suppressMessages({ suppressPackageStartupMessages({ library(YESCDS) library(tibble) library(dplyr) library(plotly) library(ggplot2) library(ggbeeswarm) library(DT) data(woncan_meta) data(woncan) library(sf) littab = woncan |> select(MSA, `Cancer Sites`, Age.Adjusted.Rate) |> as.data.frame() }) })
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()
Coordinates for metropolitan statistical areas are provided by "statcrunch":
data(statcrunch_msa) head(statcrunch_msa)
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()
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.
B.2.1 B.2.2
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")
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?
B.2.3 B.2.4
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")
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.
B.2.5
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