r-dplyr：计算同一数据帧中另一个变量的每个唯一值的一个变量中唯一值的频率

Question

So here's a sample of some of the rows from my dataframe:

> data[1:25, c("TR_DATE", "TR_TYPE...")]
  TR_DATE TR_TYPE...
1  2016-03-01          4
2  2016-03-01          4
3  2016-03-01          5
4  2016-03-01          4
5  2016-03-01          1
6  2016-03-01          7
7  2016-03-01          4
8  2016-03-01          4
9  2016-03-01          24
10 2016-03-01          23
11 2016-03-01          4
12 2016-03-02          4
13 2016-03-02          1
14 2016-03-02          1
15 2016-03-02          4
16 2016-03-02          4
17 2016-03-02          14
18 2016-03-02          4
19 2016-03-02          4
20 2016-03-03          4
21 2016-03-03          1
22 2016-03-03          4
23 2016-03-03          23
24 2016-03-03          1
25 2016-03-03          4

What I'd like to do exactly is rearrange in such a way that for every unique day, I get the number of unique transaction types and the frequency of each transaction type

Here's the code that I tried:

data %>%
group_by(TR_DATE) %>%
summarise(trancount = n(), trantype = n_distinct(TR_TYPE...))

which gave me part of the result that I wanted:

# A tibble: 68 x 3
  TR_DATE trancount trantype
   <date>     <int>    <int>
 1 2016-03-01      5816        6
 2 2016-03-02      5637        3
 3 2016-03-03      4818        3
 4 2016-03-04      5070        8
 5 2016-03-05         4        2
 6 2016-03-08      6707        5
 7 2016-03-09      5228        5
 8 2016-03-10      4722        6
 9 2016-03-11      4469        8
10 2016-03-12         1        1
# ... with 58 more rows

so trantype tells me the number of unique transaction types that happened on a particular day, but I'd like to know the frequency of each of these unique transaction types. What would be the best way to go around doing this? I tried looking around and found similar questions but was unable to modify the solutions to my requirement. I'm fairly new to R and would really appreciate some help. Thanks.

Answer 1

You should group by both variables:

data %>%
group_by(TR_DATE, TR_TYPE...) %>%
summarise(trancount = n(), trantype = n_distinct(TR_TYPE...))