indio City BRFSS Unhealthy Beh…
2 2017 CA California Corona City BRFSS Unhealthy Beh…
3 2017 CA California Fullerton City BRFSS Unhealthy Beh…
4 2017 CA California Fullerton City BRFSS Unhealthy Beh…
5 2017 CA California San Diego City BRFSS Unhealthy Beh…
6 2017 CA California Tracy 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>ca_lon <- -119.4
ca_lat <- 36.82. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
## Non-map plot unhealthy behaviors across cities
ggplot(latlong_CA, aes(x = Data_Value, fill = Short_Question_Text )) +
geom_histogram(color = "white") + # I wanted to make it white so that there's a little white space separating the types of unhealthy behavior
theme_bw() +
labs(
title = "Distribution of Unhealthy Behavior Prevalence (California, 2017)",
x = "Age-adjusted Prevalence (%)",
y = "Number of Cities",
fill = "Type of Unhealthy Behavior",
caption = "Source: latlong_CA dataset"
)`stat_bin()` using `bins = 30`. Pick better value `binwidth`.
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet() |>
setView(lng = -119.4, lat = 36.8, zoom =6.1) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
addCircles(
data = latlong_CA,
radius =sqrt(1.75^latlong_CA$Data_Value) * 2,
)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
# Create popup or when you click on point to show info like Japan's tutorial
popup_CA <- paste0(
"<b>City: </b>", latlong_CA$CityName, "<br>",
"<b>Behavior: </b>", latlong_CA$Short_Question_Text, "<br>",
"<b>Age-adjustedPrevalence: </b>", latlong_CA$Data_Value, "%<br>",
"<b>Data Source: </b>", latlong_CA$DataSource
)leaflet() |>
setView(lng = -119.4, lat = 36.8, zoom =6.1) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
addCircles(
data = latlong_CA,
radius =sqrt(1.75^latlong_CA$Data_Value) * 2,
popup = popup_CA
)Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
For the first non-map plot, I decided to make a histogram showing how common behavior rates are across the cities in California 2017. The x-axis shows how common the rates are, the y-axis shows how many cities fall in each range, and then the colors show four different types of unhealthy behaviors. For question three I made an interactive map of California with circles for each city. Bigger circles mean higher prevalence of unhealthy behaviors. Mostly followed steps from japan’s tutorial and then switching up for my own dataset, variable names etc. For question four I added popups or mouse tool tips so when you click a circle it shows city, behavior, prevalence, and the data source. For the radius of the circles on the map, I started with the formula from the tutorial but adjusted it to fit my dataset.