`Count()`

does exactly what it says: it counts the number of cases! Applied directly to a data frame, `count()`

will provide you with the number `n`

of cases. Applied to a table which has been pre-grouped with `group_by()`

(read more about `group_by()`

here) or in a pipe in combination with `group_by()`

, it will give you the number of cases `n`

for each group.

Let’s illustrate this with the data frame `Orange`

:

Orange %>% count()

The table shows the single value 35 which matches the number of observations in the original data frame.

Let’s now see what happens when we apply it in combination with `group_by()`

:

Orange %>% group_by(age) %>% count()

The result table shows indeed the number of observations for each factor of the variable `age`

.

And the same happens when we apply `count()`

to a pre-grouped table such as `Orange_grouped_by_age`

:

Orange_grouped_by_age <- Orange %>% group_by(age) Orange_grouped_by_age %>% count()

Finally, note that we may apply `count()`

to a table where a variable is already organised in groups while specifying another variable between the parentheses of `count()`

. In this case, the resulting table will show the number of cases for each of the combinations of variables. Let’s illustrate this again with the already-sorted table `Orange_grouped_by_age`

and let’s apply `count(Tree)`

:

Orange_grouped_by_age <- Orange %>% group_by(age) Orange_grouped_by_age %>% count(Tree)

And indeed, the table shows that there is only 1 observation per `age`

and per `Tree`

.