# CG $Q0$ # 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$.$

# 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.

####### It 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.

# CG Q$2$b # Use the summary$()$ function on the taster twitter handle column to view the exact spellings of the tasters' twitter handles.

###### 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.