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("./bikes.xlsx") # fast key: alt+-
bikeshops_tbl <- read_excel("./bikeshops.xlsx")
orderlines_tbl <- read_excel("./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("./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) %>%
# create calculated columns
mutate(total.price = price * quantity) %>%
# Reorganize columns
select(-...1, -location) %>%
# Reorder columns
select(contains('date'), contains('id'),
contains('order'),
quantity, price, total.price,
everything()) %>%
# Rename columns
rename(order_date = order.date) %>%
set_names(names(.) %>% str_replace_all("\\.", "_"))
# save the file as RDS
saveRDS(bike_orderlines_wrangled_tbl, './bike_orderlines.rds')
# dplyr and tidyr
# pull() vs. select()
bike_orderlines_wrangled_tbl %>%
# select(total_price)
pull(total_price) %>%
mean()
## [1] 4540.548
# select_if
bike_orderlines_wrangled_tbl %>%
# select_if(is.character)
select_if(is.numeric)
## # A tibble: 15,644 × 7
## order_id customer_id product_id order_line quantity price total_price
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 2 48 1 1 6070 6070
## 2 1 2 52 2 1 5970 5970
## 3 2 10 76 1 1 2770 2770
## 4 2 10 52 2 1 5970 5970
## 5 3 6 2 1 1 10660 10660
## 6 3 6 50 2 1 3200 3200
## 7 3 6 1 3 1 12790 12790
## 8 3 6 4 4 1 5330 5330
## 9 3 6 34 5 1 1570 1570
## 10 4 22 26 1 1 4800 4800
## # ℹ 15,634 more rows
# arrange() and desc()
bikes_tbl %>%
select(model, price) %>%
arrange(desc(price))
## # A tibble: 97 × 2
## model price
## <chr> <dbl>
## 1 Supersix Evo Black Inc. 12790
## 2 Scalpel-Si Black Inc. 12790
## 3 Habit Hi-Mod Black Inc. 12250
## 4 F-Si Black Inc. 11190
## 5 Supersix Evo Hi-Mod Team 10660
## 6 Synapse Hi-Mod Disc Black Inc. 9590
## 7 Scalpel-Si Race 9060
## 8 F-Si Hi-Mod Team 9060
## 9 Trigger Carbon 1 8200
## 10 Supersix Evo Hi-Mod Dura Ace 1 7990
## # ℹ 87 more rows
# filter()
bikes_tbl %>%
select(model, price) %>%
filter(price > mean(price))
## # A tibble: 35 × 2
## model price
## <chr> <dbl>
## 1 Supersix Evo Black Inc. 12790
## 2 Supersix Evo Hi-Mod Team 10660
## 3 Supersix Evo Hi-Mod Dura Ace 1 7990
## 4 Supersix Evo Hi-Mod Dura Ace 2 5330
## 5 Supersix Evo Hi-Mod Utegra 4260
## 6 CAAD12 Black Inc 5860
## 7 CAAD12 Disc Dura Ace 4260
## 8 Synapse Hi-Mod Disc Black Inc. 9590
## 9 Synapse Hi-Mod Disc Red 7460
## 10 Synapse Hi-Mod Dura Ace 5860
## # ℹ 25 more rows
bikes_tbl %>%
select(model, price) %>%
filter((price > 5000) & (price < 10000)) %>%
arrange(desc(price))
## # A tibble: 22 × 2
## model price
## <chr> <dbl>
## 1 Synapse Hi-Mod Disc Black Inc. 9590
## 2 Scalpel-Si Race 9060
## 3 F-Si Hi-Mod Team 9060
## 4 Trigger Carbon 1 8200
## 5 Supersix Evo Hi-Mod Dura Ace 1 7990
## 6 Jekyll Carbon 1 7990
## 7 Synapse Hi-Mod Disc Red 7460
## 8 Scalpel-Si Hi-Mod 1 7460
## 9 Habit Carbon 1 7460
## 10 Slice Hi-Mod Black Inc. 7000
## # ℹ 12 more rows
bikes_tbl %>%
select(model, price) %>%
filter(price > 6000,
model %>% str_detect("Supersix"))
## # A tibble: 3 × 2
## model price
## <chr> <dbl>
## 1 Supersix Evo Black Inc. 12790
## 2 Supersix Evo Hi-Mod Team 10660
## 3 Supersix Evo Hi-Mod Dura Ace 1 7990
# Filtering one or more conditions using == and %in%
bike_orderlines_wrangled_tbl %>%
filter(category_2 %in% c("Over Mountain", "Trail", "Endurance Road")) %>%
View()
# slice()
bikes_tbl %>%
arrange(desc(price)) %>%
# slice(1:5)
slice((nrow(.)-4):nrow(.))
