### Note that the Code Grade virtual machine $($computer$)$ already has the Wine.csv file in its working directory.

#### Do not submit code with setwd$()$ or that has a path $($for example: Users$/$first$.$last$/$Desktop$)$ in it$.$

CG Q$1$a # How many observations $($rows$)$ are available to you? Use the nrow$()$ function.

CG Q$1$b # How many variables $($columns$)$ are collected on each rated wine? Use the ncol$()$ function.

$#### Io is important to find the exact spellings of column names and entries within columns in the wine dataframe.

CG Q$2$a $$ Use the names$()$ or str$()$ function on the wine dataframe to view the exact spellings of the column names. ##### You will use this strategy of "looking" in a column to get exact sepllings to answer questions on this quiz.

##### It is important to know whether there are missing values $($NAs$)$ in your data

#### since NAs need to be addressed for certain functions to work correctly.

# CG Q$3$a # Use the sum$()$ and

is$.$na$()$ functions to find the number of missing values in the wine dataframe.

# CG Q$3$b # Use the sum$()$ and

is$.$na$()$ functions to count the number of missing values in the points column.

# CG Q$3$C # Use the sum$()$ and

is$.$na$()$ functions to count the number of missing values in the price column.

# CG Q$3$d # Use the sum$()$ and

is$.$na$()$ functions to count the number of observations that have a price.

##### Hint: Since the $!$ means "not", putting an$!$ in front of

is$.$na$()$ counts the non$-$missing values.

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

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

# CG Q$5$a # 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..

# CG Q$6$ # Now we$\text{'}11$ 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.

# CG Q$7$a # Now we$\text{'}11$ 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.