library(tidyverse)
library(tidyr)
setwd("C:/Users/Administrator/Documents/Data110/Datasets")
cities500 <- read_csv("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 <- tidyr::extract(
cities500,
GeoLocation,
c('lat', 'long'),
regex = '\\(([-+]?\\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
Explore categories
library(dplyr)
# Look at categories in data
categories <- prevention |>
group_by(CategoryID, MeasureId, Data_Value_Type, Short_Question_Text) |>
summarize(
Min_Data_Value = min(Data_Value, na.rm = TRUE),
Max_Data_Value = max(Data_Value, na.rm = TRUE)
)
# Print the result
print(categories)# A tibble: 4 × 6
# Groups: CategoryID, MeasureId, Data_Value_Type [4]
CategoryID MeasureId Data_Value_Type Short_Question_Text Min_Data_Value
<chr> <chr> <chr> <chr> <dbl>
1 PREVENT ACCESS2 Crude prevalence Health Insurance 2.4
2 PREVENT BPMED Crude prevalence Taking BP Medication 9.6
3 PREVENT CHECKUP Crude prevalence Annual Checkup 42.3
4 PREVENT CHOLSCREEN Crude prevalence Cholesterol Screening 33.9
# ℹ 1 more variable: Max_Data_Value <dbl>Filter subset
# Filter data
filtered_data <- prevention |>
filter(StateAbbr == "MD") |>
filter(GeographicLevel == "Census Tract") |>
#filter(Short_Question_Text == "Annual Checkup") |>
# Change lat/long to numeric
mutate(
lat = as.numeric(lat),
long = as.numeric(long),
)
head(filtered_data)# A tibble: 6 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Chole…
2 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
3 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
4 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Curre…
5 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Curre…
6 2017 MD Maryland Baltimore Census Tract Preventi… 2404000… "Visit…
# 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>2. Based on the GIS tutorial (Japan earthquakes), create one plot about something in your subsetted dataset.
library(ggplot2)
# Scatter plot with facet wrap
ggplot(filtered_data, aes(x = PopulationCount,
y = Data_Value,
color = Short_Question_Text
)) +
geom_point(alpha = 0.05) +
scale_color_viridis_d() +
geom_jitter() +
facet_wrap(~Short_Question_Text, scales = "free") +
labs(title = "Scatter Plot of Population Count vs. Annual Checkup Data",
x = "Population Count",
y = "Cholesterol Data"
) +
# Modify legend title and add auto trend line
labs(color = "Test Type") +
geom_smooth(method = "auto", se = FALSE, color = "red", size = 1) +
#Theme
theme_bw()
3. Now create a map of your subsetted dataset.
First map chunk here
library(leaflet)
library(sf)
library(tidyverse)
library(knitr)
# Filter data
filtered_data <- filtered_data |>
filter(Short_Question_Text == "Annual Checkup")
# Color palette for the heatmap
color_palette <- colorQuantile("YlOrRd", domain = filtered_data$Data_Value)
# Leaflet map
leaflet(data = filtered_data) |>
setView(lat = mean(filtered_data$lat),
lng = mean(filtered_data$long),
zoom = 11) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircleMarkers(data = filtered_data,
radius = ~sqrt(Data_Value),
color = "#14010d",
fill = TRUE,
fillColor = ~color_palette(Data_Value),
fillOpacity = 0.7)4. Refine your map to include a mousover tooltip
Refined map chunk here
# Filter data
filtered_data <- filtered_data |>
filter(Short_Question_Text == "Annual Checkup")
# Color palette for the heatmap
color_palette <- colorQuantile("YlOrRd", domain = filtered_data$Data_Value)
# Pop up
tooltip <- paste0(
"<b>City FIPS: </b>", filtered_data$CityFIPS, "<br>",
"<b>Tract FIPS: </b>", filtered_data$TractFIPS, "<br>",
"<b>City Name: </b>", filtered_data$CityName, "<br>",
"<b>State: </b>", filtered_data$StateAbbr, "<br>",
"<br>",
"<b>Annual Checkup Cholesterol Data: </b>", filtered_data$Data_Value, "<br>"
)
# Leaflet map
leaflet(data = filtered_data) |>
setView(lat = mean(filtered_data$lat),
lng = mean(filtered_data$long),
zoom = 11) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircleMarkers(data = filtered_data,
radius = ~sqrt(Data_Value),
color = "#14010d",
fill = TRUE,
fillColor = ~color_palette(Data_Value),
fillOpacity = 0.7,
popup = tooltip)5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
Plot: The chart is a scatter plot with a facet wrap to display a plot for each type of checkup or test on cholesterol levels. Charting it on a single graph was difficult due to the difference in the range and scale of cholesterol levels. The cholesterol data is on the y-axis, and population count is on the x-axis. Census tracts were used, but the population of census tracts might be normalized yielding not a very interesting trend. However, it was interesting to observe the range and the clustering of the different types.
Leaflet map: Maryland was filtered along with the ‘annual checkup’ type of test with the colors in the form of a heat map. The darker the hue of red, the higher the cholesterol number. It seems to correlate well with socioeconomic conditions and poverty, which is not surprising, but I’m basing this on my familiarity with the area.
Popup tooltip: A pop-up was added to the above map, which includes the city FIPS and tract FIPS as an ID for that data point, along with the city, state, and the annual checkup cholesterol data, for easy exploration by clicking the data point.
One thing I found challenging was figuring out how to add titles and legends to the leaflet map.