library(tidyverse)
library(tidyr)
library(leaflet)
setwd("~/DATA 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(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 CA California Hawthorne Census Tract BRFSS Health Outcom…
2 2017 CA California Hawthorne City BRFSS Unhealthy Beh…
3 2017 CA California Hayward City BRFSS Unhealthy Beh…
4 2017 CA California Indio Census Tract BRFSS Health Outcom…
5 2017 CA California Inglewood Census Tract BRFSS Health Outcom…
6 2017 CA California Lakewood 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>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 CA California Hawthorne Census Tract Health … 0632548… Arthri…
2 2017 CA California Hawthorne City Unhealt… 632548 Curren…
3 2017 CA California Hayward City Unhealt… 633000 Obesit…
4 2017 CA California Indio Census Tract Health … 0636448… Arthri…
5 2017 CA California Inglewood Census Tract Health … 0636546… Diagno…
6 2017 CA California Lakewood City Unhealt… 639892 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>md <- latlong_clean2 |>
filter(StateAbbr == "MD")
unique(md$CityName)[1] "Baltimore"The new dataset “Prevention” is a manageable dataset now.
For your assignment, work with a cleaned dataset.
1. Once you run the above code and learn how to filter in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.
Filter chunk here
# Make a data set for each of the 2 variables I want to look at
mapData <- latlong_clean2 |>
filter(StateAbbr == "DC" & Short_Question_Text == "Obesity") |>
arrange(lat)
mapData2 <- latlong_clean2 |>
filter(StateAbbr == "DC" & Short_Question_Text == "Annual Checkup") |>
arrange(lat)
# Combine the data
checkup <- pull(mapData2, Data_Value)
lat2 <- pull(mapData2, lat)
long2 <- pull(mapData2, long)
mapData$checkup <- checkup
mapData$lat2 <- lat2
mapData$long2 <- long2
# Check to make sure they combined properly
mapData |>
ggplot(aes(long, long2)) +
geom_point()
2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
mapData |>
ggplot(aes(Data_Value, lat, color = long, size = PopulationCount)) +
geom_point() +
labs(title = "DC Obesity Exploration\nwith Location & Population",
x = "Obesity (%)", y = "Latitude",
color = "Longitude",
caption = "Source: CDC 500 Cities Project") +
scale_color_gradient(low = "blue", high = "pink")Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
mapData |>
ggplot(aes(Data_Value, checkup)) +
geom_point() +
geom_smooth() +
labs(title = "DC Annaual Checkups vs Obesity Rates",
x = "Obesity (%)", y = "Annaual Checkup (%)",
caption = "Source: CDC 500 Cities Project") +
theme_bw()`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 1 row containing non-finite outside the scale range (`stat_smooth()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
3. Now create a map of your subsetted dataset.
First map chunk here
# https://stackoverflow.com/questions/49951416/how-to-use-colornumeric-within-addcircles-in-leaflet
cols <- colorNumeric(palette = "magma", domain = mapData$checkup)
leaflet() |>
setView(lng = -77.0369, lat = 38.9072, zoom = 11) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(data = mapData, radius = mapData$Data_Value^1.7, color = ~cols(checkup))Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
cols <- colorNumeric(palette = "plasma", domain = mapData$checkup)
tooltip <- paste0(
"<b>Obesity: </b>", mapData$Data_Value, "<b>%</b>", "<br>",
"<b>Annual Checkup: </b>", mapData$checkup, "<b>%</b>", "<br>",
"<b>Population: </b>", mapData$PopulationCount, "<br>")
leaflet() |>
setView(lng = -77.0369, lat = 38.9072, zoom = 11) |>
addProviderTiles("Esri.NatGeoWorldMap") |>
addCircles(data = mapData, radius = mapData$Data_Value^1.7,
color = ~cols(checkup), popup = tooltip)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 I made is just to make sure I constructed my dataset correctly making the spacial coordinates match up. Then I made a exploratory graph looking at the relationship between location (latitude and longitude) and population. There seemed to be a relationship with location but not with population. The second plot shows that there is a positive correlation between the rates of people receiving annual checkups and getting obesity. This was very interesting to me because it is not what I expected. I would have guessed that people who go to the doctor regularly would have all around better health. I would be interested to see what other variable(s) may be affecting these trends. My final map shows DC with the size of the dot being obesity percentage and the color representing the annual medical visit percentage. The map is able to show that cities in the north west of DC have both lower checkup and obesity percentages, while the west of DC has the opposite.