Healthy Cities GIS Assignment

Load the libraries and set the working directory

library(tidyverse)
library(tidyr)
setwd("C:/Users/Truly/OneDrive/Documents/Data work")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

The GeoLocation variable has (lat, long) format

Split GeoLocation (lat, long) into two columns: lat and long

latlong <- tidyr::extract(cities500, GeoLocation, c('lat', 'long'), 
               regex = ',?\\s*\\((\\d+\\.\\d+).*(-?\\d+\\.\\d+)\\)')
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 <chr>, long <chr>, 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 <chr>, long <chr>, 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 <chr>, long <chr>, 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

charleston_data <- prevention |>
  filter(CityName == "Charleston")
head(charleston_data)

# A tibble: 6 × 18
   Year StateAbbr StateDesc   CityName GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>       <chr>    <chr>           <chr>    <chr>    <chr>  
1  2017 SC        South Caro… Charles… City            Prevent… 4513330  "Visit…
2  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… "Curre…
3  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… "Visit…
4  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… "Visit…
5  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… "Curre…
6  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… "Curre…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   PopulationCount <dbl>, lat <chr>, long <chr>, CategoryID <chr>,
#   MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>

unique(charleston_data$Measure)

[1] "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years"               
[2] "Current lack of health insurance among adults aged 18\x9664 Years"                                    
[3] "Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressure"
[4] "Cholesterol screening among adults aged >=18 Years"                                                   

screen <- charleston_data %>%
  filter(grepl("screening", Measure))

Warning: There were 75 warnings in `filter()`.
The first warning was:
ℹ In argument: `grepl("screening", Measure)`.
Caused by warning in `grepl()`:
! unable to translate 'Current lack of health insurance among adults aged 18<96>64 Years' to a wide string
ℹ Run `dplyr::last_dplyr_warnings()` to see the 74 remaining warnings.

head(screen)

# A tibble: 6 × 18
   Year StateAbbr StateDesc   CityName GeographicLevel Category UniqueID Measure
  <dbl> <chr>     <chr>       <chr>    <chr>           <chr>    <chr>    <chr>  
1  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
2  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
3  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
4  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
5  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
6  2017 SC        South Caro… Charles… Census Tract    Prevent… 4513330… Choles…
# ℹ 10 more variables: Data_Value_Type <chr>, Data_Value <dbl>,
#   PopulationCount <dbl>, lat <chr>, long <chr>, 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

I made the first plot, but it had two crazy outliers that needed to be removed

plot1 <- screen %>%
  ggplot(aes(x = Data_Value, y = PopulationCount)) +
  geom_point() +
  labs(title = "Cholesterol Screening in Charleston", x = "Data Value", y = "Population Count")

print(plot1)

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Removed the oultiers

plot2 <- screen %>%
  filter(PopulationCount < 50000) %>%
  ggplot(aes(x = Data_Value, y = PopulationCount)) +
  geom_point() +
  labs(title = "Cholesterol Screening in Charleston", x = "Data Value", y = "Population Count")
plot2

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

3. Now create a map of your subsetted dataset.

First map chunk here

library(leaflet)

Warning: package 'leaflet' was built under R version 4.3.3

leaflet(screen) %>%
  addTiles() %>%
  addCircleMarkers(~-81, ~33, label = ~paste(TractFIPS, Data_Value), popup = ~TractFIPS)

4. Refine your map to include a mousover tooltip

Refined map chunk here

library(leaflet)
leaflet(screen) %>%
  addTiles() %>%
  addCircleMarkers(~-81, ~33, label = ~paste(TractFIPS, Data_Value), popup = ~TractFIPS)

5. Write a paragraph

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

After filtering the dataset for the city of Charleston, The first code filters the prevention dataset to include only data from Charleston, displaying the first few rows with head(charleston_data) The second part identifies unique measures within the Charleston data, focusing on those related to screening by using grepl(“screening”, Measure). It then displays the top entries with head(screen).The third snippet creates a scatter plot (plot2) showing the relationship between the Data_Value (presumably a measure of cholesterol screening rates or outcomes) and PopulationCount for populations under 50,000 in Charleston.I tried my hardest on the geo map but for some reason I could only use the “TractFIPS”.