Creating a function with an argument passed to dplyr::filter what is the best way to work around nse?

Question

Non standard evaluation is really handy when using dplyr's verbs. But it can be problematic when using those verbs with function arguments. For example let us say that I want to create a function that gives me the number of rows for a given species.

# Load packages and prepare data
library(dplyr)
library(lazyeval)
# I prefer lowercase column names
names(iris) <- tolower(names(iris))
# Number of rows for all species
nrow(iris)
# [1] 150

Example not working

This function doesn't work as expected because species is interpreted in the context of the iris data frame instead of being interpreted in the context of the function argument:

nrowspecies0 <- function(dtf, species){
    dtf %>%
        filter(species == species) %>%
        nrow()
}
nrowspecies0(iris, species = "versicolor")
# [1] 150

3 examples of implementation

To work around non standard evaluation, I usually append the argument with an underscore :

nrowspecies1 <- function(dtf, species_){
    dtf %>%
        filter(species == species_) %>%
        nrow()
}

nrowspecies1(iris, species_ = "versicolor")
# [1] 50
# Because of function name completion the argument
# species works too
nrowspecies1(iris, species = "versicolor")
# [1] 50

It is not completely satisfactory since it changes the name of the function argument to something less user friendly. Or it relies on autocompletion which I'm afraid is not a good practice for programming. To keep a nice argument name, I could do :

nrowspecies2 <- function(dtf, species){
    species_ <- species
    dtf %>%
        filter(species == species_) %>%
        nrow()
}
nrowspecies2(iris, species = "versicolor")
# [1] 50

Another way to work around non standard evaluation based on this answer. interp() interprets species in the context of the function environment:

nrowspecies3 <- function(dtf, species){
    dtf %>%
        filter_(interp(~species == with_species, 
                       with_species = species)) %>%
        nrow()
}
nrowspecies3(iris, species = "versicolor")
# [1] 50

Considering the 3 function above, what is the preferred - most robust - way to implement this filter function? Are there any other ways?

Answer 1

The answer from @eddi is correct about what's going on here. I'm writing another answer that addresses the larger request of how to write functions using dplyr verbs. You'll note that, ultimately, it uses something like nrowspecies2 to avoid the species == species tautology.

To write a function wrapping dplyr verb(s) that will work with NSE, write two functions:

First write a version that requires quoted inputs, using lazyeval and an SE version of the dplyr verb. So in this case, filter_.

nrowspecies_robust_ <- function(data, species){ 
  species_ <- lazyeval::as.lazy(species) 
  condition <- ~ species == species_ # *
  tmp <- dplyr::filter_(data, condition) # **
  nrow(tmp)
} 
nrowspecies_robust_(iris, ~versicolor) 

Second make a version that uses NSE:

nrowspecies_robust <- function(data, species) { 
  species <- lazyeval::lazy(species) 
  nrowspecies_robust_(data, species) 
} 
nrowspecies_robust(iris, versicolor) 

* = if you want to do something more complex, you may need to use lazyeval::interp here as in the tips linked below

** = also, if you need to change output names, see the .dots argument

• For the above, I followed some tips from Hadley

• Another good resource is the dplyr vignette on NSE, which illustrates .dots, interp, and other functions from the lazyeval package

• For even more details on lazyeval see it's vignette

• For a thorough discussion of the base R tools for working with NSE (many of which lazyeval helps you avoid), see the chapter on NSE in Advanced R

Answer 2

This question has absolutely nothing to do with non standard evaluation. Let me rewrite your initial function to make that clear:

nrowspecies4 <- function(dtf, boo){
    dtf %>%
        filter(boo == boo) %>%
        nrow()
}
nrowspecies4(iris, boo = "versicolor")
#150

The expression inside your filter always evaluates to TRUE (almost always - see example below), that's why it doesn't work, not because of some NSE magic.

Your nrowspecies2 is the way to go.

Fwiw, species in your nrowspecies0 is indeed evaluated as a column, not as the input variable species, and you can check that by comparing nrowspecies0(iris, NA) to nrowspecies4(iris, NA).

Answer 3

in his 2016 UseR talk (@38min30s), Hadley Wickham explains the concept of referential transparency . Using a formula, the filter function can be reformulated as:

nrowspecies5 <- function(dtf, formula){
    dtf %>%
        filter_(formula) %>%
        nrow()
}

This has the added benefit of beeing more generic

nrowspecies5(iris, ~ species == "versicolor")
# 50
nrowspecies5(iris, ~ sepal.length > 6 & species == "virginica")
# 41
nrowspecies5(iris, ~ sepal.length > 6 & species == "setosa")
# 0