Подсчет вероятности отказа пользователя от заказа

library(ggplot2)
library(dplyr)
library(knitr)

orders   <- read.table("~/orders.csv", sep=";", head=TRUE)
interval <- seq(0, to = 120000, by = 1000)
orders   <- orders %>% mutate(group_price = cut(price_total_init, interval, dig.lab = 5))

all_count <- orders %>%
  group_by(group_price) %>%
  summarise(id=n()) %>%
  select(group_price, all_count = id)

returned_count <- orders %>%
  filter(state=="returned") %>%
  group_by(group_price) %>%
  summarise(id=n()) %>%
  select(group_price, returned_count = id)

result <- all_count %>%
  merge(returned_count, sort=F) %>%
  mutate(p = round(returned_count/all_count, 2))

result

##      group_price all_count returned_count    p
## 1       (0,1000]      2100            186 0.09
## 2    (1000,2000]      5206            469 0.09
## 3    (2000,3000]      3449            373 0.11
## 4    (3000,4000]      3443            254 0.07
## 5    (4000,5000]      1765            160 0.09
## 6    (5000,6000]      1028             76 0.07
## 7    (6000,7000]       938             63 0.07
## 8    (7000,8000]       394             26 0.07
## 9    (8000,9000]       260             22 0.08
## 10  (9000,10000]       179             18 0.10
## 11 (10000,11000]       116             11 0.09
## 12 (11000,12000]        93             15 0.16
## 13 (12000,13000]        68              6 0.09
## 14 (13000,14000]        51              8 0.16
## 15 (14000,15000]        26              4 0.15
## 16 (15000,16000]        29              1 0.03
## 17 (17000,18000]        14              2 0.14
## 18 (18000,19000]         6              1 0.17
## 19 (19000,20000]        10              2 0.20
## 20 (23000,24000]         2              2 1.00
## 21 (27000,28000]         2              1 0.50
## 22 (31000,32000]         1              1 1.00
## 23 (33000,34000]         4              1 0.25
## 24 (45000,46000]         1              1 1.00