R: How to for loop a multiple linear regression which drops factors with <2 levels

Question

I am trying to loop a multiple linear regression and automatically drop factors which don't have at least two levels to avoid the following error message:

Error in contrasts<- ( *tmp* , value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels*

Right now my code is:

df %>% 
  group_by(crop_name) %>% 
    do(tidy(lm(formula = value ~ intercrop + 
erosion_c + purchased_seed + inorg_pest +
 org_pest + landscape + fert + inorgfert,
             data = . )))

The problem is, some crops have a large sample size with plenty of points for all the variables I'm regressing, while others have a very small sample size and zero received a given treatment (ie, no blood fruit crops were intercropped, etc.).

Is there a way to within the for loop tell R to regress what it can, drop everything else, and avoid this error message?

Answer 1

I am quite new, so this may not be the best way. You may need to set up a for loop with crop_name, as in my example df is your subset for one crop group.

df <- data.frame(intercrop = c("A","B","C","A","B","C"),
                   erosion_c = c("A","D","C","A","B","C"),
                   purchased_seed = c("A","B","D","F","E","C"),
                   inorg_pest = c("A","B","C","A","B","C"),
                   org_pest = c("A","B","A","A","B","B"),
                   landscape = c("A","A","A","A","A","A"),
                   fert = c("A","B","C","A","B","C"),
                   inorgfert = c("A","B","C","A","B","C")
                   )


yo <- sapply(df, levels)
hi <- as.data.frame(c(NA))
for(i in 1:length(yo)){
  hi[i] <- length(yo[[i]])
  names(hi)[i] <- names(df[i])
}

hi <- subset(as.data.frame(t(hi)), V1 >= 2)

formu <- row.names(hi)
formu <- as.formula(paste("value ~ ",gsub('.{3}$', '', paste( unlist(paste(formu,"+ ")), collapse=''))))