To preempt, I’ve figured out a solution to my problem using left_join() (or merge()), but I don’t really understand why ifelse() doesn’t work for this problem. Would love to hear any other ways I could do this or improve my left_join() use. Sorry if this is a long post.

Basically, I’m trying to create a new column in a data frame df1 by matching the observations in a column df1$code to the observations in a corresponding column in an indexing df index.df$code. The new column df1$type would be the values from index.df$type that correspond to the df1$code value:

#index data frame
index.df <- data.frame(
  code = c("c10", "c20", "c03", "c48", "c19"),
  id = c("apple", "strawberry", "pear", "banana", "blackberry"),
  type = c("pome", "aggregate", "pome", "berry", "aggregate")
)
> index.df
  code         id      type
1  c10      apple      pome
2  c20 strawberry aggregate
3  c03       pear      pome
4  c48     banana     berry
5  c19  blackberry aggregate

#df to add col to
df1 <- data.frame(
  code = c("c10", "c19", "c03", "c20", "c19", "c10", "c48", "c03", "c10", "c03"),
  id = c("apple", "blackberry", "pear","strawberry", "blackberry", "apple", "banana", "pear", "apple", "pear")
)
> df1
   code         id
1   c10      apple
2   c19 blackberry
3   c03       pear
4   c20 strawberry
5   c19 blackberry
6   c10      apple
7   c48     banana
8   c03       pear
9   c10      apple
10  c03       pear

And this is the desired output

> df2
   code         id      type
1   c10      apple      pome
2   c19 blackberry aggregate
3   c03       pear      pome
4   c20 strawberry aggregate
5   c19 blackberry aggregate
6   c10      apple      pome
7   c48     banana     berry
8   c03       pear      pome
9   c10      apple      pome
10  c03       pear      pome

I tried ifelse() this way:

df2 <- df1 %>%
  mutate(df1, type = ifelse(df1$code == index.df$code, index.df$type, NA))

> df2
   no code         id      type
1   1  c10      apple      pome
2   2  c19 blackberry      <NA>
3   3  c03       pear      pome
4   4  c20 strawberry      <NA>
5   5  c19 blackberry aggregate
6   6  c10      apple      pome
7   7  c48     banana      <NA>
8   8  c03       pear      pome
9   9  c10      apple      <NA>
10 10  c03       pear      <NA>

Why is this the output? Am I using ifelse() incorrectly? Thank you in advance!

Also, the (rather bulky) code I used to get my desired output was:

df1 <- data.frame(
  no = 1:10,
  code = c("c10", "c19", "c03", "c20", "c19", "c10", "c48", "c03", "c10", "c03"),
  id = c("apple", "blackberry", "pear","strawberry", "blackberry", "apple", "banana", "pear", "apple", "pear")
)

df2 <- index.df %>%
  left_join(df1, by = c("code", "id")) %>%
  arrange(no) %>%
  select(-no)