dplyr: mean of a group count

Question

I am trying to find the mean length of a variable over a dataframe using dplyr:

x <- data %>% group_by(Date, `% Bucket`) %>% summarise(count = n())

Date          % Bucket count
(date)    (fctr) (int)
1  2015-01-05       <=1  1566
2  2015-01-05    (1-25]   421
3  2015-01-05   (25-50]   461
4  2015-01-05   (50-75]   485
5  2015-01-05  (75-100]   662
6  2015-01-05 (100-150]  1693
7  2015-01-05      >150 12359
8  2015-01-13       <=1  1608
9  2015-01-13    (1-25]   441
10 2015-01-13   (25-50]   425

How to aggregate to find average across each % Bucket over the year with dplyr?

in base:
x <- as.data.frame(x)
aggregate(count ~ `% Bucket`, data = x, FUN=mean)

% Bucket      count
1       <=1  2609.5294
2    (1-25]   449.0000
3   (25-50]   528.7059
4   (50-75]   593.2157
5  (75-100]   763.0000
6 (100-150]  1758.6667
7      >150 12457.9216

Aggregate function will take the count found by dplyr across each bucket above and sum them, dividing by the number of rows that contain that % Bucket variable and give the answer above. How can I accomplish this with dplyr though? This is not about completing the problem but understanding how the dplyr package would be used in such a scenario.

Another example of this type of thing would be summarise the n() of each group_by variable and also listing the minimum length "count" of that variable across the 52 weeks.

I am struggling because dplyr seems to be built to find a mean of a value in a column, but here I am counting the number of row occurrences given a variable in a column and trying to find the mean, min, max, etc. of it.

Answer 1

We can use dplyr methods

library(dplyr)
x %>%
   group_by(`% Bucket`) %>%
   summarise(count= mean(count))