Using R coding please help: The tidyr package contains tables named table$1,$ table$2,$ and table $4a+$ table $4b,$ which show the same tuberculosis data organized in four different ways. Each dataset shows the same four variables $-$ country, year, population, and cases $-$ but each dataset organizes the values in a different way. Let us define a new variable rate$10$K that gives the number of TB infections per $10,000$ people:

rate$10K=(cases\frac{?}{?}$ population$)*10000$

a$)$ For each of table$1,$ table$2,$ and table $4a+$ table $4b$ write a separate piece of $R$ code to create a data frame that contains three columns: country, year, and ratel $10$ k $.$

b$)$ Which of the tables was easiest to work with? Which was the hardest? Why?

$4.$ Create a narrow tidy dataset from the quarterly property tax payment data in the file "Homework$6\_$Problem$4.$txt$"$ that accompanies this homework on Moodle. Your final data frame should contain only columns named LotNumber, Street, City, State, Quarter, and Taxes. The end result should look like this:

$\backslash$table$[[,\backslash$table$[[$LotNumber Street$],[$