Aggregation using Dplyr

dplyrwoes.txt

library(dplyr); library(magrittr)

#To Create The Dataframe of Fake Orders

set.seed(867.5309)

onums <- paste("AA", as.integer(runif(20) * 1e8), sep = "")
prices <- round((runif(20) * 1000), 2)
orders <- as.data.frame(cbind(onums, prices))

orders %>% 
    group_by(prices) %>% 
    summarise(orders = n())

What I get:
    
prices orders
(fctr)  (int)
1  173.87      1
2  176.45      1
3  212.39      1
4  323.37      1
5  353.56      1
6  434.05      1
7  469.32      1
8   48.23      1
9  489.06      1
10 502.43      1
11 518.76      1
12 603.93      1
13 649.49      1
14  65.23      1
15 678.09      1
16 822.91      1
17  83.25      1
18   85.1      1
19 912.28      1
20 925.66      1

Desired Output:
    
0-100   4
100-200 2
200-300 1
300-400 2
400-500 3
500-600 2
600-700 3
800-900 1
900-1000 2

orders %>%
group_by(prices) %>%
summarise(orders = n()) %>%
mutate(prices = as.numeric(as.character(prices)),
container = .bincode(prices,seq(0,1000,100)) * 100
) %>%
group_by(container) %>%
summarise(Count = n())

You can use cut as well, but you have to get the label of the factor and convert it to numeric first.

orders %>%
group_by(prices) %>%
summarise(orders = n()) %>%
mutate(cut_prices = cut(as.numeric(as.character(prices)), seq(0,1000,100), include.lowest = TRUE)) %>%
group_by(cut_prices) %>%
summarise(orders = n())