Binding dataframes of different length (no cbind, no merge)

Question

I am trying to display multiple dataframes next to each other to compare certain entries. However, they have a different number of rows and I want each data frame to be in the exact same order. I tried to use cbind which did not work because of the different number of rows. I used merge to bind two dfs together and then merge them again, however they change order when I do that and it seems inefficient to merge two dfs when I have more than 5 in total.

Examp:

df <-  data.frame(v=1:5, x=sample(LETTERS[1:5],5))
df 
  v x
1 1 E
2 2 B
3 3 D
4 4 C
5 5 A

df2 <- data.frame(m=7:10, n=sample(LETTERS[6:9],4))
df2
   m n
1  7 G
2  8 I
3  9 F
4 10 H

Then I ordered df2

df2 <- df2[order(df2$m, decreasing = TRUE),]
df2
   m n
4 10 F
3  9 I
2  8 H
1  7 G

Expected output:

  v x m n
1 1 E 10 F
2 2 B 9 I
3 3 D 8 H
4 4 C 7 G
5 5 A NA NA

As I said, I have more than two dfs and the order of the dfs should be remained. Any help will be greatly appreciated!

Answer 1

Edit: In case there are multiple df. Do this

• Create a list of all dfs except one say first one
• use purrr::reduce to join all these together
• pass first df in .init argument.

df2 <- data.frame(m=7:10, n=sample(LETTERS[6:9],4))
df <-  data.frame(v=1:5, x=sample(LETTERS[1:5],5))
df3 <- data.frame(bb = 101:110, cc = sample(letters, 10))


reduce(list(df2, df3), .init = df %>% mutate(id = row_number()) , ~full_join(.x, .y %>% mutate(id = row_number()), by = "id" )) %>%
  select(-id)

    v    x  m    n  bb cc
1   1    A 10    I 101  u
2   2    C  9    H 102  v
3   3    D  8    G 103  n
4   4    E  7    F 104  w
5   5    B NA <NA> 105  s
6  NA <NA> NA <NA> 106  y
7  NA <NA> NA <NA> 107  g
8  NA <NA> NA <NA> 108  i
9  NA <NA> NA <NA> 109  p
10 NA <NA> NA <NA> 110  h

---

Earlier Answer: Create a dummy column id in both dfs and use full_join

full_join(df %>% mutate(id = row_number()), df2 %>% mutate(id = row_number()), by = "id") %>%
  select(-id)

  v x  m    n
1 1 A 10    I
2 2 C  9    H
3 3 D  8    G
4 4 E  7    F
5 5 B NA <NA>

Results are different from as expected becuase of different random number seed

---

Or in BaseR

merge(transform(df, id = seq_len(nrow(df))), transform(df2, id = seq_len(nrow(df2))), all = T)

  id v x  m    n
1  1 1 A 10    I
2  2 2 C  9    H
3  3 3 D  8    G
4  4 4 E  7    F
5  5 5 B NA <NA>

Remove extra column simply by subsetting []

merge(transform(df, id = seq_len(nrow(df))), transform(df2, id = seq_len(nrow(df2))), all = T)[-1]

  v x  m    n
1 1 A 10    I
2 2 C  9    H
3 3 D  8    G
4 4 E  7    F
5 5 B NA <NA>