library(tidyverse)
library(tidyr)
library(leaflet)
cities500 <- read_csv("~/Downloads/500CitiesLocalHealthIndicators.cdc (1).csv")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)
my_subset <- latlong_clean2 |>
filter(GeographicLevel == "City")
nrow(my_subset)[1] 32my_subset_clean <- my_subset |>
select(
Year,
StateAbbr,
StateDesc,
CityName,
GeographicLevel,
Category,
Measure,
Short_Question_Text,
Data_Value_Type,
Data_Value,
PopulationCount,
lat,
long
)
head(my_subset_clean)# A tibble: 6 × 13
Year StateAbbr StateDesc CityName GeographicLevel Category Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 CT Connecticut Danbury City Unhealthy Beh… Obesit…
2 2017 CT Connecticut Stamford City Unhealthy Beh… Binge …
3 2017 CT Connecticut New Haven City Unhealthy Beh… Obesit…
4 2017 CT Connecticut Waterbury City Unhealthy Beh… No lei…
5 2017 CT Connecticut New Britain City Unhealthy Beh… No lei…
6 2017 CT Connecticut Hartford City Unhealthy Beh… No lei…
# ℹ 6 more variables: Short_Question_Text <chr>, Data_Value_Type <chr>,
# Data_Value <dbl>, PopulationCount <dbl>, lat <dbl>, long <dbl>2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
ggplot(my_subset_clean,
aes(x = reorder(CityName, Data_Value),
y = Data_Value,
fill = Measure)) +
geom_col() +
coord_flip() +
labs(
title = "Unhealthy Behaviors in Connecticut Cities",
subtitle = "CDC 500 Cities crude prevalence estimates from 2017",
x = "City",
y = "Percent of Adults",
fill = "Health Measure",
caption = "Source: CDC 500 Cities Local Health Indicators"
) +
theme_minimal()
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet(my_subset_clean) |>
addTiles() |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = ~Data_Value / 4,
popup = ~paste(
"<b>City:</b>", CityName,
"<br><b>Measure:</b>", Measure,
"<br><b>Value:</b>", Data_Value, "%"
)
)4. Refine your map to include a mouse-click tooltip
Refined map chunk here
leaflet(my_subset_clean) |>
addProviderTiles("CartoDB.Positron") |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = ~Data_Value / 4,
color = "darkblue",
fillColor = "red",
fillOpacity = 0.7,
stroke = FALSE,
label = ~paste(CityName, "-", Short_Question_Text, ":", Data_Value, "%"),
popup = ~paste(
"<b>City:</b>", CityName,
"<br><b>Measure:</b>", Measure,
"<br><b>Value:</b>", Data_Value, "%",
"<br><b>Population:</b>", PopulationCount
)
)5. Write a paragraph
I made a bar graph and two interactive maps using the CDC 500 Cities dataset. I filtered the data so it only showed Connecticut cities from 2017 and looked at unhealthy behaviors using crude prevalence. The bar graph lets you compare the percentages for different health measures across the cities. The maps show the same information but on a map, which makes it easier to see where the different health behaviors are happening. When you click on a city, it shows the city name, the health measure, the percentage, and the population. I think the maps make the data easier to understand because you can actually see where everything is located instead of just looking at numbers. It also helped me compare the cities alot faster.