B2 Enhancing tables with geography

Vincent J. Carey, stvjc at channing.harvard.edu

April 06, 2023

Source: vignettes/B2_table_plus_geo.Rmd

Basic geographic data about US counties

The coordinates of central locations in US counties are provided in us_county_geo.

data(us_county_geo)
us_county_geo |> select(state, county, geometry) |> head()
## Simple feature collection with 6 features and 2 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -87.74607 ymin: 30.65922 xmax: -85.4051 ymax: 33.97736
## CRS:           NA
## # A tibble: 6 × 3
##   state county                     geometry
##   <chr> <chr>                       <POINT>
## 1 AL    Autauga County (-86.64644 32.53224)
## 2 AL    Baldwin County (-87.74607 30.65922)
## 3 AL    Barbour County  (-85.4051 31.87025)
## 4 AL    Bibb County    (-87.12715 33.01589)
## 5 AL    Blount County  (-86.56644 33.97736)
## 6 AL    Bullock County (-85.71726 32.10176)

Coordinates for metropolitan statistical areas are provided by “statcrunch”:

data(statcrunch_msa)
head(statcrunch_msa)
##   rank                                         msa  pop2013  pop2020 changepct
## 1    1       New York-Newark-Jersey City, NY-NJ-PA 19949502 19567410      1.95
## 2    2          Los Angeles-Long Beach-Anaheim, CA 13131431 12828837      2.36
## 3    3          Chicago-Naperville-Elgin, IL-IN-WI  9537289  9461105      0.81
## 4    4             Dallas-Fort Worth-Arlington, TX  6810913  6426214      5.99
## 5    5        Houston-The Woodlands-Sugar Land, TX  6313158  5920416      6.63
## 6    6 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD  6034678  5965343      1.16
##   changecat      lat        lng
## 1    0 to 5 40.71278  -74.00594
## 2    0 to 5 34.05223 -118.24368
## 3    0 to 5 41.87811  -87.62980
## 4   5 to 10 32.78014  -96.80045
## 5   5 to 10 29.76019  -95.36939
## 6    0 to 5 39.95233  -75.16379

This geographic data has already been used to enhance the CDC incidence data in woncan:

data(woncan)
woncan |> group_by(MSA) |> 
    summarize(lat=head(lat)[1], lng=head(lng)[1]) |> head()
## # A tibble: 6 × 3
##   MSA                                 lat    lng
##   <chr>                             <dbl>  <dbl>
## 1 Akron, OH                          41.1  -81.5
## 2 Albany-Schenectady-Troy, NY        42.7  -73.8
## 3 Albuquerque, NM                    35.1 -107. 
## 4 Allentown-Bethlehem-Easton, PA-NJ  40.6  -75.5
## 5 Atlanta-Sandy Springs-Roswell, GA  33.7  -84.4
## 6 Augusta-Richmond County, GA-SC     33.5  -82.0

Exercise

B.2.1 Create a new notebook cell and run YESCDS::table_woncan("Prostate")

B.2.2 Use this table to find the latitude and longitude of Boston.

Answers 

B.2.1

B.2.2

An interactive map of the US with cancer rates

After combining cancer rate data for a collection of cancer types and metropolitan statistical areas, we can produce a map showing variation in cancer incidence over the United States. Here is the example for stomach cancer:

cancer_map_usa(site="Stomach")

Exercise

B.2.3 Create a new notebook cell and run cancer_map_usa(site="Prostate", scaling=0.05)

B.2.4 Can you identify the area with the highest incidence of prostate cancer?

Answers 

B.2.3

B.2.4

A data graphic

Finally, to get a different view of variation in cancer rates across the United States, run a cell with the following command. Geographic location is lost, but the range of variation, and details of variation can be seen very clearly in these displays.

browseURL("https://vjcitn.shinyapps.io/ratevariation")

Exercise

B.2.5 Change the body site to “Pancreas” (remove “Brain”) and find, using the histogram tab, the areas with highest and lowest age-adjusted rates of pancreatic cancer.

Answer 

B.2.5