LA still second in national unemployment
Unemployment rates have remained dismal in Los Angeles, according to the latest unemployment report by the U.S. Labor Department. The LA metro area has remained second in unemployment among most populated US metropolitan areas from prior to the recession until now, but that isn't the whole story.
While LA did drop from first to second nationwide in the last decade, it has also pulled away from the mean unemployment rates among the America's largest population hubs. LA appears to be weathering the recession worse than most major population centers in the US.
Our code
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| # Once we loaded our data, we made a plan for what we wanted to graph | |
| # To plot the current unemployment data in the top metro areas in the counrty, we made a variable for the current state of unemployment | |
| now <- subset(data, Year == 2013 & Month == 7) | |
| # Next we created a list of areas to focus on, using the full length "Area" names | |
| cities <- c("New York-Northern New Jersey-Long Island, NY-NJ-PA MSA", "Los Angeles-Long Beach-Santa Ana, CA MSA", "Chicago-Joliet-Naperville, IL-IN-WI MSA", "Dallas-Fort Worth-Arlington, TX MSA", "Houston-Sugar Land-Baytown, TX MSA", "Philadelphia-Camden-Wilmington, PA-NJ-DE-MD MSA", "Washington-Arlington-Alexandria, DC-VA-MD-WV MSA", "Miami-Fort Lauderdale-Pompano Beach, FL MSA", "Atlanta-Sandy Springs-Marietta, GA MSA", "Boston-Cambridge-Quincy, MA-NH Met NECTA", "San Francisco-Oakland-Fremont, CA MSA") | |
| # From there, we made a subset of our variable for the right Areas | |
| now2 <- subset(now, Area%in%cities) | |
| # Then we put them in order | |
| now2 <- now2[order(now2$Unemployment.Rate, decreasing=T),] | |
| # view "now2" to get the order of cities so we can lable them in the our plot | |
| # Then we plot it, adding in the correct area names | |
| barplot(now2$Unemployment.Rate, col="orange", main="Current Unemployment in Largest US Metro Areas", ylab="Unemployment Rate (%)", ylim=c(0, 8), names.arg=c("CHI", "LAX", "ATL", "PHI", "NYC", "MIA", "BOS", "SFO", "DAL", "HOU", "WSH"), cex.names=0.6) | |
| # We then repeated the whole process for 2007 | |
| then <- subset(data, Year == 2007 & Month == 7) | |
| then2 <- subset(then, Area%in%cities) | |
| then2 <- then2[order(then2$Unemployment.Rate, decreasing=T),] | |
| barplot(then2$Unemployment.Rate, col="green", main="Unemployment in Largest US Metro Areas in 2007", ylab="Unemployment Rate (%)", ylim=c(0, 8), names.arg=c("CHI", "LAX", "ATL", "NYC", "SFO", "PHI", "DAL", "HOU", "BOS", "MIA", "WSH"), cex.names=0.6) | |
| # Then we repeated once more for 2000 | |
| backthen <- subset(data, Year == 2000 & Month == 7) | |
| backthen2 <- subset(backthen, Area%in%cities) | |
| backthen2 <- backthen2[order(backthen2$Unemployment.Rate, decreasing=T),] | |
| barplot(backthen2$Unemployment.Rate, col="yellow", main="Unemployment in Largest US Metro Areas in 2000", ylab="Unemployment Rate (%)", ylim=c(0, 8), names.arg=c("LAX", "MIA", "NYC", "CHI", "HOU", "PHI", "DAL", "SFO", "ATL", "WSH", "BOS"), cex.names=0.6) |