R lazyeval: pass parameters to dplyr::filter

Question

I think this questions has multiple other variations (eg: here, here and perhaps here) - probably even an answer somewhere.

How to provide parameters to the filter function.

library(dplyr)
library(lazyeval)
set.seed(10)
data <- data.frame(a=sample(1:10, 100, T))

If I need to count the number of occurrences of the number 1 through 10 occurs and show the count for, say 1, 2 and 3, I would do this:

data %>% 
  group_by(a) %>% 
  summarise(n = n()) %>% 
  filter(a < 4)

Gives:

# A tibble: 3 × 2
      a     n
  <int> <int>
1     1    11
2     2     8
3     3    16

Now, how can I put this into a function ? Here grp is the grouping var.

   fun <- function(d, grp, no){
      d %>% 
        group_by_(grp) %>% 
        summarise_(n = interp(~ n() )) %>%
        filter_( grp < no)
        # This final line instead also does not work:  
        # filter_(interp( grp < no), grp = as.name(grp))
    }

Now,

fun(data, 'a', 4)

Gives:

# A tibble: 0 × 2
# ... with 2 variables: a <int>, n <int>

Answer 1

We can use the quosures approach from the devel version of dplyr (soon to be released 0.6.0)

fun <- function(d, grp, no){
   grp <- enquo(grp)

   d %>% 
      group_by(UQ(grp)) %>% 
      summarise(n = n() )%>%
       filter(UQ(grp) < no)

}

fun(data, a, 4)
# A tibble: 3 x 2
#      a     n
#  <int> <int>
#1     1    11
#2     2     8
#3     3    16

We use enquo to take the input argument and convert it to quosure, within the group_by/summarise/mutate, the quosure is evaluated by unquoting (UQ or !!)

---

The above function can be also modified to take both quoted and unquoted arguments

fun <- function(d, grp, no){
   lst <- as.list(match.call())
   grp <- if(is.character(lst$grp)) {
              rlang::parse_quosure(grp)
            } else enquo(grp)


   d %>% 
      group_by(UQ(grp)) %>% 
      summarise(n = n() )%>%
       filter(UQ(grp) < no)

}




fun(data, a, 4)
# A tibble: 3 x 2
#      a     n
#  <int> <int>
#1     1    11
#2     2     8
#3     3    16

 fun(data, 'a', 4)
# A tibble: 3 x 2
#      a     n
#  <int> <int>
#1     1    11
#2     2     8
#3     3    16