Pass arguments to dplyr functions

I want to parameterise the following computation using dplyr that finds which values of Sepal.Length are associated with more than one value of Sepal.Width:

library(dplyr)

iris %>%
    group_by(Sepal.Length) %>%
    summarise(n.uniq=n_distinct(Sepal.Width)) %>%
    filter(n.uniq > 1)

Normally I would write something like this:

not.uniq.per.group <- function(data, group.var, uniq.var) {
    iris %>%
        group_by(group.var) %>%
        summarise(n.uniq=n_distinct(uniq.var)) %>%
        filter(n.uniq > 1)
}

However, this approach throws errors because dplyr uses non-standard evaluation. How should this function be written?

You need to use the standard evaluation versions of the dplyr functions (just append '_' to the function names, ie. group_by_ & summarise_) and pass strings to your function, which you then need to turn into symbols. To parameterise the argument of summarise_, you will need to use interp(), which is defined in the lazyeval package. Concretely:

library(dplyr)
library(lazyeval)

not.uniq.per.group <- function(df, grp.var, uniq.var) {
    df %>%
        group_by_(grp.var) %>%
        summarise_( n_uniq=interp(~n_distinct(v), v=as.name(uniq.var)) ) %>%
        filter(n_uniq > 1)
}

not.uniq.per.group(iris, "Sepal.Length", "Sepal.Width")

Note that in recent versions of dplyr the standard evaluation versions of the dplyr functions have been "soft deprecated" in favor of non-standard evaluation.

See the Programming with dplyr vignette for more information on working with non-standard evaluation.

Like the old dplyr versions up to 0.5, the new dplyr has facilities for both standard evaluation (SE) and nonstandard evaluation (NSE). But they are expressed differently than before.

If you want an NSE function, you pass bare expressions and use enquo to capture them as quosures. If you want an SE function, just pass quosures (or symbols) directly, then unquote them in the dplyr calls. Here is the SE solution to the question:

library(tidyverse)
library(rlang)

f1 <- function(df, grp.var, uniq.var) {
   df %>%
       group_by(!!grp.var) %>%
       summarise(n_uniq = n_distinct(!!uniq.var)) %>%
       filter(n_uniq > 1)  
}

a <- f1(iris, quo(Sepal.Length), quo(Sepal.Width))
b <- f1(iris, sym("Sepal.Length"), sym("Sepal.Width"))
identical(a, b)
#> [1] TRUE

Note how the SE version enables you to work with string arguments - just turn them into symbols first using sym(). For more information, see the programming with dplyr vignette.

In the devel version of dplyr (soon to be released 0.6.0), we can also make use of slightly different syntax for passing the variables.

f1 <- function(df, grp.var, uniq.var) {
   grp.var <- enquo(grp.var)
   uniq.var <- enquo(uniq.var)

   df %>%
       group_by(!!grp.var) %>%
       summarise(n_uniq = n_distinct(!!uniq.var)) %>%
       filter(n_uniq >1)  


}

res2 <- f1(iris, Sepal.Length, Sepal.Width) 
res1 <- not.uniq.per.group(iris, "Sepal.Length", "Sepal.Width")
identical(res1, res2)
#[1] TRUE

Here enquo takes the arguments and returns the value as a quosure (similar to substitute in base R) by evaluating the function arguments lazily and inside the summarise, we ask it to unquote (!! or UQ) so that it gets evaluated.

Here's the way to do it from rlang 0.4 using curly curly {{ pseudo operator :

library(dplyr)

not.uniq.per.group <- function(data, group.var, uniq.var) {
  data %>%
    group_by({{group.var}}) %>%
    summarise(n.uniq=n_distinct({{uniq.var}})) %>%
    filter(n.uniq > 1)
}

iris %>% not.uniq.per.group(Sepal.Length, Sepal.Width)
#> # A tibble: 25 x 2
#>    Sepal.Length n.uniq
#>           <dbl>  <int>
#>  1          4.4      3
#>  2          4.6      4
#>  3          4.8      3
#>  4          4.9      5
#>  5          5        8
#>  6          5.1      6
#>  7          5.2      4
#>  8          5.4      4
#>  9          5.5      6
#> 10          5.6      5
#> # ... with 15 more rows

In the current version of dplyr (0.7.4) the use of the standard evaluation function versions (appended '_' to the function name, e.g. group_by_) is deprecated. Instead you should rely on tidyeval when writing functions.

Here's an example of how your function would look then:

# definition of your function
not.uniq.per.group <- function(data, group.var, uniq.var) {
  # enquotes variables to be used with dplyr-functions
  group.var <- enquo(group.var)
  uniq.var <- enquo(uniq.var)

  # use '!!' before parameter names in dplyr-functions
  data %>%
    group_by(!!group.var) %>%
    summarise(n.uniq=n_distinct(!!uniq.var)) %>%
    filter(n.uniq > 1)
}

# call of your function
not.uniq.per.group(iris, Sepal.Length, Sepal.Width)

If you want to learn all about the details, there's an excellent vignette by the dplyr-team on how this works.

Pass arguments to dplyr functions

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