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Clarify whether chisq_test is supposed to give same results as base::chisq.test() #515

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sda030 opened this issue Nov 11, 2023 · 2 comments
Closed

Clarify whether chisq_test is supposed to give same results as base::chisq.test() #515

sda030 opened this issue Nov 11, 2023 · 2 comments

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@sda030
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sda030 commented Nov 11, 2023

I expected infer::chisq_test() to provide the same results as chisq.test(), just containing some nice wrapper features. However, this inconsistency has led to some headaches for us. Note that I do not worry about the warning, but the NaN.
The data are real.

library(infer)
data <- structure(list(y = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 
                                       2L, 2L, 2L, 1L, 2L, NA, 1L, NA, 2L, 2L, 3L, 3L, 2L, NA, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 1L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, NA, 2L, 1L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, NA, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       NA, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 
                                       2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, NA, NA, 2L, 2L, 2L, 3L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, 2L, 2L, 1L, NA, 1L, NA, 
                                       NA, NA, 2L, 2L, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 
                                       NA, 1L, 2L, 1L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, NA, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, 
                                       NA, 2L, NA, NA, 2L, 2L, 2L, 1L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       NA, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, NA, 1L, NA, 2L, 2L, 2L, NA, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, NA, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, NA, NA, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
                                       2L, 1L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
                                       2L, NA, 2L, 2L, 1L, NA, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
                                       1L, 1L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, NA, 
                                       2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, NA, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, NA, 
                                       NA, NA, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, NA, 2L, 
                                       NA, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 1L, 1L, NA, 2L, 2L, 3L, 2L, 2L, NA, 2L, 2L, NA, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 1L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, NA, 
                                       2L, NA, 1L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L), levels = c("A", 
                                                                                               "B", "C"), class = "factor"), 
                       x = structure(c(3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
                                       1L, 3L, 3L, 1L, 1L, 2L, NA, NA, NA, 2L, 3L, 2L, 2L, 2L, 2L, 
                                       3L, 3L, 3L, 3L, 1L, 2L, NA, NA, 1L, 3L, 2L, 3L, NA, 3L, NA, 
                                       1L, 1L, 2L, NA, 1L, NA, 3L, 1L, 2L, 1L, 1L, NA, 2L, 2L, 1L, 
                                       2L, 2L, 3L, 3L, 3L, 2L, 2L, 1L, NA, NA, 3L, 3L, NA, 2L, 2L, 
                                       3L, 1L, 3L, 3L, 3L, 2L, 3L, NA, NA, NA, 1L, 3L, NA, 2L, NA, 
                                       1L, 1L, 3L, 2L, 1L, NA, 2L, 1L, 3L, 2L, 3L, NA, NA, NA, 3L, 
                                       3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, NA, NA, 3L, 1L, 3L, 3L, 
                                       NA, 2L, 2L, 3L, 2L, 1L, 3L, NA, NA, 3L, NA, 3L, 2L, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, NA, 3L, 1L, 2L, 3L, 2L, 
                                       NA, NA, 1L, 3L, NA, 2L, NA, NA, 1L, 3L, NA, 3L, 1L, NA, 2L, 
                                       3L, NA, NA, 2L, NA, 1L, 1L, 3L, 2L, 2L, 2L, 3L, NA, 1L, NA, 
                                       2L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 
                                       2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, NA, 1L, NA, 2L, 
                                       2L, 2L, 2L, 1L, NA, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 1L, 
                                       1L, 2L, 1L, 1L, NA, 2L, NA, 2L, 1L, 1L, 1L, 1L, NA, 3L, NA, 
                                       2L, 1L, 1L, 3L, 3L, NA, 1L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 
                                       2L, 3L, 2L, 3L, 2L, 1L, NA, 3L, 1L, NA, 1L, 1L, 2L, 3L, 3L, 
                                       3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 
                                       3L, 2L, 2L, 2L, 1L, 2L, NA, 1L, 2L, 3L, 2L, 3L, 2L, NA, 2L, 
                                       2L, NA, 3L, 2L, NA, 2L, 3L, NA, 3L, 2L, NA, 3L, 2L, 2L, NA, 
                                       2L, 3L, NA, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 
                                       NA, 2L, 1L, 3L, 3L, 1L, 2L, NA, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 
                                       1L, 2L, 2L, NA, 2L, 3L, 2L, NA, NA, 3L, 3L, 1L, 1L, 2L, 2L, 
                                       NA, NA, 1L, 2L, 3L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
                                       3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, NA, 3L, NA, 3L, 
                                       3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 
                                       3L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, NA, NA, 3L, 3L, NA, 2L, 
                                       3L, 1L, 1L, NA, 2L, 1L, 2L, NA, 3L, 3L, 2L, 1L, NA, 2L, 3L, 
                                       3L, 1L, 2L, 3L, 2L, 1L, NA, 3L, 2L, 3L, NA, 3L, 2L, 2L, 2L, 
                                       2L, 1L, 2L, 2L, 2L, 2L, 2L, NA, NA, 3L, 2L, 1L, 3L, NA, 2L, 
                                       3L, 2L, 2L, 3L, 2L, 3L, 3L, NA, 3L, NA, NA, 2L, 2L, 2L, 3L, 
                                       3L, 3L, NA, NA, 1L, 2L, NA, NA, NA, 2L, 2L, 3L, 3L, 1L, 2L, 
                                       2L, NA, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, NA, 3L, 1L, NA, 
                                       1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 1L, 3L, 
                                       2L, 3L, NA, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                       NA, NA, 3L, NA, NA, 3L, 2L, 2L, 2L, 2L, 2L, 3L, NA, NA, 3L, 
                                       2L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, NA, 
                                       1L, NA, 3L, 3L, 1L, 2L, 3L, 2L, NA, NA, 2L, 2L, 3L, 1L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, NA, 3L, 1L, 3L, 2L, 
                                       2L, 2L, 1L, 2L, NA, 1L, 3L, 2L, 1L, 1L, 3L, 1L, NA, 2L, NA, 
                                       NA, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, NA, 3L, NA, 
                                       2L, 2L, NA, 1L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, NA, NA, 
                                       1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 
                                       2L, 1L, 2L, NA, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 
                                       3L, NA, NA, 2L, 2L, NA, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 
                                       2L, 2L, 3L, 3L, 3L), levels = c("A", "B", 
                                                                       "C"), class = "factor")), row.names = NULL, class = c("data.frame"))
chisq.test(data$y, data$x)
#> 
#>  Pearson's Chi-squared test
#> 
#> data:  data$y and data$x
#> X-squared = 33.258, df = 2, p-value = 5.999e-08
chisq_test(data,response=y, explanatory = x)
#> Warning in stats::chisq.test(table(x), ...): Chi-squared approximation may be
#> incorrect
#> # A tibble: 1 × 3
#>   statistic chisq_df p_value
#>       <dbl>    <int>   <dbl>
#> 1       NaN        4     NaN

