[r] Count number of rows by group using dplyr

I am using the mtcars dataset. I want to find the number of records for a particular combination of data. Something very similar to the count(*) group by clause in SQL. ddply() from plyr is working for me

library(plyr)
ddply(mtcars, .(cyl,gear),nrow)

has output

  cyl gear V1
1   4    3  1
2   4    4  8
3   4    5  2
4   6    3  2
5   6    4  4
6   6    5  1
7   8    3 12
8   8    5  2

Using this code

library(dplyr)
g <- group_by(mtcars, cyl, gear)
summarise(g, length(gear))

has output

  length(cyl)
1          32

I found various functions to pass in to summarise() but none seem to work for me. One function I found is sum(G), which returned

Error in eval(expr, envir, enclos) : object 'G' not found

Tried using n(), which returned

Error in n() : This function should not be called directly

What am I doing wrong? How can I get group_by() / summarise() to work for me?

I think what you are looking for is as follows.

cars_by_cylinders_gears <- mtcars %>%
  group_by(cyl, gear) %>%
  summarise(count = n())

This is using the dplyr package. This is essentially the longhand version of the count () solution provided by docendo discimus.

another approach is to use the double colons:

mtcars %>% 
  dplyr::group_by(cyl, gear) %>%
  dplyr::summarise(length(gear))

There's a special function n() in dplyr to count rows (potentially within groups):

library(dplyr)
mtcars %>% 
  group_by(cyl, gear) %>% 
  summarise(n = n())
#Source: local data frame [8 x 3]
#Groups: cyl [?]
#
#    cyl  gear     n
#  (dbl) (dbl) (int)
#1     4     3     1
#2     4     4     8
#3     4     5     2
#4     6     3     2
#5     6     4     4
#6     6     5     1
#7     8     3    12
#8     8     5     2

But dplyr also offers a handy count function which does exactly the same with less typing:

count(mtcars, cyl, gear)          # or mtcars %>% count(cyl, gear)
#Source: local data frame [8 x 3]
#Groups: cyl [?]
#
#    cyl  gear     n
#  (dbl) (dbl) (int)
#1     4     3     1
#2     4     4     8
#3     4     5     2
#4     6     3     2
#5     6     4     4
#6     6     5     1
#7     8     3    12
#8     8     5     2

Another option, not necesarily more elegant, but does not require to refer to a specific column:

mtcars %>% 
  group_by(cyl, gear) %>%
  do(data.frame(nrow=nrow(.)))