library(lubridate)
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library(readxl)
library(tidyverse)
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library(scales)
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library(tidyquant)
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bike_orderlines_tbl <- read_excel(path = "bike_orderlines.xlsx")
bike_orderlines_tbl %>% select(bikeshop_name, category_1, category_2, quantity) %>%
group_by(bikeshop_name, category_1, category_2) %>%
summarise(total_quantity = sum(quantity)) %>%
ungroup() %>%
mutate(bikeshop_name = as_factor(bikeshop_name) %>%
fct_reorder(total_quantity)) %>%
arrange(desc(total_quantity)) %>%
mutate(total_quantity_pct = total_quantity/sum(total_quantity)) %>%
mutate(total_quantity_pct_txt = scales::percent(total_quantity_pct))
## `summarise()` has grouped output by 'bikeshop_name', 'category_1'. You can
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## # A tibble: 270 × 6
## bikeshop_name category_1 category_2 total_quantity total_quantity_pct
## <fct> <chr> <chr> <dbl> <dbl>
## 1 Kansas City 29ers Mountain Cross Cou… 896 0.0444
## 2 Kansas City 29ers Mountain Trail 620 0.0307
## 3 Kansas City 29ers Mountain Sport 558 0.0277
## 4 Denver Bike Shop Mountain Cross Cou… 549 0.0272
## 5 Kansas City 29ers Road Elite Road 437 0.0217
## 6 Denver Bike Shop Mountain Trail 411 0.0204
## 7 Denver Bike Shop Mountain Sport 388 0.0192
## 8 Oklahoma City Race E… Road Elite Road 382 0.0189
## 9 Ithaca Mountain Clim… Mountain Cross Cou… 379 0.0188
## 10 Kansas City 29ers Road Endurance… 328 0.0163
## # ℹ 260 more rows
## # ℹ 1 more variable: total_quantity_pct_txt <chr>