hw3

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(stringr)
library(scales)

bike_orderline_tbl <- read.csv("bike_orderlines.csv")
bike_orderline_tbl %>%
  group_by(category_1, category_2, frame_material) %>%
  summarise(Sales = sum(total_price, na.rm = TRUE)) %>%
  mutate(Sales = dollar(Sales)) %>% 
  rename(
   ' Prime category' = category_1,      
   ' Secondary category' = category_2,  
   ' Frame Material' = frame_material   
  )

## `summarise()` has grouped output by 'category_1', 'category_2'. You can
## override using the `.groups` argument.

## # A tibble: 13 × 4
## # Groups:   Prime category, Secondary category [9]
##    ` Prime category` ` Secondary category` ` Frame Material` Sales      
##    <chr>             <chr>                 <chr>             <chr>      
##  1 Mountain          Cross Country Race    Aluminum          $3,318,560 
##  2 Mountain          Cross Country Race    Carbon            $15,906,070
##  3 Mountain          Fat Bike              Aluminum          $1,052,620 
##  4 Mountain          Over Mountain         Carbon            $7,571,270 
##  5 Mountain          Sport                 Aluminum          $1,932,755 
##  6 Mountain          Trail                 Aluminum          $4,537,610 
##  7 Mountain          Trail                 Carbon            $4,835,850 
##  8 Road              Cyclocross            Carbon            $2,108,120 
##  9 Road              Elite Road            Aluminum          $5,637,795 
## 10 Road              Elite Road            Carbon            $9,696,870 
## 11 Road              Endurance Road        Aluminum          $1,612,450 
## 12 Road              Endurance Road        Carbon            $8,768,610 
## 13 Road              Triathalon            Carbon            $4,053,750