R-按列状态子集，并根据另一个数据框计数唯一记录

Question

I've got a dataset that looks like this:

customer_id    group_a    group_b    group_c    group_d
123            true       false      true       false
456            false      true       false      true
789            false      true       true       false

I also have each customer's record in a dataset like this.

customer_id    date
123            01/01/2019
123            01/02/2019
123            01/03/2019
123            01/04/2019
123            01/04/2019  

456            01/01/2019
456            01/02/2019
456            01/03/2019

789            01/01/2019
789            01/03/2019
789            01/03/2019

I'd like to be able to get the counts of unique records by date for every group iteration where the customer is "true" and the total number of customers for every group . The result of which will look like this:

date         group    record   total
01/01/2019   a        1        1
01/02/2019   a        1        1
01/03/2019   a        1        1
01/04/2019   a        1        1

01/01/2019   b        2        2
01/02/2019   b        1        2
01/03/2019   b        2        2
01/04/2019   b        0        2

01/01/2019   c        2        2
01/02/2019   c        1        2
01/03/2019   c        2        2
01/04/2019   c        1        2

01/01/2019   d        1        1
01/02/2019   d        1        1
01/03/2019   d        1        1
01/04/2019   d        0        1

Answer 1

I don't feel this is very elegant, but the result matches your expected output so: Here it is.

library(lubridate)
library(dplyr)
library(tidyr)

df2$date <- mdy(df2$date)

df2 %>% 
  inner_join(df1, by = "customer_id", copy = TRUE) %>%
  gather(key = "group", value = "member", group_a:group_d) %>%
  filter(member == "true") %>% 
  complete(date, group) %>%
  select(date, group, customer_id) ->  df3

df3 %>%
  group_by(group, date) %>% 
  summarise(record = n_distinct(customer_id, na.rm = TRUE)) %>% 
  left_join( df3 %>%
             group_by(group) %>%
             summarise(total = n_distinct(customer_id, na.rm = TRUE)),
             by = "group") %>% ungroup() %>%
  select(date, group, record, total) -> result

which gives:

# A tibble: 16 x 4
   date       group   record total
   <date>     <chr>    <int> <int>
 1 2019-01-01 group_a      1     1
 2 2019-01-02 group_a      1     1
 3 2019-01-03 group_a      1     1
 4 2019-01-04 group_a      1     1
 5 2019-01-01 group_b      2     2
 6 2019-01-02 group_b      1     2
 7 2019-01-03 group_b      2     2
 8 2019-01-04 group_b      0     2
 9 2019-01-01 group_c      2     2
10 2019-01-02 group_c      1     2
11 2019-01-03 group_c      2     2
12 2019-01-04 group_c      1     2
13 2019-01-01 group_d      1     1
14 2019-01-02 group_d      1     1
15 2019-01-03 group_d      1     1
16 2019-01-04 group_d      0     1