Friedman test gives back error: not an unreplicated complete block design. How do I fix this?

Question

Whatever I try, I keep getting the error 'not an unreplicated complete block design' when I try to perform a Friedman test in R. I have a dataset with the total number of flower visits that I counted on different shrub species, which were then divided into different categories (locations). Since the data did not meet the two-way ANOVA assumptions I turned to the Friedman test. My data frame is as follows:

> diversity_columns
# A tibble: 74 × 3
   Category_ID Species         Total_visits
   <fct>       <fct>                  <dbl>
 1 UN          Common hawthorn           22
 2 UN          Common hawthorn           42
 3 UN          Common hawthorn            3
 4 UN          Common hawthorn           13
 5 UN          Common hawthorn           76
 6 UN          Common hawthorn           95
 7 UN          Common hawthorn           53
 8 RN          Common hawthorn           50
 9 RN          Common hawthorn           18
10 UN          Common hawthorn            6

The labels for the Category_IN are "RA", "RN" and "UN" and for Species "Common hawthorn", "Blackberry" and "Rose"

I tried the following command:

friedman.test(Total_visits ~ Category_ID | Species, data = diversity_columns)

However, I got this error in return:

Error in friedman.test.default(mf[[1L]], mf[[2L]], mf[[3L]]) : 
  not an unreplicated complete block design

I already tried to transform my dataset with the function as.matrix:

as.matrix(diversity_columns)

And I also tried transformed my variables into factors:

Shannon_diversity$Species <- factor(Shannon_diversity$Species)
Shannon_diversity$Category_ID <- factor(Shannon_diversity$Category_ID)

Answer 1

This indicates that your missing at least one of the six cross-categories of Species/Category.ID.

You can see which one(s) are missing by using table:

with(diversity_columns, table(Category_ID, Species))

I can replicate this error using the built-in warpbreaks dataset.

wb <- aggregate(warpbreaks$breaks,
                by = list(w = warpbreaks$wool,
                          t = warpbreaks$tension),
                FUN = mean)
wb

---

  w t        x
1 A L 44.55556
2 B L 28.22222
3 A M 24.00000
4 B M 28.77778
5 A H 24.55556
6 B H 18.77778

friedman.test(x ~ w | t, data = wb)

---

    Friedman rank sum test

data:  x and w and t
Friedman chi-squared = 0.33333, df = 1, p-value = 0.5637

This works. Now remove one of the rows from wb:

wb2 <- wb[-2,]
with(wb2, table(w, t))

---

   t
w   L M H
  A 1 1 1
  B 0 1 1

friedman.test(x ~ w | t, data = wb2)
#Error in friedman.test.default(mf[[1L]], mf[[2L]], mf[[3L]]) : 
  #not an unreplicated complete block design