Is there a way to impute chi-square data? I've tried looking here, as well as the go-to book Flexible Imputation of Missing Data, but cannot seem to find any reliable information on how to simulate chi-square missingness (as well as imputing it with multiple imputation). I tried simulating some data like so. Note that while I have NA listed explicitly, I also tried with NA as a character value and it didn't change the outcome.
#### Load Library and Set Seed ####
library(mice)
library(miceadds)
set.seed(123)

#### Create Data ####
color <- c("blue","green","purple","red",
                 "orange","black","gray",NA)
choice <- c("yes","no",NA)

#### Sample ####
color.sample <- sample(color, size= 100, replace = T)
choice.sample <- sample(choice, size = 100, replace = T)
df <- data.frame(color.sample,choice.sample)

If you run head(df), it will look something like this:
   color.sample choice.sample
1           red           yes
2           red            no
3         black          <NA>
4        orange           yes
5          gray          <NA>
6           red            no
7          blue            no
8        purple          <NA>
9        purple          <NA>
10         gray            no
11        black          <NA>
12       orange            no
13         blue           yes
14       purple          <NA>
15         blue            no
16        black            no
17       purple            no
18          red            no
19        black            no
20       orange          <NA>

Then all that's left is imputation...
#### Impute ####
imp <- mice(df)

Once I get to this last step, no matter how many ways I alter the factor data, I get this error:
Error in edit.setup(data, setup, ...) : 
  `mice` detected constant and/or collinear variables. No predictors were left after their removal.

This doesn't make much sense to me. I would think that if my data is randomly sampled, there should be no collinearity issues. What could be causing this and is there a solution? I looked in the categorical data section of Buuren's book, but the polyr, polyreg, pmm, and logreg methods all seem to not work.

mice does not implicitly coerce character variables in your data.frame into categorical factor variables.  You can check the format of your variables using str(df)
This works:
df <- data.frame(factor(color.sample), 
                 factor(choice.sample))

imp <- mice(df)

