Why is enquo + !! preferable to substitute + eval

Question

In the following example, why should we favour using f1 over f2? Is it more efficient in some sense? For someone used to base R, it seems more natural to use the "substitute + eval" option.

library(dplyr)

d = data.frame(x = 1:5,
               y = rnorm(5))

# using enquo + !!
f1 = function(mydata, myvar) {
  m = enquo(myvar)
  mydata %>%
    mutate(two_y = 2 * !!m)
}

# using substitute + eval    
f2 = function(mydata, myvar) {
  m = substitute(myvar)
  mydata %>%
    mutate(two_y = 2 * eval(m))
}

all.equal(d %>% f1(y), d %>% f2(y)) # TRUE

In other words, and beyond this particular example, my question is: can I get get away with programming using dplyr NSE functions with good ol' base R like substitute+eval, or do I really need to learn to love all those rlang functions because there is a benefit to it (speed, clarity, compositionality,...)?

Answer 1

I want to give an answer that is independent of dplyr, because there is a very clear advantage to using enquo over substitute. Both look in the calling environment of a function to identify the expression that was given to that function. The difference is that substitute() does it only once, while !!enquo() will correctly walk up the entire calling stack.

Consider a simple function that uses substitute():

f <- function( myExpr ) {
  eval( substitute(myExpr), list(a=2, b=3) )
}

f(a+b)   # 5
f(a*b)   # 6

This functionality breaks when the call is nested inside another function:

g <- function( myExpr ) {
  val <- f( substitute(myExpr) )
  ## Do some stuff
  val
}

g(a+b)
# myExpr     <-- OOPS

Now consider the same functions re-written using enquo():

library( rlang )

f2 <- function( myExpr ) {
  eval_tidy( enquo(myExpr), list(a=2, b=3) )
}

g2 <- function( myExpr ) {
  val <- f2( !!enquo(myExpr) )
  val
}

g2( a+b )    # 5
g2( b/a )    # 1.5

And that is why enquo() + !! is preferable to substitute() + eval().

dplyr simply takes full advantage of this property to build a coherent set of NSE functions.

Answer 2

enquo() and !! also allows you to program with other dplyr verbs such as group_by and select. I'm not sure if substitute and eval can do that. Take a look at this example where I modify your data frame a little bit

library(dplyr)

set.seed(1234)
d = data.frame(x = c(1, 1, 2, 2, 3),
               y = rnorm(5),
               z = runif(5))

# select, group_by & create a new output name based on input supplied
my_summarise <- function(df, group_var, select_var) {

  group_var <- enquo(group_var)
  select_var <- enquo(select_var)

  # create new name
  mean_name <- paste0("mean_", quo_name(select_var))

  df %>%
    select(!!select_var, !!group_var) %>% 
    group_by(!!group_var) %>%
    summarise(!!mean_name := mean(!!select_var))
}

my_summarise(d, x, z)

# A tibble: 3 x 2
      x mean_z
  <dbl>  <dbl>
1    1.  0.619
2    2.  0.603
3    3.  0.292

Edit: also enquos & !!! make it easier to capture list of variables

# example
grouping_vars <- quos(x, y)
d %>%
  group_by(!!!grouping_vars) %>%
  summarise(mean_z = mean(z))

# A tibble: 5 x 3
# Groups:   x [?]
      x      y mean_z
  <dbl>  <dbl>  <dbl>
1    1. -1.21   0.694
2    1.  0.277  0.545
3    2. -2.35   0.923
4    2.  1.08   0.283
5    3.  0.429  0.292


# in a function
my_summarise2 <- function(df, select_var, ...) {

  group_var <- enquos(...)
  select_var <- enquo(select_var)

  # create new name
  mean_name <- paste0("mean_", quo_name(select_var))

  df %>%
    select(!!select_var, !!!group_var) %>% 
    group_by(!!!group_var) %>%
    summarise(!!mean_name := mean(!!select_var))
}

my_summarise2(d, z, x, y)

# A tibble: 5 x 3
# Groups:   x [?]
      x      y mean_z
  <dbl>  <dbl>  <dbl>
1    1. -1.21   0.694
2    1.  0.277  0.545
3    2. -2.35   0.923
4    2.  1.08   0.283
5    3.  0.429  0.292

Credit: Programming with dplyr

Answer 3

Imagine there is a different x you want to multiply:

> x <- 3
> f1(d, !!x)
  x            y two_y
1 1 -2.488894875     6
2 2 -1.133517746     6
3 3 -1.024834108     6
4 4  0.730537366     6
5 5 -1.325431756     6

vs without the !!:

> f1(d, x)
  x            y two_y
1 1 -2.488894875     2
2 2 -1.133517746     4
3 3 -1.024834108     6
4 4  0.730537366     8
5 5 -1.325431756    10

!! gives you more control over scoping than substitute - with substitute you can only get the 2nd way easily.