Created on 2023-11-11 with reprex v2.0.2
@simonpcouch
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Collaborator

Thank you for the issue and reprex @sda030! What you're seeing is actually an inconsistency of chisq.test(). The function ignores unused factor levels with data.frame input but not table input:

library(infer)
data <- structure(list(y = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 
                                       2L, 2L, 2L, 1L, 2L, NA, 1L, NA, 2L, 2L, 3L, 3L, 2L, NA, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 1L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, NA, 2L, 1L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, NA, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       NA, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 
                                       2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, NA, NA, 2L, 2L, 2L, 3L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, 2L, 2L, 1L, NA, 1L, NA, 
                                       NA, NA, 2L, 2L, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 
                                       NA, 1L, 2L, 1L, NA, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, NA, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, 
                                       NA, 2L, NA, NA, 2L, 2L, 2L, 1L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       NA, 2L, 2L, NA, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, NA, 1L, NA, 2L, 2L, 2L, NA, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, NA, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, NA, NA, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, NA, 2L, NA, NA, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
                                       2L, 1L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
                                       2L, NA, 2L, 2L, 1L, NA, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
                                       1L, 1L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, NA, 2L, 2L, NA, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, NA, 
                                       2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, NA, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, NA, 
                                       NA, NA, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, NA, 2L, 
                                       NA, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 1L, 1L, NA, 2L, 2L, 3L, 2L, 2L, NA, 2L, 2L, NA, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
                                       2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
                                       2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L, NA, 1L, 
                                       2L, 2L, 2L, 2L, NA, 2L, 2L, 2L, NA, NA, 2L, 2L, 2L, 2L, NA, NA, 
                                       2L, NA, 1L, 2L, 2L, NA, 2L, 2L, 2L, 2L, 2L), levels = c("A", 
                                                                                               "B", "C"), class = "factor"), 
                       x = structure(c(3L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 
                                       1L, 3L, 3L, 1L, 1L, 2L, NA, NA, NA, 2L, 3L, 2L, 2L, 2L, 2L, 
                                       3L, 3L, 3L, 3L, 1L, 2L, NA, NA, 1L, 3L, 2L, 3L, NA, 3L, NA, 
                                       1L, 1L, 2L, NA, 1L, NA, 3L, 1L, 2L, 1L, 1L, NA, 2L, 2L, 1L, 
                                       2L, 2L, 3L, 3L, 3L, 2L, 2L, 1L, NA, NA, 3L, 3L, NA, 2L, 2L, 
                                       3L, 1L, 3L, 3L, 3L, 2L, 3L, NA, NA, NA, 1L, 3L, NA, 2L, NA, 
                                       1L, 1L, 3L, 2L, 1L, NA, 2L, 1L, 3L, 2L, 3L, NA, NA, NA, 3L, 
                                       3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, NA, NA, 3L, 1L, 3L, 3L, 
                                       NA, 2L, 2L, 3L, 2L, 1L, 3L, NA, NA, 3L, NA, 3L, 2L, 1L, NA, 
                                       2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, NA, 3L, 1L, 2L, 3L, 2L, 
                                       NA, NA, 1L, 3L, NA, 2L, NA, NA, 1L, 3L, NA, 3L, 1L, NA, 2L, 
                                       3L, NA, NA, 2L, NA, 1L, 1L, 3L, 2L, 2L, 2L, 3L, NA, 1L, NA, 
                                       2L, 3L, 3L, 1L, 1L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 3L, 