## # A tibble: 5 × 4
## bike.id model description price
## <dbl> <chr> <chr> <dbl>
## 1 93 Trail 5 Mountain - Sport - Aluminum 815
## 2 94 Catalyst 1 Mountain - Sport - Aluminum 705
## 3 95 Catalyst 2 Mountain - Sport - Aluminum 585
## 4 96 Catalyst 3 Mountain - Sport - Aluminum 480
## 5 97 Catalyst 4 Mountain - Sport - Aluminum 415
# distinct(): extract unique values from data
bike_orderlines_wrangled_tbl %>%
distinct(category_1, category_2) %>%
View()
# mutate(): add new columns in our data
bike_orderlines_wrangled_tbl %>%
mutate(total_price_log = log(total_price)) %>%
mutate(total_price_sqrt = total_price^0.5) %>%
View()
# Binning with ntile()
bike_orderlines_wrangled_tbl %>%
mutate(total_price_binned = ntile(total_price, 3)) %>%
View()
# case_when(): provide flexible conditions for grouping (binning)
bike_orderlines_wrangled_tbl %>%
mutate(total_price_binned = ntile(total_price, 3)) %>%
mutate(total_price_binned2 = case_when(
total_price > quantile(total_price, 0.75) ~ "High",
total_price > quantile(total_price, 0.25) ~ "Medium",
TRUE ~ "Low"
)) %>%
View()
# Grouping and summarizing with group_by() and summarize()
bike_orderlines_wrangled_tbl %>%
summarise(revenue = sum(total_price))
## # A tibble: 1 × 1
## revenue
## <dbl>
## 1 71032330
bike_orderlines_wrangled_tbl %>%
group_by(category_1) %>%
summarise(revenue = sum(total_price)) %>%
ungroup() %>%
arrange(desc(revenue))
## # A tibble: 2 × 2
## category_1 revenue
## <chr> <dbl>
## 1 Mountain 39154735
## 2 Road 31877595
#
bike_orderlines_wrangled_tbl %>%
group_by(category_1, category_2, frame_material) %>%
summarise(revenue = sum(total_price)) %>%
ungroup() %>%
arrange(desc(revenue))
## `summarise()` has grouped output by 'category_1', 'category_2'. You can
## override using the `.groups` argument.
## # A tibble: 13 × 4
## category_1 category_2 frame_material revenue
## <chr> <chr> <chr> <dbl>
## 1 Mountain Cross Country Race Carbon 15906070
## 2 Road Elite Road Carbon 9696870
## 3 Road Endurance Road Carbon 8768610
## 4 Mountain Over Mountain Carbon 7571270
## 5 Road Elite Road Aluminum 5637795
## 6 Mountain Trail Carbon 4835850
## 7 Mountain Trail Aluminum 4537610
## 8 Road Triathalon Carbon 4053750
## 9 Mountain Cross Country Race Aluminum 3318560
## 10 Road Cyclocross Carbon 2108120
## 11 Mountain Sport Aluminum 1932755
## 12 Road Endurance Road Aluminum 1612450
## 13 Mountain Fat Bike Aluminum 1052620
# summarize_all()
# Q1: What are the unique categories of products?
bike_orderlines_wrangled_tbl %>% distinct(category_1)
## # A tibble: 2 × 1
## category_1
## <chr>
## 1 Mountain
## 2 Road
bike_orderlines_wrangled_tbl %>% distinct(category_2)
## # A tibble: 9 × 1
## category_2
## <chr>
## 1 Over Mountain
## 2 Trail
## 3 Elite Road
## 4 Endurance Road
## 5 Sport
## 6 Cross Country Race
## 7 Cyclocross
## 8 Triathalon
## 9 Fat Bike
bike_orderlines_wrangled_tbl %>% distinct(frame_material)
## # A tibble: 2 × 1
## frame_material
## <chr>
## 1 Carbon
## 2 Aluminum
# Q2: Which product categories have the largest sales?
# category_1
bike_orderlines_wrangled_tbl %>%
select(category_1, total_price) %>%
group_by(category_1) %>%
summarise(sales = sum(total_price)) %>%
ungroup() %>%
rename(Sales = sales) %>%
# format dollars
mutate(Sales1 = Sales %>% scales::dollar())
## # A tibble: 2 × 3
## category_1 Sales Sales1
## <chr> <dbl> <chr>
## 1 Mountain 39154735 $39,154,735
## 2 Road 31877595 $31,877,595
# Q3: Renaming the columns
bike_orderlines_wrangled_tbl <- bike_orderlines_wrangled_tbl %>%
rename('Prime Category' = category_1,
'Secondary Category' = category_2,
'Frame Material' = frame_material
)
bike_orderlines_wrangled_tbl %>% distinct('Prime Category')
## # A tibble: 1 × 1
## `"Prime Category"`
## <chr>
## 1 Prime Category
bike_orderlines_wrangled_tbl %>% distinct('Secondary Category')
## # A tibble: 1 × 1
## `"Secondary Category"`
## <chr>
## 1 Secondary Category
bike_orderlines_wrangled_tbl %>% distinct('Frame Material')
## # A tibble: 1 × 1
## `"Frame Material"`
## <chr>
## 1 Frame Material
# Q4: Group
category_sales_tbl <- bike_orderlines_wrangled_tbl %>%
group_by(`Prime Category`, `Secondary Category`, `Frame Material`) %>%
summarise(Sales = sum(total_price), .groups = "drop") %>%
mutate(Sales = scales::dollar(Sales))
print(category_sales_tbl)
## # A tibble: 13 × 4
## `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