$1.$ Consider the $`$POPULATION$`$ data frame.

a$.$ Filter for all observations within the $`$State$`$ variable with a value of Indiana. Save this variable into an object called INDIANA. Print the first $10$ rows.

b$.$ Using the $`$INDIANA$`$ data frame, use the pivot longer function to make the data set into long form. All of the year columns $(`$X$2010`-`$X$2019`)$ should become one column while all the values should become another column. Choose appropriate names for the variable name. Save the long form data set into an object called $`$INDIANA$.$LONG$`.$ Sort by the variable "POP". Print rows $75-83.$

i$.*$Extra Point$*-$ Remove the X at the beginning of each variable name within the pivot$\_$longer function using extra arguments. $($Adding extra functions to remove X will not grant the extra point.$)$

c$.$ Turn the $`$INDIANA$.$LONG$`$ data frame back into wide format using pivot$\_$wider. Call this data frame $$INDIANA$.$WIDE$.$ Print the ten largest populations only in $2015$ in order from largest to smallest.

$2.$ This question requires no code, but include written$/$typed responses to the following in your RMarkdown PDF output:

Dataset A has $70$ unique ID rows of data and dataset B has $58$ unique ID rows of data. Dataset C has $100$ rows of data but only $72$ unique IDs.

a$.$ If you were to left join dataset A and B by ID$,$ how many rows would the joined dataset have?

b$.$ If you were to right join dataset A and B by ID$,$ how many rows would the joined dataset have?

c$.$ If you were to left join dataset A and C by ID$,$ how many rows would the joined dataset have?

d$.$ If you were to right join data B and C by ID$,$ how many rows would hte joined dataset have?

e$.$ Name the three requirements satisfied by tidy data.

$3.$ Consider the DRINKS data set.

a$.$ Filter to only contain data from the countries of USA, China, and Italy.

b$.$ Remove the $`$total$\_$litres$\_$of$\_$pure$\_$alcohol$`$ variable, and rename the following columns: $`$beer$\_$servings$`$ to $`$beer$`,`$spirit$\_$servings$`$ to $`$spirit$`,$ and $`$wine$\_$servings$`$ to $`$wine$`.$

c$.$ Converts the new subset from wide format to long format.

i$.$ Convert the variables $($column names$)$ into a columns named $`$type$`$ while the numerical values are a variable called $`$servings$.`$