How to merge two dataframes by columns and alternating the columns that don't match in R

Question

I have to do a D in D analysis and for that I have two dataframes with the exact same colums but with different values (pre treatment and post treatment). In addition, from one period to another I lost some participants so some values won't be taken into account:

Survey1:

ID	City	Children	Q1	Q2
1	Paris	Yes	0.5	Yes
2	NY	No	1	No
3	London	No	NA	Yes
4	Madrid	Yes	2.1	No
5	Paris	Yes	1.8	Yes
6	Paris	No	NA	Yes
7	NY	Yes	3	Yes
8	Madrid	Yes	0.8	No
9	Paris	No	2.5	No
10	Paris	No	1	Yes

Survey 2:

ID	City	Children	Q1	Q2
1	Paris	Yes	1	Yes
3	London	No	2	Yes
4	Madrid	Yes	0.5	Yes
6	Paris	No	2	Yes
7	NY	Yes	1.8	Yes
9	Paris	Yes	2.5	Yes
10	Paris	No	1	No

As you can see in Survey2 I have lost subjects: 2, 5 and 8 + Subject 9 had a baby meanwhile.

I would like to merge both dataframes by ID, alternating columns and if possible changing the name to make clear the columps pre and post treatment:

Result:

ID	City	Children	Q1	Q1_t	Q2	Q2_t
1	Paris	Yes	0.5	1	Yes	Yes
3	London	No	NA	2	Yes	Yes
4	Madrid	Yes	2.1	0.5	No	Yes
6	Paris	No	NA	2	Yes	Yes
7	NY	Yes	3	1.8	Yes	Yes
9	Paris	Yes	2.5	2.5	No	Yes
10	Paris	No	1	1	Yes	No

When i use merge(Survey1, Survey2, by = "ID") it keeps the correct ID but since subject 9 had a baby it gives me an extra observation so my Result dataframe has 8 obs. instead of only 7 obs. (since I lost 3 subjects). I would like to only take into account the observation once subject 9 had a baby. It also tries to merge some of my questions which as a consequence eliminates my pre and post treatment observations and puts them in the order: Q1, Q2, Q1, Q2 instead of: Q1, Q1, Q2, Q2. Also, I don't know how to merge by adding the "_t" at the end ob the second dataframecolumn names.

Does somebody has an idea?

#For Survey 1
a <- c("1","2","3","4","5","6","7","8","9","10")
b <- c("Paris", "NY", "London", "Madrid", "Paris", "Paris", "NY", "Madrid", "Paris", "Paris")
c <- c("Yes", "No", "No", "Yes", "Yes", "No", "Yes", "Yes", "No", "No")
d <- c(0.5, 1, NA, 2.1, 1.8, NA, 3, 0.8, 2.5, 1)
e <- c("Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", "No", "Yes")
Survey1 <- data.frame(a,b,c,d,e)
names(Survey1) <- c("ID", "City", "Children", "Q1", "Q2")
Survey1

#For Survey2
a <- c("1","3","4","6","7","9","10")
b <- c("Paris", "London", "Madrid", "Paris", "NY", "Paris", "Paris")
c <- c("Yes", "No", "Yes", "No", "Yes", "Yes", "No")
d <- c(1, 2, 0.5, 2, 1.8, 2.5, 1)
e <- c("Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No")
Survey2 <- data.frame(a,b,c,d,e)
names(Survey2) <- c("ID", "City", "Children", "Q1", "Q2")
Survey2 

Answer 1

Your data is not reproducible, so I haven't tried my solution, but by the looks of your data, it seems you want to do:

library(tidyverse)
results <- survey1 %>%
  select(-Children) %>%
  right_join(survey2 %>%
               rename(Q1_t = Q1,
                      Q2_t = Q2),
             by = c("id", "City")) %>%
  relocate(Q1_T, .after = Q1)