This error occurs when a function expects a vector and gets a scalar object instead. This commonly happens when some code attempts to assign a scalar object as column in a data frame:

fn <- function() NULL
tibble::tibble(x = fn)
#> Error: All columns in a tibble must be vectors.
#> x Column x is a function.

fit <- lm(1:3 ~ 1)
tibble::tibble(x = fit)
#> Error: All columns in a tibble must be vectors.
#> x Column x is a lm object.


## Vectorness in base R and in the tidyverse

In base R, almost everything is a vector or behaves like a vector. In the tidyverse we have chosen to be a bit stricter about what is considered a vector. The main question we ask ourselves to decide on the vectorness of a type is whether it makes sense to include that object as a column in a data frame.

The main difference is that S3 lists are considered vectors by base R but in the tidyverse that’s not the case by default:

fit <- lm(1:3 ~ 1)

typeof(fit)
#> [1] "list"
class(fit)
#> [1] "lm"

# S3 lists can be subset like a vector using base R:
fit[1:3]
#> $coefficients #> (Intercept) #> 2 #> #>$residuals
#>             1             2             3
#> -1.000000e+00 -3.885781e-16  1.000000e+00
#>
#> $effects #> (Intercept) #> -3.4641016 0.3660254 1.3660254 # But not in vctrs vctrs::vec_slice(fit, 1:3) #> Error: Input must be a vector, not a <lm> object.  Defused function calls are another (more esoteric) example: call <- quote(foo(bar = TRUE, baz = FALSE)) call #> foo(bar = TRUE, baz = FALSE) # They can be subset like a vector using base R: call[1:2] #> foo(bar = TRUE) lapply(call, function(x) x) #> [[1]] #> foo #> #>$bar
#> [1] TRUE
#>
#> \$baz
#> [1] FALSE

# But not with vctrs:
vctrs::vec_slice(call, 1:2)
#> Error: Input must be a vector, not a call.


## I get a scalar type error but I think this is a bug

It’s possible the author of the class needs to do some work to declare their class a vector. Consider reaching out to the author. We have written a developer FAQ page to help them fix the issue.