GIS Assignment

Load the libraries and set the working directory

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(tidyr)
library(ggplot2)
library(leaflet)

## Warning: package 'leaflet' was built under R version 4.3.2

library(viridis)

## Warning: package 'viridis' was built under R version 4.3.2

## Loading required package: viridisLite

setwd("C:/Users/danyd/OneDrive/Desktop/data 110/week10hw")
cities500 <- read_csv("500CitiesLocalHealthIndicators.cdc.csv")

## Rows: 810103 Columns: 24
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (17): StateAbbr, StateDesc, CityName, GeographicLevel, DataSource, Categ...
## dbl  (6): Year, Data_Value, Low_Confidence_Limit, High_Confidence_Limit, Cit...
## num  (1): PopulationCount
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

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

annualcheckup <- prevention |>
  filter(Measure == "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years")
head(annualcheckup)

## # A tibble: 6 × 18
##    Year StateAbbr StateDesc   CityName GeographicLevel Category UniqueID Measure
##   <dbl> <chr>     <chr>       <chr>    <chr>           <chr>    <chr>    <chr>  
## 1  2017 CA        California  Concord  City            Prevent… 616000   Visits…
## 2  2017 CA        California  Fontana  City            Prevent… 624680   Visits…
## 3  2017 FL        Florida     Hialeah  Census Tract    Prevent… 1230000… Visits…
## 4  2017 CA        California  Stockton City            Prevent… 675000   Visits…
## 5  2017 NY        New York    Rochest… Census Tract    Prevent… 3663000… Visits…
## 6  2017 NC        North Caro… Gastonia Census Tract    Prevent… 3725580… Visits…
## # 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

ggplot(annualcheckup, aes(x = long, y = lat, color = Data_Value)) +
  geom_point() +
  labs(title = "Annual Checkup Prevalence",
       x = "Longitude",
       y = "Latitude",
       color = "Prevalence Rate (%)") +
  theme_minimal()

3. Now create a map of your subsetted dataset.

First map chunk here

annualcheckup$long <- as.numeric(annualcheckup$long)
annualcheckup$lat <- as.numeric(annualcheckup$lat)
annualcheckup <- annualcheckup[!is.na(annualcheckup$lat) & !is.na(annualcheckup$long), ]

worldmap <- map_data("world")

ggplot() +
  geom_polygon(
    data = worldmap, 
    aes(x = long, y = lat, group = group), 
    fill = "lightblue", 
    color = "white"
  ) +
  geom_point(
    data = annualcheckup, 
    aes(x = long, y = lat, color = Data_Value), 
    size = 3,
    alpha = 0.7  
  ) +
  scale_color_viridis(option = "plasma", limits = c(0, 100), name = "Prevalence Rate (%)") +
  labs(
    title = "Annual Checkup Prevalence Map",
    x = "Longitude", 
    y = "Latitude"
  ) +
  coord_cartesian(xlim = c(-125, -65), ylim = c(24, 49)) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(size = 18, hjust = 0.5, margin = margin(b = 20)),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  ) +
  coord_fixed(1.3) +
  theme(panel.background = element_rect(fill = scales::viridis_pal()(100), color = "black"))

## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.

4. Refine your map to include a mousover tooltip

Refined map chunk here

annualcheckup$long <- as.numeric(annualcheckup$long)
annualcheckup$lat <- as.numeric(annualcheckup$lat)
annualcheckup <- annualcheckup[!is.na(annualcheckup$long) & !is.na(annualcheckup$lat),]

color_palette <- colorNumeric(palette = "viridis", domain = annualcheckup$Data_Value)

leaflet(annualcheckup) %>%
  addTiles() %>%  
  addCircles(
    lng = ~long, 
    lat = ~lat,
    radius = 5,
    color = ~color_palette(Data_Value),
    label = ~paste("Prevalence Rate:", Data_Value, "%"),
    popup = ~paste("City:", CityName, "<br>",
                   "Prevalence Rate:", Data_Value, "%")
  ) %>%
  addLegend(
    "bottomright",
    pal = color_palette,
    values = ~Data_Value,
    title = "Prevalence Rate",
    labFormat = labelFormat(suffix = "%")
  )

5. Write a paragraph

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

Using the filtered prevention dataset, I created several graphs solely focusing on the rate of doctor visits for routine checkups. At first it began with a disorganized scatterplot, which was difficult to understand and required a new visualization to be able to read it. The first map utilized the library(viridis) from week 3 to show a more appealing visualization with a plasma legend highlighting the lowest and highest rates of doctor visits, however I was unable to move the plots to the designated region it corresponded to. With the refined map, it provided interactivity in order to read the percentages and cities of the plots, however I was still unable to get the plots to hover over the designated region. In addition, I was unable to implement a similar legend to that of the first map.