library(tidyverse)
library(tidyr)
library(dplyr)
cities500 <- read_csv('C:/Users/omyue/OneDrive/Desktop/Montgomery College/Spring 24/Data 101/datasets/500CitiesLocalHealthIndicators.cdc.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(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>dmv <- prevention |>
filter(StateAbbr== c("MD", "VA", "DC"))Warning: There was 1 warning in `filter()`.
ℹ In argument: `StateAbbr == c("MD", "VA", "DC")`.
Caused by warning in `StateAbbr == c("MD", "VA", "DC")`:
! longer object length is not a multiple of shorter object lengthhead(dmv)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 VA Virginia Norfolk Census Tract Prevent… 5157000… "Visit…
2 2017 VA Virginia Virgini… Census Tract Prevent… 5182000… "Visit…
3 2017 DC District o… Washing… Census Tract Prevent… 1150000… "Visit…
4 2017 DC District o… Washing… Census Tract Prevent… 1150000… "Curre…
5 2017 DC District o… Washing… Census Tract Prevent… 1150000… "Takin…
6 2017 DC District o… Washing… Census Tract Prevent… 1150000… "Chole…
# 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 the cleaned “Prevention” dataset
1. Once you run the above code, filter this dataset one more time for any particular subset.
Filter chunk here
access2_dmv <- dmv |>
filter(MeasureId=="ACCESS2")
dmv2 <- access2_dmv |>
group_by(CityName) |>
mutate(PopulationCount = (sum(PopulationCount))/10^2)2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
library(ggplot2)
plot <- ggplot(dmv2, aes(x = CityName, y = PopulationCount)) +
geom_bar(stat = "identity", fill = "skyblue") +
labs(title = "Population Count of Health Insurance by City", x = "City Name", y = "Population Count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot
3. Now create a map of your subsetted dataset.
First map chunk here
library(leaflet)Warning: package 'leaflet' was built under R version 4.3.3leaflet() |>
setView(lng = -77.0369, lat = 37.9072, zoom =7) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = access2_dmv,
radius = access2_dmv$PopulationCount/10
)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mousover tooltip
Refined map chunk here
popupcity <- paste0(
"<b>City: </b>", access2_dmv$CityName, "<br>",
"<b>State: </b>", access2_dmv$StateDesc, "<br>",
"<b>Population: </b>", access2_dmv$PopulationCount, "<br>"
)library(leaflet)
leaflet() |>
setView(lng = -77.0369, lat = 37.9072, zoom =7) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = access2_dmv,
radius = access2_dmv$PopulationCount/10,
popup = popupcity
)Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
The first plot measures the population of individuals who have no access to health insurance. The x-axis describes all of the cities listed in the access2_dmv data frame. The y-axis measures the population of each city that has no access to health insurance. The Baltimore bar in the first plot shows that the most people who have access to no health insurance in the data set, but that may be because it has the most amount of residents compared to the other cities in the data set. The map plot visualizes where individuals with no health insurance are the most prevalent. The size of the circle on the graph visualizes the size of the population in a designated area that does not have access to health insurance.