library(tidyverse)
library(tidyr)
library(leaflet)
library(sf)
setwd("/Users/Lucinda/Downloads/data110")
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>#unique(md$CityName) The new dataset “Prevention” is a manageable dataset now.
For your assignment, work with a cleaned dataset.
1. Once you run the above code and learn how to filter in this format, filter this dataset however you choose so that you have a subset with no more than 900 observations.
Filter chunk here
unique(latlong_clean2$StateDesc) [1] "California" "Alabama" "Alaska" "Arizona"
[5] "Arkansas" "Connecticut" "Delaware" "District of C"
[9] "Florida" "Colorado" "Illinois" "Indiana"
[13] "Iowa" "Kansas" "Georgia" "Idaho"
[17] "Kentucky" "Louisiana" "Maine" "Massachusetts"
[21] "Michigan" "Minnesota" "Mississippi" "Missouri"
[25] "Montana" "Nebraska" "New York" "Nevada"
[29] "New Hampshire" "New Jersey" "Pennsylvania" "North Carolin"
[33] "North Dakota" "Ohio" "Oklahoma" "Oregon"
[37] "Texas" "Rhode Island" "South Carolin" "South Dakota"
[41] "Tennessee" "Utah" "Vermont" "Virginia"
[45] "Washington" "West Virginia" "Wisconsin" "Wyoming"
[49] "Hawaii" "Maryland" "New Mexico" tx_lat <- latlong_clean2 |>
filter(StateDesc == "Texas")# I think the professor meant to select "prevention" in chunk 3, or maybe we're supposed to have noticed and added it, but I'm going to add it here
tx_lat2 <- tx_lat |>
filter(Category == "Prevention") |>
filter(GeographicLevel == "Census Tract") |> # there were too few in the only other option, "City", though I might as well just remove "City" altogether
filter(PopulationCount > 7200)2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
First plot chunk here
tx_lat3 <- tx_lat2 |>
filter(Measure == "Current lack of health insurance among adults aged 18\x9664 Years") |>
group_by(CityName, PopulationCount) |>
summarize(count = n())`summarise()` has grouped output by 'CityName'. You can override using the
`.groups` argument.ggplot(tx_lat3, aes(x=PopulationCount, y=CityName))+
geom_histogram(stat = "identity", fill = "#de3a83") +
labs(title = "Total Count for Lack of Health Insurance in Adults by City (Texas)",
x = "Count",
y = "City",
caption = "Source: Centers for Disease Control and Prevention") +
theme_minimal(base_size = 11, base_family = "serif")Warning in geom_histogram(stat = "identity", fill = "#de3a83"): Ignoring
unknown parameters: `binwidth`, `bins`, and `pad`
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet() |>
setView(lng = -99.9, lat = 31.96, zoom =5.2) |>
addProviderTiles("OpenTopoMap") |>
addCircles(
data = tx_lat2,
radius = tx_lat2$PopulationCount,
color = "#bf1871")Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
popuplat <- paste0(
"<b>Population: </b>", tx_lat3$PopulationCount, "<br>",
"<b>City Name: </b>", tx_lat3$CityName, "<br>")
leaflet() |>
setView(lng = -99.9, lat = 31.96, zoom =5.2) |>
addProviderTiles("OpenTopoMap") |>
addCircles(
data = tx_lat2,
radius = tx_lat2$PopulationCount,
color = "#bf1871",
popup = popuplat)Assuming "long" and "lat" are longitude and latitude, respectivelypopup = popuplat5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
My first plot shows the occurance of a lack of health insurance per county. Sometimes, subsetting data, especially for a bar chart, is unusually difficult. This was one of those times. I was trying to figure out how to show which prevention measure was most common in each city, but for whatever reason just couldn’t figure it out. What’s shown is really just the total number of instances created instead of the fraction out of the population for each city, which is what I wanted to show. I’m going to spend more time after submitting this trying to figure out what the issue was. I used filter, group_by and summarize to subset the data, and used a histogram to plot it. The second and third charts show population per city in Texas, using leaflet.