Hashing every row of a tibble - r

Question

I am using the newly minted dplyr 1.0.0 and the digest package to generate a hash of every row in a tibble.
I am aware of
adding hash to each row using dplyr and digest in R
but I would like to use the revamped rowwise() in dplyr 1.0.0.
See the example below. Anyone has any idea about why it fails? I should be allowed to digest a row where the entries are of different types.
library(dplyr)
library(digest)
df <- tibble(
student_id = letters[1:4],
student_id2 = letters[9:12],
test1 = 10:13,
test2 = 20:23,
test3 = 30:33,
test4 = 40:43
)
df
#> # A tibble: 4 x 6
#> student_id student_id2 test1 test2 test3 test4
#> <chr> <chr> <int> <int> <int> <int>
#> 1 a i 10 20 30 40
#> 2 b j 11 21 31 41
#> 3 c k 12 22 32 42
#> 4 d l 13 23 33 43
dd <- df %>%
rowwise(student_id) %>%
mutate(hash = digest(c_across(everything()))) %>%
ungroup
#> Error: Problem with `mutate()` input `hash`.
#> ✖ Can't combine `student_id2` <character> and `test1` <integer>.
#> ℹ Input `hash` is `digest(c_across(everything()))`.
#> ℹ The error occured in row 1.
### but digest should not care too much about the type of the input
Created on 2020-06-04 by the reprex package (v0.3.0)

Answer 1

It seems that the different column types have an issue. One option is to first change the column types to a single one and then do the rowwise
library(dplyr)
library(digest)
df %>%
mutate(across(everything(), as.character)) %>%
rowwise %>%
mutate(hash = digest(c_across(everything())))
# A tibble: 4 x 7
# Rowwise:
# student_id student_id2 test1 test2 test3 test4 hash
# <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#1 a i 10 20 30 40 2638067de6dcfb3d58b83a83e0cd3089
#2 b j 11 21 31 41 21162fc0c528a6550b53c87ca0c2805e
#3 c k 12 22 32 42 8d7539eacff61efbd567b6100227523b
#4 d l 13 23 33 43 9739997605aa39620ce50e96f1ff4f70
Or another option is to unite the columns to a single one and then do the digest on that column
library(tidyr)
df %>%
unite(new, everything(), remove = FALSE) %>%
rowwise %>%
mutate(hash = digest(new)) %>%
select(-new)
# A tibble: 4 x 7
# Rowwise:
# student_id student_id2 test1 test2 test3 test4 hash
# <chr> <chr> <int> <int> <int> <int> <chr>
#1 a i 10 20 30 40 a9e4cafdfbc88f17b7593dfd684eb2a1
#2 b j 11 21 31 41 a67a5df8186972285bd7be59e6fdab38
#3 c k 12 22 32 42 9c20bd87a50642631278b3e6d28ecf68
#4 d l 13 23 33 43 3f4f373d1969dcf0c8f542023a258225
Or another option is pmap, where we concatenate the elements to a single one in each row, resulting in integer converting to character as vectors can hold only a single class
library(purrr)
df %>%
mutate(hash = pmap_chr(., ~ digest(c(...))))
# A tibble: 4 x 7
# student_id student_id2 test1 test2 test3 test4 hash
# <chr> <chr> <int> <int> <int> <int> <chr>
#1 a i 10 20 30 40 f0fb4100907570ef9bda073b78dc44a6
#2 b j 11 21 31 41 754b09e8d4d854aa5e40aa88d1edfc66
#3 c k 12 22 32 42 5f3a699caff833e900fd956232cf61dd
#4 d l 13 23 33 43 4d31c65284e5db36c37461126a9eb63c
The advantage here is that we are not changing the column types