Is Kruskal Wallis the right statistical test to use? what post hoc formula should i use?

[rhimack99]

this is my dataset:

I have three alcohol concentrations (0,1,2) and four different qualitative grades (1, 2, 3 and 4) that I've assigned to embryos after ISH.

To show a different between the concentrations, I did a Kruskal Wallis test which gave me a p-value of 4.34e-06 (this seems quite low?). Was this the right test to use?

Also, what function/test should I use for a post hoc test to analyse between 0%:1%, 0%:2% and 1%:2%? I've tried dunn methods, pairwise.wilcox.test and wilcox.test but they won't work with my data.

here's my coding:

#alcohol conc into categorical
conc_analysis$alcoholpercentage<- as.factor(conc_analysis$alcoholpercentage)

#ordinal factor variable conversion for embryo grading
conc_analysis$embryograding = factor(conc_analysis$embryograding,
ordered = TRUE,
levels = c("1", "2", "3", "4"))

str(conc_analysis)

#stats
kruskal.test(conc_analysis$alcoholpercentage ~ conc_analysis$embryograding)

#posthoc attempt 1
pairwise.wilcox.test(conc_analysis$alcoholpercentage, conc_analysis$embryograding, p.adjust.method = "bonferroni")

#post hoc testing 2
install.packages("FSA")
library("FSA")
install.packages("dunn.test")
library("dunn.test")

wilcox.test(alcoholpercentage ~ embryograding,
data = conc_analysis,
p.adjust.method="bonferroni")

dunnTest(alcoholpercentage ~ embryograding,
data = conc_analysis,
p.adjust.method="bonferroni")