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The Local Area Unemployment Statistics page from the Bureau of Labor Statistics makes available county-level monthly unemployment data for a 14-month window. The file for November 2017 through December 2018 is available at http://www.stat.uiowa.edu/~luke/data/laus/laucntycur14-2018.txt.
One way to read the data into R is:
lausURL <- "http://www.stat.uiowa.edu/~luke/data/laus/laucntycur14-2018.txt" lausUS <- read.table(lausURL, col.names = c("LAUSAreaCode", "State", "County", "Title", "Period", "LaborForce", "Employed", "Unemployed", "UnempRate"), quote = '"', sep = "|", skip = 6, stringsAsFactors = FALSE, strip.white = TRUE, fill = TRUE) footstart <- grep("------", lausUS$LAUSAreaCode) lausUS <- lausUS[1:(footstart - 1),]
grep functions may be useful for cleaning the data.
In these problems you will be working with this unemployment data. For each of the following problems
describe any interesting features your plots reveal;
choose an appropriate color palette and explain your palette choice.
Create a choropleth map of the average state unemployment rates over the period covered by the data. In computing the averages keep in mind that the workforce sizes differ across counties.
State variable in the
lausUS data frame is the state FIPS code. The
state.fips data frame in the
maps package can be used to map state names to FIPS codes. One possible approach to attaching state FIPS codes to the map data needed for
state.fips <- select(maps::state.fips, fips, region = polyname) %>% mutate(region = sub(":.*", "", region)) %>% unique() gusa <- left_join(gusa, state.fips, "region")
For the months of March, June, September, and December of 2018 create a choropleth map of the Iowa county unemployment rates for each of these months in a faceted display.
County column of the
lausUS data frame contains the county portions of the county FIPS codes. The full county FIPS codes can be computed as
with(lausUS, 1000 * State + County)
Create a choropleth map of the difference between the Iowa county unemployment rates in Dec 2018 and December 2017.