Week 3 Challenge

Challenge Summary

This is a short challenge to begin applying what you are learning to the problem at hand. You will go through a series of questions related to the course project goals:

1. Coming up with a new product idea, and

2. Segmenting the customer-base

Objectives

1. Apply lubridate and stringr functions to answer questions related to the course projects.

2. Gain exposure to rmarkdown.

Data

To read the data, make sure that the paths point to the appropriate data sets. Saving the file in the “challenges folder” should enable the paths to be detected correctly.

# Load libraries
library(tidyverse)
library(lubridate)

# Read bike orderlines data
path_bike_orderlines <- "../00_data/bike_sales/data_wrangled/bike_orderlines.rds"
bike_orderlines_tbl <- read_rds(path_bike_orderlines) %>%
    
    # Fix typos found in Feature Engineering
    mutate(model = case_when(
        model == "CAAD Disc Ultegra" ~ "CAAD12 Disc Ultegra",
        model == "Syapse Carbon Tiagra" ~ "Synapse Carbon Tiagra",
        model == "Supersix Evo Hi-Mod Utegra" ~ "Supersix Evo Hi-Mod Ultegra",
        TRUE ~ model
    ))

glimpse(bike_orderlines_tbl)

## Rows: 15,644
## Columns: 13
## $ order_date     <dttm> 2011-01-07, 2011-01-07, 2011-01-10, 2011-01-10, 2011-0~
## $ order_id       <dbl> 1, 1, 2, 2, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7~
## $ order_line     <dbl> 1, 2, 1, 2, 1, 2, 3, 4, 5, 1, 1, 2, 3, 4, 1, 2, 3, 4, 1~
## $ quantity       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1~
## $ price          <dbl> 6070, 5970, 2770, 5970, 10660, 3200, 12790, 5330, 1570,~
## $ total_price    <dbl> 6070, 5970, 2770, 5970, 10660, 3200, 12790, 5330, 1570,~
## $ model          <chr> "Jekyll Carbon 2", "Trigger Carbon 2", "Beast of the Ea~
## $ category_1     <chr> "Mountain", "Mountain", "Mountain", "Mountain", "Road",~
## $ category_2     <chr> "Over Mountain", "Over Mountain", "Trail", "Over Mounta~
## $ frame_material <chr> "Carbon", "Carbon", "Aluminum", "Carbon", "Carbon", "Ca~
## $ bikeshop_name  <chr> "Ithaca Mountain Climbers", "Ithaca Mountain Climbers",~
## $ city           <chr> "Ithaca", "Ithaca", "Kansas City", "Kansas City", "Loui~
## $ state          <chr> "NY", "NY", "KS", "KS", "KY", "KY", "KY", "KY", "KY", "~

Questions

lubridate: Which month has the highest bike sales? (Difficulty = Medium)

• Start with bike_orderlines_tbl
• Select columns order_date and total_price
• Add a column called month
• Group by, summarize, and ungroup calculating the sales
• Arrange the sales values by month (Jan - Dec)
• Format the sales values as dollar()
• Adjust column names to title case

What does this tell us about a time of year to focus marketing efforts?

bike_orderlines_tbl %>%
    select(order_date, total_price) %>%
    mutate(month = order_date %>% floor_date("month") %>% month(label = TRUE)) %>%
    group_by(month) %>%
    summarise(sales = sum(total_price)) %>%
    ungroup() %>%
    mutate(sales = scales::dollar(sales)) %>%
    arrange(month) %>%
    
    rename(
        "Month" = month,
        "Sales" = sales
        )

stringr: What is the median orderline sales value by Bike Attribute? (Difficulty = Medium)

• Begin with bike_orderlines
• Select model and total_price
• Detect if string is present (e.g. “black inc”)
• Groupby, summarize, and ungroup calculating the median() orderline
• Format numeric price as dollar() (Hint: investigate largest_with_cents argument)
• Rename column to evaluation string (e.g. “Black Inc”)

Evaluate “Black Inc”. What does this tell us about the “Black Inc” feature?

bike_orderlines_tbl %>%
    select(model, total_price)%>%
    mutate(Black = model %>% str_detect("Black Inc") %>% as_factor()) %>%
    group_by(Black)%>%
    summarise(Median_Orderline = median(total_price))%>%
    ungroup() %>%
    mutate(Median_Orderline = scales::dollar(Median_Orderline))

Evaluate “Ultegra”. What does this tell us about the “Ultegra” feature?

bike_orderlines_tbl %>%
    select(model, total_price)%>%
    mutate(Ultegra = model %>% str_detect("Ultegra") %>% as_factor()) %>%
    group_by(Ultegra)%>%
    summarise(Median_Orderline = median(total_price))%>%
    ungroup() %>%
    mutate(Median_Orderline = scales::dollar(Median_Orderline))

Evaluate “Disc” option. What does this tell us about the “Disc” feature?

bike_orderlines_tbl %>%
    select(model, total_price)%>%
    mutate(Disc = model %>% str_detect("Disc") %>% as_factor()) %>%
    group_by(Disc)%>%
    summarise(Median_Orderline = median(total_price))%>%
    ungroup() %>%
    mutate(Median_Orderline = scales::dollar(Median_Orderline))

stringr: What are the average, min, and max prices by Base Model? (Difficulty = High)

• Start with bike_orderlines_tbl
• Select distinct primary category, secondary category, model, and price (unit price, not total price)
• Create the base feature, model_base (Hint: Use the Feature Engineering code)
  • separate the models
  • Create a base feature that combines the appopriate parts (e.g. “Beast of the East”)
• Remove any unnecessary columns (Hint: Deselect any columns matching "model_[0-9]")
• Group by, summarize, and ungroup (Hint: use mean(), min(), and max())
• Arrange descending by average price
• Format any numeric columns as dollar() (Hint: Check out largest_with_cents)
• Adjust the column names to title case

What does this tell us about how bikes are priced?

bike_orderlines_tbl %>%
    distinct(category_1, category_2, model, price)%>%
    separate(col    = model,
             into   = str_c("model_", 1:9),
             sep    = " ",
             remove = FALSE,
             fill   = "right",
             extra  = "drop"
             ) %>%
    
    #lets create a base model feature from column model
    mutate(model_base = case_when(
        
        #fix  Scalpel 29
        str_detect(str_to_lower(model_1), "scalpel") ~ str_c(model_1, model_2,  sep =" "),
        
        #fix Beast of the East
        str_detect(str_to_lower(model_1), "beast") ~ str_c(model_1, model_2, model_3, model_4,  sep =" "),
        TRUE ~ model_1)) %>%
    
    #mutate(model_tier =  model %>% str_replace(model_base) %>%
               #str_trim) %>%
    
    #remove unnecessary columns
    select(-(model_1:model_9)) %>%
    #select(-matches("[0-9]")) %>%
    group_by(category_1, category_2, model_base) %>%
    summarise(
        mean_price = mean(price),
        min_price  = min(price),
        max_price  = max(price)
    ) %>%
    ungroup() %>%
    mutate(
        mean_price = scales::dollar(mean_price, largest_with_cents = 1e+4),
        min_price  = scales::dollar(min_price, largest_with_cents = 1e+4),
        max_price  = scales::dollar(max_price, largest_with_cents = 1e+4)) %>%
    
    #arrange(mean_price)
    arrange(desc(mean_price)) 

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

    #set_names(names(.) %>% str_replace ("_", " ") %>% str_to_title()