library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readxl)
library(writexl)

# 2. Data import
# ./, ../, 
bikes_tbl <- read_excel("C:/Users/dell/OneDrive/Documents/bikes.xlsx") # fast key: alt+-
bikeshops_tbl <- read_excel("C:/Users/dell/OneDrive/Documents/bikeshops.xlsx")
orderlines_tbl <- read_excel("C:/Users/dell/OneDrive/Documents/orderlines.xlsx")
## New names:
## • `` -> `...1`
# Examine data:
bikes_tbl
## # A tibble: 97 × 4
##    bike.id model                          description                price
##      <dbl> <chr>                          <chr>                      <dbl>
##  1       1 Supersix Evo Black Inc.        Road - Elite Road - Carbon 12790
##  2       2 Supersix Evo Hi-Mod Team       Road - Elite Road - Carbon 10660
##  3       3 Supersix Evo Hi-Mod Dura Ace 1 Road - Elite Road - Carbon  7990
##  4       4 Supersix Evo Hi-Mod Dura Ace 2 Road - Elite Road - Carbon  5330
##  5       5 Supersix Evo Hi-Mod Utegra     Road - Elite Road - Carbon  4260
##  6       6 Supersix Evo Red               Road - Elite Road - Carbon  3940
##  7       7 Supersix Evo Ultegra 3         Road - Elite Road - Carbon  3200
##  8       8 Supersix Evo Ultegra 4         Road - Elite Road - Carbon  2660
##  9       9 Supersix Evo 105               Road - Elite Road - Carbon  2240
## 10      10 Supersix Evo Tiagra            Road - Elite Road - Carbon  1840
## # ℹ 87 more rows
head(bikes_tbl)
## # A tibble: 6 × 4
##   bike.id model                          description                price
##     <dbl> <chr>                          <chr>                      <dbl>
## 1       1 Supersix Evo Black Inc.        Road - Elite Road - Carbon 12790
## 2       2 Supersix Evo Hi-Mod Team       Road - Elite Road - Carbon 10660
## 3       3 Supersix Evo Hi-Mod Dura Ace 1 Road - Elite Road - Carbon  7990
## 4       4 Supersix Evo Hi-Mod Dura Ace 2 Road - Elite Road - Carbon  5330
## 5       5 Supersix Evo Hi-Mod Utegra     Road - Elite Road - Carbon  4260
## 6       6 Supersix Evo Red               Road - Elite Road - Carbon  3940
# Import csv file:
bike_orderlines_tbl <- read_csv("C:/Users/dell/OneDrive/Documents/bike_orderlines.csv")
## Rows: 15644 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (7): model, category_1, category_2, frame_material, bikeshop_name, city...
## dbl  (5): order_id, order_line, quantity, price, total_price
## dttm (1): order_date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Joining data: 
orderlines_bikes_tbl <- left_join(orderlines_tbl, bikes_tbl, by = c("product.id" = "bike.id"))

bike_orderlines_bikeshops_joined <- left_join(orderlines_bikes_tbl, bikeshops_tbl, 
                                               by = c('customer.id' = 'bikeshop.id'))


# %>% is called pipe: fast key: ctl + shift + m 
bike_orderlines_bikeshops_joined <- left_join(orderlines_tbl, bikes_tbl, by = c("product.id" = "bike.id")) %>% 
                                    left_join(bikeshops_tbl, by = c("customer.id" = "bikeshop.id"))

# Wrangling data: decompose description into three columns: category.1, category.2 and frame.material
bike_orderlines_wrangled_tbl <- bike_orderlines_bikeshops_joined %>% 
                                  separate(description, 
                                           into = c('category.1', 'category.2', 'frame.material'), 
                                           sep  = ' - ') %>% 
                                  separate(location, 
                                           into = c('city', 'state'), 
                                           sep  = ', ',
                                           remove = FALSE)

# Show model and price in descending order

bikes_desc <- bike_orderlines_bikeshops_joined %>%
  select(model, price) %>%
  arrange(desc(price))

# Show model and price where price is greater than mean price

mean_price <- mean(bike_orderlines_bikeshops_joined$price, na.rm = TRUE)

bikes_greater_than_mean <- bike_orderlines_bikeshops_joined %>%
  select(model, price) %>%
  filter(price > mean_price)


View(bikes_desc <- bike_orderlines_bikeshops_joined %>%
        select(model, price) %>%
        arrange(desc(price))
      )

View(bikes_greater_than_mean <- bike_orderlines_bikeshops_joined %>%
        select(model, price) %>%
        filter(price > mean_price))