The data file called $$countries$.$csv$$ in the DataForLabs folder contains information about all the countries on Earth. Each row is a country, and each column contains a variable.

Use read.csv$()$ to read the data from this file into a data frame called countries.

Use summary$()$ to get a quick description of this data set. What are the first three variables?

Using the output of summary$(),$ how many countries are from Africa in this data set?

What kinds of variables $($i$.$e$.,$ categorical or numerical$)$ are continents, cell$\_$phone$\_$subscriptions$\_$per$\_100\_$people$\_2012,$ total$\_$population$\_$in$\_$thousands$\_2015$ and fines$\_$for$\_$tobacco$\_$advertising$\_2014?($Don$$t go by their variable names $$ look at the data in the summary results to decide.$)$

Add a new column to your countries data frame that has the difference in ecological footprint between $2012$ and $2000.$ What is the mean of this difference? $($Note: this variable will have $$missing data$,$ which means that some of the countries do not have data in this file for one or the other of the years of ecological footprint. By default, R doesn$$t calculate a mean unless all the data are present. To tell R to ignore the missing data, add an option to the mean$()$ command that says na$.$rm$=$TRUE. We$$ll learn more about this later.$)$

$4.$ Using the countries data again, create a new data frame called AfricaData, that only includes data for countries in Africa. What is the sum of the total$\_$population$\_$in$\_$thousands$\_2015$ for this new data fram