library(tidyverse)
library(tidyr)
setwd("~/Downloads/First data 110 assignment_files")
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_filtered <- latlong |>
filter(StateDesc != "United States") |>
filter(Data_Value_Type == "Crude prevalence") |>
filter(Year == 2017) |>
filter(StateAbbr == "NC") |>
filter(Short_Question_Text == "Current Asthma")
head(latlong_filtered)# A tibble: 6 × 25
Year StateAbbr StateDesc CityName GeographicLevel DataSource Category
<dbl> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 NC North Carolin Cary Census Tract BRFSS Health …
2 2017 NC North Carolin Gastonia City BRFSS Health …
3 2017 NC North Carolin Winston-Sal… Census Tract BRFSS Health …
4 2017 NC North Carolin Concord Census Tract BRFSS Health …
5 2017 NC North Carolin Raleigh Census Tract BRFSS Health …
6 2017 NC North Carolin Greensboro Census Tract BRFSS Health …
# 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>latlong_final <- latlong_filtered |>
select(-DataSource,-Data_Value_Unit, -DataValueTypeID, -Low_Confidence_Limit, -High_Confidence_Limit, -Data_Value_Footnote_Symbol, -Data_Value_Footnote)
head(latlong_final)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 NC North Caro… Cary Census Tract Health … 3710740… Curren…
2 2017 NC North Caro… Gastonia City Health … 3725580 Curren…
3 2017 NC North Caro… Winston… Census Tract Health … 3775000… Curren…
4 2017 NC North Caro… Concord Census Tract Health … 3714100… Curren…
5 2017 NC North Caro… Raleigh Census Tract Health … 3755000… Curren…
6 2017 NC North Caro… Greensb… Census Tract Health … 3728000… Curren…
# 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>2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
current_asthma <- latlong_final |>
filter(!is.na(Data_Value)) |>
mutate(Data_Value = as.numeric(Data_Value)) |>
group_by(CityName) |>
summarize(avg_asthma = mean(Data_Value), .groups = "drop")
current_asthma |>
ggplot(aes(x = reorder(CityName, avg_asthma), y = avg_asthma, fill = avg_asthma)) +
geom_col() +
coord_flip() +
scale_fill_gradient(low = "lightblue", high = "navy") +
labs(title = "Average Current Asthma Prevalence in North Carolina",
x = "City",
y = "Average Asthma Prevalence",
fill = "Asthma Rate") +
theme_minimal()
3. Now create a map of your subsetted dataset.
First map chunk here
library(leaflet)
leaflet(latlong_final) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
setView(lng = -79.0, lat = 35.5, zoom = 6) |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = 3,
color = "blue",
fillOpacity = 0.5
)4. Refine your map to include a mouse-click tooltip
Refined map chunk here
latlong_map <- latlong_final |>
filter(!is.na(Data_Value))leaflet(latlong_map) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
setView(lng = -79.0, lat = 35.5, zoom = 6) |>
addCircleMarkers(
lng = ~long,
lat = ~lat,
radius = 3,
color = "blue",
fillOpacity = 0.5,
popup = ~paste(
"<b>State:<b>", StateDesc,
"<br><b>City:</b>", CityName,
"<br><b>Year:</b>", Year,
"<br><b>Asthma rate:</b>", round(Data_Value, 1), "%"
)
)5. Write a paragraph
For this project, I chose North Carolina because it is a state I like. I have been there before and I really enjoyed it, so I was curious to learn more about it. I also decided to focus on current asthma rates because I am asthmatic myself. I don’t often meet people who have the same condition, so I thought it might be rare and wanted to explore it more through this data.
The first plot is a bar graph that shows the average asthma rate by city. From this graph, we can see that some cities like Greenville and Gastonia have higher asthma rates, while others like Cary have lower rates.
The second visualization is a map that shows where asthma cases are located. Each point represents a location, and when we click on it, we can see more details such as the city, year, and asthma rate. From the map, we can see that many points are grouped around larger cities like Charlotte and Raleigh. This may be because these areas have more people, more traffic, and possibly more pollution, which can increase asthma cases. In addition, the map shows that asthma is present across the whole state, not just in one area, but the intensity varies by location.