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.
答案 0 :(得分：1)
您应该按两个变量进行分组：
data %>%
group_by(TR_DATE, TR_TYPE...) %>%
summarise(trancount = n(), trantype = n_distinct(TR_TYPE...))