Exercise $1$

Fill in the blanks to create three new variables named regular$\_$price, loyalty$\_$price, and coupon$\_$price

according to the following logic:

regular$\_$price $=($sales$\_$value $+$ retail$\_$disc $+$ coupon$\_$match$\_$disc$)/$ quantity

loyalty$\_$price $=($sales$\_$value $+$ coupon$\_$match$\_$disc$)/$ quantity

coupon$\_$price $=($sales$\_$value $-$ coupon$\_$disc$)/$ quantity

$...$ and then perform the following analyses:

$1.$ Identify the five households with the largest loyalty$\_$price transactions. What is unique about the

transaction with the largest loyalty$\_$price value?

$2.$ Now filter for only those observations where quantity was greater than $0.$ Now which household$($s$)$

have the largest loyalty$\_$price transaction?

$3.$ Using the first transaction in the result from #$2,$ filter the products data based on the product$\_$id

to find out which product the largest loyalty$\_$price transaction is associated with.

# Q$0$: Create three new variables named $`$regular$\_$price$`,`$loyalty$\_$price$`,$ and

# $`$coupon$\_$price$`$ according to the logic shown above

transactions $<-$ transactions $\%>\%$

mutate$($

regular$\_$price $=\_\_\_\_\_\_\_\_\_\_\_\_,$

loyalty$\_$price $=\_\_\_\_\_\_\_\_\_\_\_\_,$

coupon$\_$price $=\_\_\_\_\_\_\_\_\_\_\_\_$

$)\%>\%$

select$($regular$\_$price, loyalty$\_$price, coupon$\_$price, product$\_$id$,$ everything$())$

# Q$1.$ Identify the five households with the largest $`$loyalty$\_$price$`$ transactions. What

# is unique about the transaction with the largest $`$loyalty$\_$price$`$ value?

transactions $\%>\%$

slice$\_$max$($order$\_$by $=$ loyalty$\_$price, n $=\_\_)$

# Q$2.$ Now filter for only those observations where quantity was greater than $0.$ Now which

# household$($s$)$ have the largest $`$loyalty$\_$price$`$ transaction?

transactions $\%>\%$

filter$($quantity $\_\_0)\%>\%$

slice$\_$max$($order$\_$by $=\_\_\_\_\_\_,$ n $=\_\_)$

$2$

# Q$3.$ Using the first transaction in the result from #$2,$ filter the $`$products$`$ data based

# on the $`$product$\_$id$`$ to find out which product the largest $`$loyalty$\_$price$`$ transaction

# is associated with.

products $\%>\%$

filter$($product$\_$id $==\_\_\_\_\_\_\_)$

Exercise $2$

transactions includes $20,902$ unique product IDs. How many of these products $($not transactions!$)$ had a

regular price of one dollar or less? How many of these products had a loyalty price of one dollar or less?

How about a coupon price of one dollar or less?

Hint: After filtering, select the product$\_$id column and count unique products using the n$\_$distinct$()$

function.

# how many products had a regular price of $$1$ or less

transactions $\%>\%$

filter$($regular$\_$price $<=\_\_)\%>\%$

select$($product$\_$id$)\%>\%$

n$\_$distinct$()$

# how many products had a loyalty price of $$1$ or less

transactions $\%>\%$

filter$(\_\_\_\_\_\_\_\_\_)\%>\%$

select$($product$\_$id$)\%>\%$

$\_\_\_\_\_\_\_\_\_$

# how many products had a coupon price of $$1$ or less

transactions $\%>\%$

filter$(\_\_\_\_\_\_\_\_\_)\%>\%$

$\_\_\_\_\_\_\_\_\_\%>\%$

$\_\_\_\_\_\_\_\_\_$

Exercise $3$

What proportion of baskets are over $$10$ in sales value? What proportion of baskets are over $$20$ in sales

value?

Hint: You need to use group$\_$by$()$ and summarize$().$ Depending on your approach you may or may not

use mutate$().$

# test your ability to right this code from scratch rather than just

# filling in the blanks :$)$

$\_\_\_\_\_\_\_$

Exercise $4$

Which stores had the largest total sales$\_$value $($hint: sum$($sales$\_$value, na$.$rm $=$ TRUE$)?$ Which stores

had the largest average loyalty discount as defined below?

Hint: You can calculate loyalty discount as a percentage of regular price using the following logic: