如何使用dplyr根据另一列的不同值在新列中填充不同的值？

Question

Here is my data:

a <- data.frame(x=c('A','A','A','B','B','B'),
                y=c('Yes','No','No','Yes','No','No'),
                z=c(1,2,3,4,5,6))

I want to generate a new column this way:

1. Group by x , so all the A s will be in one group and all B s in another
2. For every group, if y=Yes , then keep the z value in the new column. If y=No , then using the z value with y=Yes .

So, the new data should look like this:

x    y   z   z1
A   Yes  1   1
A   No   2   1
A   No   3   1
B   Yes  4   4
B   No   5   4
B   No   6   4

I can use this way to do:

a1 <- a %>%
   filter(y=='Yes') %>%
   distinct(x,y,z)
 a2 <- a %>%
    left_join(a1,by='x') %>%...

But in this way, I have to generate a1 as an intermediate. How to do this just in one pipeline without generating a new variable like a1 in my example?

Answer 1

You could combine both pipelines and perform the same functions in one shot.

ie..

    a <- data.frame(x=c('A','A','A','B','B','B'),
                    y=c('Yes','No','No','Yes','No','No'),
                    z=c(1,2,3,4,5,6))

    a %>% left_join(a %>% filter(y=='Yes') %>% distinct(x,y,z), by='x') %>% select(-y.y)

This results in duplicate columns tagged with .x and .y as a result of the join.