Obtain or conserve the group (zero value) not captured by `group_by` using dplyr

Question

Using this sample data :

filteredUS <- structure(list(Orientation.1 = c(0L, 0L, 10L, 12L, 16L, 16L, 
18L, 20L, 30L, 34L, 38L, 48L, 50L, 99L, 102L, 102L, 115L, 115L, 
266L, 288L, 289L, 289L, 290L, 310L, 310L, 312L, 312L, 318L, 320L, 
324L, 328L, 332L, 332L, 332L, 332L, 332L, 332L, 334L, 334L, 334L, 
340L)), class = "data.frame", row.names = c(NA, -41L), .Names = "Orientation.1")

I want to conserve all counted value when i cut a dataframe using factor, like table do : table(cut(filteredUS$Orientation.1, breaks = breaksVal, right = FALSE ))

Now i want the same behaviour with a dataframe which contain value for each factor using group_by method of dplyr, is it possible ?

 breaksVal <- c(seq(0, 360, by = 20), Inf)
    filteredUS$levels <- cut(filteredUS$Orientation.1, breaks = breaksVal, right = FALSE )
    filteredUS %>% group_by(levels) %>% summarize(n())

You can observe the result bottom :

      levels   n()
      (fctr) (int)
1     [0,20)     7
2    [20,40)     4
3    [40,60)     2
4   [80,100)     1
5  [100,120)     4
6  [260,280)     1
7  [280,300)     4
8  [300,320)     5
9  [320,340)    12
10 [340,360)     1

I also want to conserve in my original dataframe all values, like this !

      levels   n()
      (fctr) (int)
     [0,20)     7
    [20,40)     4
    [40,60)     2
    [60,80)     0
   [80,100)     1
  [100,120)     4
  [120,140)     0
  [140,160)     0
  [160,180)     0
  [180,200)     0
  [200,220)     0
  [220,240)     0
  [240,260)     0
  [260,280)     1
  [280,300)     4
  [300,320)     5
  [320,340)    12
 [340,360)     1