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I am trying to look at the interaction of many categorical variables. I am trying to gather P-values. I found code here that appears to be similar to what I am looking to do, but I can't seem to get the code to function correctly with my data.

I comb my data to first look at variables and make a list of every possible variable combination. But when I attempt to use the function and apply it to every variable combination, it throws me an error in the chisq.test portion of the code so that df_res is never created.

catOnly.test contains data like (all categorical except the last two).

Any suggestions?

V_NP_001  001  FU12Month   V   N   0   4   None   0.000   1.734




  f = function(x,y) {
  tbl = as.data.frame(catOnly.test)%>% select(x,y) %>% table()
  chisq_pval = round(chisq.test(tbl)$p.value, 4)
  cramV = round(cramersV(tbl), 4)
  data.frame(x, y, chisq_pval, cramV) }

# create unique combinations of column names
# sorting will help getting a better plot (upper triangular)
df_comb = data.frame(t(combn(sort(names(catOnly.test)), 4)), stringsAsFactors = F)

# apply function to each variable combination
df_res = map2_df(df_comb$X1, df_comb$X2, f)

# plot results
df_res %>%
  ggplot(aes(x,y,fill=chisq_pval))+
  geom_tile()+
  geom_text(aes(x,y,label=cramV))+
  scale_fill_gradient(low="red", high="yellow")+
  theme_classic()
print(df_res)
James Jean IV
  • 1
  • It's easier to help you if you include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. – MrFlick Jul 01 '21 at 16:55

0 Answers0