GIS Assignment - 500 Healthy cities markdown

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
library(leaflet)
library(viridis)
getwd()

## [1] "/Users/gimle/Desktop/Data 110/Submitted Data 110"

setwd("/Users/gimle/Desktop/Data 110/Datasets 110/")


cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

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?)

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>

md <- prevention |>
  filter(StateAbbr=="MD")

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

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.

prevention_new <- prevention |>
  select(-Data_Value_Type, -CategoryID)
head(prevention_new)

## # A tibble: 6 × 16
##    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 8 more variables: Data_Value <dbl>, PopulationCount <dbl>, lat <dbl>,
## #   long <dbl>, MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>,
## #   Short_Question_Text <chr>

usa_cholesterol <- prevention |>
  filter(MeasureId== "CHOLSCREEN")

head(usa_cholesterol)

## # 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   Choles…
## 3  2017 CA        California Richmond  Census Tract    Prevent… 0660620… Choles…
## 4  2017 FL        Florida    Davie     Census Tract    Prevent… 1216475… Choles…
## 5  2017 FL        Florida    Hialeah   Census Tract    Prevent… 1230000… Choles…
## 6  2017 FL        Florida    Miami Be… Census Tract    Prevent… 1245025… 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>

unique(latlong_clean$StateAbbr)

##  [1] "AL" "CA" "FL" "CT" "IL" "MN" "NY" "PA" "NC" "OH" "OK" "OR" "TX" "RI" "SC"
## [16] "SD" "TN" "UT" "VA" "WA" "AK" "WI" "AZ" "AR" "CO" "DE" "NV" "DC" "GA" "ID"
## [31] "HI" "MA" "MI" "IN" "KS" "KY" "IA" "LA" "MD" "ME" "NH" "NJ" "NM" "MO" "MS"
## [46] "NE" "MT" "ND" "WV" "VT" "WY"

2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.

ggplot(usa_cholesterol, aes(x=Data_Value, y = PopulationCount))+
  geom_point(alpha = 0.4, na.rm = TRUE) +
  scale_color_viridis_d()  +
  theme_bw() +
  labs(title = "Cholesterol value plotted against population size tested",
       caption = "Source: 500 Healthy Cities Dataset",
       x = "Measured Cholesterol level",
       y = "Size of population tested"
       ) 

3. Now create a map of your subsetted dataset.

leaflet(usa_cholesterol)|>
  addProviderTiles("Esri.WorldStreetMap") |>
  addCircles(
    lng = ~long, 
    lat = ~lat, 
    weight = 1, 
    radius = 1, 
    popup = ~CityName)

4. Refine your map to include a mousover tooltip

colorPal1 <- colorNumeric(palette = "inferno", domain = c(33,95.7))

popupCHOL<- paste0(
      "<b>City: </b>", usa_cholesterol$CityName, "<br>",
      "<b>Cholesterol value: </b>", usa_cholesterol$Data_Value, "<br>",
      "<b>Cholesterol value: </b>", usa_cholesterol$PopulationCount, "<br>"
    )

leaflet() |>
  setView(lng = -118, lat = 34, zoom = 10) |>
  addProviderTiles("CartoDB.DarkMatter") |>
  addCircles(
    data = usa_cholesterol,
    radius = 4,
    color = ~colorPal1(Data_Value),
    opacity = 0.8,
    popup = popupCHOL,
    lng = ~long,
    lat = ~lat
  )

5. Write a paragraph

In a paragraph, describe the plots you created and what they show:

The first graph was made hoping to check and see if the population size of those tested had an effect on the result. The graph did not clearly demonstrate that, although more could be done to explore that (higher population tested resulted in a result closer to the median). In the second graph I wanted to use leaflet to see how the tested cholesterol levels appeared geografically. I chose the inferno palette in order to bring that into stronger relief against the dark background of the Carto DB Darkmatter map. The results does suggest that these levels vary quite signifcantly depending on the location of the test.