library(tidyverse)
library(tidyr)
library(leaflet)
library(viridis)
setwd("C:/Users/Marti/OneDrive/Desktop/MC-DV")
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>names(prevention) <- tolower(names(prevention))
names(prevention) <- gsub(" ","_",names(prevention))
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>wi <- prevention |>
filter(stateabbr == "WI")
head(wi)# A tibble: 6 × 18
year stateabbr statedesc cityname geographiclevel category uniqueid measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 WI Wisconsin Madison Census Tract Preventi… 5548000… "Takin…
2 2017 WI Wisconsin Madison Census Tract Preventi… 5548000… "Chole…
3 2017 WI Wisconsin Milwaukee Census Tract Preventi… 5553000… "Visit…
4 2017 WI Wisconsin Milwaukee Census Tract Preventi… 5553000… "Chole…
5 2017 WI Wisconsin Milwaukee Census Tract Preventi… 5553000… "Takin…
6 2017 WI Wisconsin Madison Census Tract Preventi… 5548000… "Curre…
# 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 data set “Prevention” is a manageable data set now.
For your assignment, work with the cleaned “Prevention” data set
1. Once you run the above code, filter this data set one more time for any particular subset.
Filter chunk here
Additional filter to only focus on the city of Kenosha, Wisconsin
kenosha_wi <- wi |>
filter(cityname == "Kenosha")
head(kenosha_wi)# A tibble: 6 × 18
year stateabbr statedesc cityname geographiclevel category uniqueid measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Chole…
2 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Visit…
3 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Curre…
4 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Chole…
5 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Chole…
6 2017 WI Wisconsin Kenosha Census Tract Prevention 5539225… "Curre…
# 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
Plotting to see the percentages of the data values in the Kenosha Wisconsin Data set by the short question text since it’s a shorter version of the measure.
ggplot(kenosha_wi, aes(x = short_question_text , y = data_value, color = short_question_text )) +
geom_point(aes(size=data_value)) +
labs(title = "Prevention for Kenosha Wisonsin", y = "Percentage", x = "Population") +
theme_bw() +
scale_fill_viridis_d(name = "Measure") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).
To see what else we can do with a differnt filter instead of just seeing Kenosha data lets filter for only “City” information for all of Wisconsin.
wisconsin_cities <- wi |>
filter(geographiclevel == "City")
head(wisconsin_cities)# A tibble: 6 × 18
year stateabbr statedesc cityname geographiclevel category uniqueid measure
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 2017 WI Wisconsin Milwaukee City Preventi… 5553000 "Visit…
2 2017 WI Wisconsin Green Bay City Preventi… 5531000 "Chole…
3 2017 WI Wisconsin Kenosha City Preventi… 5539225 "Visit…
4 2017 WI Wisconsin Appleton City Preventi… 5502375 "Chole…
5 2017 WI Wisconsin Racine City Preventi… 5566000 "Curre…
6 2017 WI Wisconsin Green Bay City Preventi… 5531000 "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>Now using a bar graph to plot Wisconsin City data for each measure comparing each city.
ggplot(wisconsin_cities, aes(x = cityname, y = data_value, fill = short_question_text)) +
geom_bar(stat = "identity", position = "Dodge") +
labs(title = "Prevention for Wisconsin Cities", y = "Percentage of Population", x = "City") +
theme_bw() +
scale_fill_viridis_d(name = "Measure") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
3. Now create a map of your subsetted dataset.
First map chunk here
leaflet(data = kenosha_wi) |>
setView(lng = -87.87357, lat = 42.58557, zoom = 12) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = kenosha_wi,
radius = kenosha_wi$data_value
)Assuming "long" and "lat" are longitude and latitude, respectivelyleaflet(data = wisconsin_cities) |>
setView(lng = -88.2, lat = 43.5, zoom = 7) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = wisconsin_cities,
radius = wisconsin_cities$data_value
)Assuming "long" and "lat" are longitude and latitude, respectively4. Refine your map to include a mouse-click tooltip
Refined map chunk here
color_palette <- colorFactor(viridis::viridis_pal()(length(unique(kenosha_wi$short_question_text))),
domain = kenosha_wi$short_question_text)
leaflet(data = kenosha_wi) |>
setView(lng = -87.87357, lat = 42.58557, zoom = 12) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = kenosha_wi,
radius = kenosha_wi$data_value* 4,
color = color_palette(kenosha_wi$short_question_text),
fillOpacity = 0.5,
weight = 1.5,
popup = ~paste("<b>City: ", cityname, "<br>",
"<b>Population Count: ", populationcount, "<br>",
"<b>Measure: ", short_question_text, "<br>",
"<b>Data Value: ", data_value))Assuming "long" and "lat" are longitude and latitude, respectivelycolor_palette <- colorFactor(viridis::viridis_pal()(length(unique(kenosha_wi$short_question_text))),
domain = kenosha_wi$short_question_text)
leaflet(data = wisconsin_cities) |>
setView(lng = -88.2, lat = 43.5, zoom = 7) |>
addProviderTiles("Esri.WorldStreetMap") |>
addCircles(
data = wisconsin_cities,
radius = wisconsin_cities$data_value* 100,
color = ~color_palette(short_question_text),
fillColor = ~color_palette(short_question_text),
fillOpacity = 0.5,
weight = 1.5,
popup = ~paste("<b>City: ", cityname, "<br>",
"<b>Population Count: ", populationcount, "<br>",
"<b>Measure: ", short_question_text, "<br>",
"<b>Data Value: ", data_value))Assuming "long" and "lat" are longitude and latitude, respectively5. Write a paragraph
In a paragraph, describe the plots you created and what they show.
From my 500 Heathly Cities Data Set, I decided to take two approaches as my hometown and home state is Kenosha, Wisconsin. I filtered to the selection of just Kenosha, as well as just Wisconsin City Data. I plotted the different measures in prevention for Kenosha, but didn’t read much so I also did it for all Wisconsin cities. In the latter I found the large difference that the lack of health insurance was much lower than the rest of the catagories. I also mapped first for each data point then to try and show to different levels of percentage per catagory in each area, both in the Kenosha map, as well as the Wisconsin map. In these different sized bubbles it shows the percentage value of the measure in the percentage of that population with different colors representing the different inner circles. While the tool tip does seem to work, it doesn’t always select the individual inner circles as unique tool tips.