4 Data exploration and manipulation The task is to explore the US census population estimates by county for 2015 from the package usmap (load the data frame from C0untyp0p.RData ). The data frame has 3142 rows and 4 variables: 'Fips is the 5-digit FIPS code corresponding to the county; abbr' is the 2- letter state abbreviation; county is the full county name; pop_2@15 is the 2015 population estimate (in number of people) for the corresponding county. Each row of the data frame represents a different county or a county equivalent. For the sake of simplicity, when we say a county, that also includes a county equivalent and when we say a state, that also includes the District of Columbia. Answer the following questions. a. Remove all the rows that contain at least one NA (1 point). b. How many unique 2-letter state abbreviations are there (1 point)? c. What is the total number of counties in the US (2 point)? d. How many unique county names are there (1 point)? e. What are the top 10 most common county names (1 point)? f. Which state has the largest number of counties? Which state has the smallest number of counties (1 point)? g. What is the average population of a county in the US (1 point)? h. Which state has the largest county in terms of population? How many people live in the largest county in terms of population (2 points)? i. What is the largest county in terms of population of each of the states (2 points)? j. What is the average population of the 100 largest counties in the US (2 points)? k. How many people live in each of the states (2 points)? |. What is the average population of a county in California (1 point)? load('countypop.RData') head(countypop) ## tips abbr county pop_2615 ## 1 81661 AL Autauga County 5534? #1? 2 81033 AL Baldwin County 253789 ## 3 AL Barbour County 26489 #1? 4 81667 AL Bibb County 22583 #1? 5 91069 AL Blount County 57673 ## 6 @1911 AL Bullock County 10696