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?

Data frame column names quotation is one of the reasons I start to prefer python. See Tidyverse style pandas: ""“Tidyverse allows a mix of quoted and unquoted references to variable names. In my (in)experience, the convenience this brings is accompanied by equal consternation. It seems to me a lot of the problems solved by tidyeval would not exist if all variables were quoted all the time, as in pandas, but there are likely deeper truths I’m missing here…”"" — Paul Rougieux

jaimedash jaimedash · Accepted Answer · 2016-04-15T18:30:41

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

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

Example not working

3 examples of implementation

3 Answers