$1.)$ Consider the CO$2$ Emissions project. Imagine the CO$2\_$and$\_$Oil dataset is recorded by month instead of by year. For each observation of a country, there is a column storing the month $($such as January$)$ and a column storing the year $($such as $2012).$ What visual recipe could be used to aggregate the data into yearly totals? Join Group Stack Prepare $2.)$ Consider the CO$2$ Emissions project. Which of these visual recipes can NOT be used to restrict the datasets in the project to the years $2008-2012?$ A Sample$/$Filter recipe A Sync recipe A filter processor in a Prepare recipe The pre$-$filter step of a Join recipe $3.)$ Use the CO$2$ Emissions project to answer this question. What country has the $5$th highest CO$2$ emissions per capita for the year $2010?($Hint: You can use options from the context menu of the column headers since the entire data fits in the dataset sample$)$ Bahrain Trinidad and Tobago Saudi Arabia Luxembourg $4.)$ Consider the CO$2$ Emissions project. Imagine the CO$2\_$and$\_$Oil dataset is recorded by month instead of by year. For each observation of a country, there is a column storing the month $($such as January$)$ and a column storing the year $($such as $2012).$ What visual recipe could be used to aggregate the data into yearly totals? Stack Join Prepare Group $5.)$ You are working with two datasets. One contains listings of Homes$\_$For$\_$Sale and the other contains a listing of Realtors for which you have contact information. You want to create a new dataset that contains only the homes for sale that also have a realtor for whom you have contact information. How can you accomplish this in the Join recipe? A Left join type with Homes$\_$For$\_$Sale on the left. A Cross join type with Homes$\_$For$\_$Sale on the left. An Inner join type. A Left join type with Realtors on the left