Is dplyr easier than data.table to be used within functions and loops?

Question

I'm comparing the convenience of dplyr vs. data.table in working within loops and functions.

For this, I'm trying to modify the code snippets used in this post: data.table vs dplyr: can one do something well the other can't or does poorly? so that, instead of hard-coded dataset variables names ("cut" and "price" variables of "diamonds" dataset), they become dataset-agnostic, i.e. cut-n-paste ready for the use inside any function or a loop (when we don't know column names in advance and need to access them by column number).

This is the original code:

tbl = diamonds
tbl %>%
  filter(cut != "Fair") %>%
  group_by(cut) %>%
  summarize(
     AvgPrice = mean(price)
  ) 

I need to rewrite it so that I can use the same code in a loop like this one:

for(nVarGroup in 2:4) # Grouped by possible categorical values...
   for(nVarMeans in 5:10) { # ... get means of all parameters

}

I've done it for data.table as shown here: How to use data.table within functions and loops?.
I'm struggling however to do the same for dplyr.
These links were recommended to resolve the problem: dplyr: How to use group_by inside a function?, https://cran.r-project.org/web/packages/dplyr/vignettes/nse.html. However, while providing solution to group_by(strVarGroup) line below, they do not not seem to provide solution to qGroup=quote(get(strVarGroup) %in% strGroupConditions) line.

nVarGroup = 2 #"cut"
nVarMeans = 7 #"price"

strVarGroup = names(dt)[nVarGroup]
strVarMeans = names(dt)[nVarMeans] 
qAction=quote(mean(strVarMeans)) 
strGroupConditions = levels(dt[[nVarGroup]])[-1] # "Good" "Very Good" "Premium" "Ideal" 
qGroup=quote(get(strVarGroup) %in% strGroupConditions) 

### DOES NOT WORK ###
tbl %>%
   filter(eval(qGroup))    %>%
   group_by(strVarGroup) %>%
   summarize(
     AvgPrice = eval(qAction),
     ) 
### END: DOES NOT WORK ###

Any additional links or ideas to help?