Careem-case-study.md

Careem Case Study

Anish Joni December 12, 2018

Data Generation

orders_df %>% 
  group_by(order_day) %>% 
  count() %>%  
  ggplot(aes(order_day, n)) +
  geom_bar(stat = "identity") +
  scale_color_viridis_c() +
  theme_fivethirtyeight()

orders_df %>% 
  mutate(order_day = wday(order_datetime, label = T))

## # A tibble: 1,000 x 7
##    store_id store_name order_datetime      destination delivery_time      
##       <int> <chr>      <dttm>              <chr>       <dttm>             
##  1      109 KFC        2018-08-23 00:00:00 Neighbourh~ 2018-08-23 00:21:10
##  2      107 McDonald's 2018-10-26 00:00:00 Neighbourh~ 2018-10-26 00:42:29
##  3      129 Dipndip    2018-10-10 00:00:00 Neighbourh~ 2018-10-10 00:56:05
##  4       82 Didi Burg~ 2018-08-21 00:00:00 Neighbourh~ 2018-08-21 00:34:53
##  5       44 KFC        2018-11-25 00:00:00 Neighbourh~ 2018-11-25 00:30:43
##  6       65 McDonald's 2018-11-25 00:00:00 Neighbourh~ 2018-11-25 00:36:53
##  7       88 KFC        2018-08-16 00:00:00 Neighbourh~ 2018-08-16 00:54:03
##  8      135 Doce       2018-11-11 00:00:00 Neighbourh~ 2018-11-11 00:25:26
##  9       48 Broccoli ~ 2018-09-27 00:00:00 Neighbourh~ 2018-09-27 00:55:13
## 10       50 Didi Burg~ 2018-10-07 00:00:00 Neighbourh~ 2018-10-07 00:23:59
## # ... with 990 more rows, and 2 more variables: travel_time <time>,
## #   order_day <ord>