Clarification regarding to_factor about sjmisc HOT 4 CLOSED

library(haven)
library(sjmisc)

test <- labelled(c(1,2,3,4,5,1,2,5,4), c(Bad = 1, Good = 5))
as_factor(test)
> [1] Bad  <NA> <NA> <NA> Good Bad  <NA> Good <NA>
> Levels: Bad Good

to_label(test)
> [1] Bad  <NA> <NA> <NA> Good Bad  <NA> Good <NA>
> Levels: Bad Good

to_label(test, add.non.labelled = TRUE)
> [1] Bad  2    3    4    Good Bad  2    Good 4   
> Levels: Bad Good 2 3 4

x <- labelled(c(1, 2, 1, 3, 4, 1),
              c(Male = 1, Female = 2, Refused = 3, "N/A" = 4),
              c(FALSE, FALSE, TRUE, TRUE))
# to labelled factor, with missing labels
to_label(x)
> [1] Male    Female  Male    Refused N/A     Male   
> Levels: Male Female Refused N/A

# to labelled factor, missings removed
to_label(x, drop.na = TRUE)
> [1] Male   Female Male   <NA>   <NA>   Male  
> Levels: Male Female

# to factor, with missing labels
to_factor(x)
> [1] 1 2 1 3 4 1
> attr(,"labels")
>    Male  Female Refused     N/A 
>       1       2       3       4 
> attr(,"is_na")
> [1] FALSE FALSE  TRUE  TRUE
> Levels: 1 2 3 4

# to factor, missings removed
to_factor(x, drop.na = TRUE)
> [1] 1    2    1    <NA> <NA> 1   
> attr(,"labels")
>   Male Female 
>      1      2 
> attr(,"is_na")
> [1] FALSE FALSE
> Levels: 1 2

get_labels(test, include.non.labelled = T, include.values = "p")
> [1] "[1] Bad"  "[2] 2"    "[3] 3"    "[4] 4"    "[5] Good"

to_label(test, add.non.labelled = T)
> [1] Bad 2   3   4   2   Bad 2   2   4  
> Levels: Bad 2 3 4 Good

to_factor(test)
> [1] 1 2 3 4 5 1 2 5 4
> attr(,"labels")
>  Bad    2    3    4 Good 
>    1    2    3    4    5 
> attr(,"is_na")
> [1] FALSE FALSE FALSE FALSE FALSE
> Levels: 1 2 3 4 5

library(sjPlot)
library(sjmisc)
data(efc)
fit <- lm(tot_sc_e ~ c12hour + c160age + e42dep + neg_c_7 + c172code, data = efc)
sjp.lm(fit)