Week12_class_exercise.R

# In class exercise: Generate quantity sales and percentage by bikeshop_name
## Hints: select(bikeshop_name, catogery_1, catogery_2, quantity )
  
  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

  path_bike_orderlines <- "bike_orderlines.rds"
  bike_orderlines_tbl <- readRDS(path_bike_orderlines)
  
  grouped_data <- bike_orderlines_tbl %>%
    group_by(bikeshop_name) %>%
    summarise(total_quantity = sum(quantity), count = n()) %>%
    mutate(percentage = (total_quantity / sum(total_quantity)) * 100)
  
  print(grouped_data)

## # A tibble: 30 × 4
##    bikeshop_name            total_quantity count percentage
##    <chr>                             <dbl> <int>      <dbl>
##  1 Albuquerque Cycles                  286   207       1.42
##  2 Ann Arbor Speed                     602   461       2.98
##  3 Austin Cruisers                     246   192       1.22
##  4 Cincinnati Speed                    391   298       1.94
##  5 Columbus Race Equipment             394   296       1.95
##  6 Dallas Cycles                       234   182       1.16
##  7 Denver Bike Shop                   2301  1801      11.4 
##  8 Detroit Cycles                      504   373       2.50
##  9 Indianapolis Velocipedes            319   231       1.58
## 10 Ithaca Mountain Climbers           1264   975       6.27
## # ℹ 20 more rows