# Q0 # Use the read.csv() function to read in the Wine.csv file. Name it wine and don't forget to use the strings=T argument. ###### Note that the Code Grade virtual machine (computer) already has the Wine.csv file in its working directory. ###### When submitting to code grade, use a read.csv() statement that assumes the file is already in the working directory. ###### Do not submit code with setwd() or that has a path (for example: Users/first.last/Desktop) in it.

# Q4b # Use the mean() function to calculate the average price for the wines in the dataset.

# Q5a # Use the mean() function to calculate the average points for the Napa Sonoma sub-region. ####### Your code should be a single line and will use the square brackets for subsetting..

# Q5b # Use the mean() function to calculate the average price for the Napa Sonoma sub-regionusing a single line of code.

# Q6 # Now we'll practice our counting skills again. ###### The rating scale being used by these tasters orignally had 91 as the highest possible score ###### 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. # Q7a # 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.

# Q7b # 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.

# Q8 # Use the median() function to find the median price for ###### a Cabernet Sauvignon that is rated higher than 91 points.