mean$($winespornts$)$

# CG Q$4$b # Use the mean$()$ function to calculate the average price for the wines in the dataset.

mean $($wine$price, na$.$rm$=$T$)$

# CG Q$5$a # Use the mean$()$ function to calculate the average points for the Napa Sonoma

sub$-$region.

$33$

$34$

subsetting. $.$

# CG Q$5$b # Use the mean$()$ function to calculate the average price for the Napa Sonoma sub$-$region using a single line of code.

# CG Q$6$ # Now we'll practice our counting skills again.

##### The rating scale being used by these tasters orignally had $91$ as the highest possible score

$39$

$40$

$41$

$42$

$43$

$44$

$45$

$46$

$47$

$48$

$49$

$50$

##### and now goes to $100$ for exceedingly delicious wines.

##### Use the sum$()$ function on a logical vector that checks for whether points are greater than $91$ or not

##### to count the number of wine ratings with more than $91$ points.

f$.$

# CG Q$7$a # Now we'll practice subsetting on more than one criteria.

##### Use the mean$()$ function to compute the average points for wines

###### that are chardonnay and from the Napa Sonoma sub$-$region.

# CG Q$7$b # More practice subsetting on more than one criteria.

##### Use the mean$()$ function to compute the average price for wines that are ###### either chardonnay or are from the finger lakes sub$-$region.

# CG Q$8$ # Use the median$()$ function to find the median price for

##### a Cabernet Sauvignon that is rated higher than $91$ points.