right_join and mutate does not preserve the index in R - r

Question

I am Mapping column_data to master and if column value is present in master than it saves it Key
ex:Parent for P and Child for C
Problem is i am getting the output but output is indexed differently
DATA
column_data <- c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "",
"", "P", "C", "C")
master <- list("Parent" = c("P"),
"Child" = c("C")
)
CODE
library(dplyr)
df <- data.frame("column" = column_data)
df <-stack(master) %>%
type.convert(as.is = TRUE) %>%
right_join(df, by = c('values' = 'column')) %>%
mutate(output = coalesce(ind, values))
This Should be the output:
structure(list(values = c("", "", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "P", "C", "C"), ind = c(NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Parent",
"Child", "Child"), output = c("", "", "", "", "", "", "", "",
"", "", "", "", "", "", "", "", "Parent", "Child", "Child")), class = "data.frame", row.names = c(NA,
-19L))
but instead i get this as output:
structure(list(values = c("P", "C", "C", "", "", "", "", "",
"", "", "", "", "", "", "", "", "", "", ""), ind = c("Parent",
"Child", "Child", NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA), output = c("Parent", "Child", "Child", "",
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "")), row.names = c(NA,
-19L), class = "data.frame")

Answer 1

With dplyr, if you do a right_join(x, y) then the result will include a subset of the matched rows for x, then unmatched rows for y.
From R documentation on mutating joins, the value returned will be:
An object of the same type as x. The order of the rows and columns of
x is preserved as much as possible. The output has the following
properties:
For inner_join(), a subset of x rows. For left_join(), all x rows. For
right_join(), a subset of x rows, followed by unmatched y rows. For
full_join(), all x rows, followed by unmatched y rows.
That is why you have the 3 matched rows at the beginning of your resulting data.frame.
To get the desired result preserving the row order of df, try a left_join as follows:
df2 <- stack(master) %>%
type.convert(as.is = TRUE)
df %>%
left_join(df2, by = c('column' = 'values')) %>%
mutate(output = coalesce(ind, column))
Output
column ind output
1 <NA>
2 <NA>
3 <NA>
4 <NA>
5 <NA>
6 <NA>
7 <NA>
8 <NA>
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 <NA>
14 <NA>
15 <NA>
16 <NA>
17 P Parent Parent
18 C Child Child
19 C Child Child