library(tidyverse)
library(tidyr)
library(leaflet)
setwd("/Users/gerardosandoval/Downloads")
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 <- 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?)
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 <dbl>, long <dbl>, CategoryID <chr>,
# MeasureId <chr>, CityFIPS <dbl>, TractFIPS <dbl>, Short_Question_Text <chr>md <- prevention |>
filter(StateAbbr=="MD")
head(md)# 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>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
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"prevention2 <- prevention %>%
filter(StateDesc == "Rhode Island") %>%
filter (Short_Question_Text == "Annual Checkup")
prevention2# A tibble: 103 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 RI Rhode Isl… Pawtuck… Census Tract Prevent… 4454640… Visits…
2 2017 RI Rhode Isl… Warwick Census Tract Prevent… 4474300… Visits…
3 2017 RI Rhode Isl… Pawtuck… Census Tract Prevent… 4454640… Visits…
4 2017 RI Rhode Isl… Cranston Census Tract Prevent… 4419180… Visits…
5 2017 RI Rhode Isl… Provide… Census Tract Prevent… 4459000… Visits…
6 2017 RI Rhode Isl… Pawtuck… Census Tract Prevent… 4454640… Visits…
7 2017 RI Rhode Isl… Provide… Census Tract Prevent… 4459000… Visits…
8 2017 RI Rhode Isl… Provide… Census Tract Prevent… 4459000… Visits…
9 2017 RI Rhode Isl… Warwick Census Tract Prevent… 4474300… Visits…
10 2017 RI Rhode Isl… Warwick City Prevent… 4474300 Visits…
# ℹ 93 more rows
# 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.
First plot chunk here
prevention3 <- prevention %>%
filter (Short_Question_Text == "Taking BP Medication")
prevention3# A tibble: 28,504 × 18
Year StateAbbr StateDesc CityName GeographicLevel Category UniqueID Measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 NY New York Schenec… Census Tract Prevent… 3665508… Taking…
2 2017 NC North Car… Cary Census Tract Prevent… 3710740… Taking…
3 2017 NC North Car… Durham Census Tract Prevent… 3719000… Taking…
4 2017 NC North Car… Gastonia Census Tract Prevent… 3725580… Taking…
5 2017 NC North Car… Greensb… Census Tract Prevent… 3728000… Taking…
6 2017 OH Ohio Columbus Census Tract Prevent… 3918000… Taking…
7 2017 OK Oklahoma Norman Census Tract Prevent… 4052500… Taking…
8 2017 OK Oklahoma Oklahom… Census Tract Prevent… 4055000… Taking…
9 2017 OK Oklahoma Oklahom… Census Tract Prevent… 4055000… Taking…
10 2017 OR Oregon Gresham Census Tract Prevent… 4131250… Taking…
# ℹ 28,494 more rows
# 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>ggplot(prevention3, aes(x = StateDesc, y = Data_Value, fill = StateDesc)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c(
"California" = "skyblue",
"New York" = "purple",
"Texas" = "forestgreen",
"Florida" = "red"
)) +
theme_minimal() +
labs(
title = "Blood Pressure Per State",
x = "State",
y = "Blood Pressure" ,
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))Warning: Removed 794 rows containing missing values or values outside the scale range
(`geom_bar()`).
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet(prevention2) |>
setView(lng = -71.4459, lat = 41.8222, zoom = 11) |>
addProviderTiles("Stadia.AlidadeSmooth") |>
addCircles(
data = prevention2,
radius = prevention2$Data_Value,
color = "hotpink",
fillColor = "pink",
fillOpacity = 0.50
)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mousover tooltip
Refined map chunk here
popupprev <- paste0(
"<b>City: </b>", prevention2$CityName, "<br>",
"<b>Population: </b>", prevention2$PopulationCount, "<br>",
"<b>Reason: </b>", prevention2$Short_Question_Text, "<br>",
"<b>Percentage of People who Visited: </b>", prevention2$Data_Value, "<br>"
)map2 <-leaflet() |>
setView(lng = -71.4459, lat = 41.8222, zoom =11) |>
addProviderTiles("Stadia.AlidadeSmooth") |>
addCircles(
data = prevention2,
radius = prevention2$Data_Value,
color = "hotpink",
fillColor = "pink",
fillOpacity = 0.50 ,
popup = popupprev
)Assuming "long" and "lat" are longitude and latitude, respectivelymap25. Write a paragraph
Due to the limited number of numerical variables in the data set, I decided to use a bar graph for my first visualization. It clearly shows that states with higher populations and more known cities tend to have higher blood pressure (BP) levels. Possible reasons for this could include a higher prevalence of fast food restaurants and lower quality food availability. For my second visualization, I used a map to display the percentage of people who have their annual checkup at specific locations. The data appears to be focused in the northern part of Rhode Island, but more information is needed to understand why.