                                       2L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 2L, NA, 1L, NA, 2L, 
                                       2L, 2L, 2L, 1L, NA, 2L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 1L, 
                                       1L, 2L, 1L, 1L, NA, 2L, NA, 2L, 1L, 1L, 1L, 1L, NA, 3L, NA, 
                                       2L, 1L, 1L, 3L, 3L, NA, 1L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 2L, 
                                       2L, 3L, 2L, 3L, 2L, 1L, NA, 3L, 1L, NA, 1L, 1L, 2L, 3L, 3L, 
                                       3L, 3L, 3L, 3L, 3L, 2L, 2L, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 
                                       3L, 2L, 2L, 2L, 1L, 2L, NA, 1L, 2L, 3L, 2L, 3L, 2L, NA, 2L, 
                                       2L, NA, 3L, 2L, NA, 2L, 3L, NA, 3L, 2L, NA, 3L, 2L, 2L, NA, 
                                       2L, 3L, NA, 2L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 1L, 1L, 
                                       NA, 2L, 1L, 3L, 3L, 1L, 2L, NA, 1L, 3L, 2L, 2L, 2L, 2L, 1L, 
                                       1L, 2L, 2L, NA, 2L, 3L, 2L, NA, NA, 3L, 3L, 1L, 1L, 2L, 2L, 
                                       NA, NA, 1L, 2L, 3L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
                                       3L, 2L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, NA, 3L, NA, 3L, 
                                       3L, 1L, 2L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 3L, 3L, 2L, 2L, 2L, 
                                       3L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 2L, NA, NA, 3L, 3L, NA, 2L, 
                                       3L, 1L, 1L, NA, 2L, 1L, 2L, NA, 3L, 3L, 2L, 1L, NA, 2L, 3L, 
                                       3L, 1L, 2L, 3L, 2L, 1L, NA, 3L, 2L, 3L, NA, 3L, 2L, 2L, 2L, 
                                       2L, 1L, 2L, 2L, 2L, 2L, 2L, NA, NA, 3L, 2L, 1L, 3L, NA, 2L, 
                                       3L, 2L, 2L, 3L, 2L, 3L, 3L, NA, 3L, NA, NA, 2L, 2L, 2L, 3L, 
                                       3L, 3L, NA, NA, 1L, 2L, NA, NA, NA, 2L, 2L, 3L, 3L, 1L, 2L, 
                                       2L, NA, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, NA, 3L, 1L, NA, 
                                       1L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 1L, 3L, 
                                       2L, 3L, NA, 1L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 
                                       NA, NA, 3L, NA, NA, 3L, 2L, 2L, 2L, 2L, 2L, 3L, NA, NA, 3L, 
                                       2L, 3L, 1L, 3L, 3L, 2L, 2L, 3L, 1L, 2L, 2L, 3L, 2L, 1L, NA, 
                                       1L, NA, 3L, 3L, 1L, 2L, 3L, 2L, NA, NA, 2L, 2L, 3L, 1L, 2L, 
                                       2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, NA, 3L, 1L, 3L, 2L, 
                                       2L, 2L, 1L, 2L, NA, 1L, 3L, 2L, 1L, 1L, 3L, 1L, NA, 2L, NA, 
                                       NA, 3L, 2L, 3L, 2L, 1L, 2L, 3L, 2L, 2L, 1L, 1L, NA, 3L, NA, 
                                       2L, 2L, NA, 1L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 2L, NA, NA, 
                                       1L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 3L, 2L, 1L, 2L, 3L, 3L, 
                                       2L, 1L, 2L, NA, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 2L, 3L, 1L, 
                                       3L, NA, NA, 2L, 2L, NA, 2L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 2L, 
                                       2L, 2L, 3L, 3L, 3L), levels = c("A", "B", 
                                                                       "C"), class = "factor")), row.names = NULL, class = c("data.frame"))

chisq.test(data$y, data$x)
#> 
#>  Pearson's Chi-squared test
#> 
#> data:  data$y and data$x
#> X-squared = 33.258, df = 2, p-value = 5.999e-08

# note:
table(data)
#>    x
#> y     A   B   C
#>   A  31  21   7
#>   B  87 184 162
#>   C   0   0   0

# so:
chisq.test(table(data))
#> Warning in chisq.test(table(data)): Chi-squared approximation may be incorrect
#> 
#>  Pearson's Chi-squared test
#> 
#> data:  table(data)
#> X-squared = NaN, df = 4, p-value = NA

Created on 2023-11-13 with reprex v2.0.2

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@sda030 @simonpcouch