Relocate rows with tidyverse

Question

Is it possible to relocate rows in tidyverse framework like it is possible for columns with dplyr relocate?

In this example I would like to relocate row 1 to position 5 (end of dataframe)

My dataframe:

df <- structure(list(ID = c(1, 2, 3, 4, 5), var1 = c("a", "b", "c", 
"d", "e"), var2 = c(1, 1, 0, 0, 1)), class = "data.frame", row.names = c(NA, 
-5L))

df
  ID var1 var2
1  1    a    1
2  2    b    1
3  3    c    0
4  4    d    0
5  5    e    1

Desired output:

  ID var1 var2
1  2    b    1
2  3    c    0
3  4    d    0
4  5    e    1
5  1    a    1

Note: In the it should be 'pipe friendly' solution. I tried a lot but found nothing. Thank you.

Answer 1

arrange() is the tidyverse verb for reordering rows. It can be (ab)used as follows:

dplyr::arrange(df, ID==1)

(ID==1 is logical; when it is ordered FALSE values come before TRUE values ...)

This isn't as flexible as relocate() (e.g. it's not immediately obvious how to say "move rows 100-200 so they are immediately after row 1000"), but you can probably find a way to do most tasks.

Another option (less idiomatic in my opinion) is slice():

dplyr::slice(df, order(ID==1))

(this is a tidyverse translation of @akrun's base-R answer). Either of these solutions can also be written with pipes (e.g. df %>% arrange(ID==1)).

Just to be silly:

df %>% `[`(order(.$ID==1),)

Answer 2

Maybe this is not so elegant but here is a way:

library(dplyr)

df %>%
  filter(between(row_number(), 2, nrow(df))) %>%
  bind_rows(df[1, ])

  ID var1 var2
1  2    b    1
2  3    c    0
3  4    d    0
4  5    e    1
5  1    a    1

Answer 3

Using base R

df[order(df$ID == 1), ]

---

Or with slice

library(dplyr)
df %>%
    slice(2:n(), 1)

Or specify with row_number()

df %>% 
   slice(lead(row_number(), default = 1))

Answer 4

Let's play a math trick

> df[order((seq(nrow(df)) -2) %% nrow(df)), ]
  ID var1 var2
2  2    b    1
3  3    c    0
4  4    d    0
5  5    e    1
1  1    a    1

or

> df %>%
+   arrange(replace(row_number(), 1, n() + 1))
  ID var1 var2
1  2    b    1
2  3    c    0
3  4    d    0
4  5    e    1
5  1    a    1

Answer 5

After the whole day of trial and error and your wonderful answers:

library(dplyr)

df %>% 
    slice(-1) %>% bind_rows(df %>% slice(1))

Output:

  ID var1 var2
1  2    b    1
2  3    c    0
3  4    d    0
4  5    e    1
5  1    a    1