I'm trying to learn a bit about tidyeval and am a bit stuck. This example is a bit contrived but I'm experimenting.
Suppose I have a couple of strings:
AA <- "setosa"
BB <- "versicolor"
and a function like this:
testfunc <- function(data, col, ...){
enq_col <- enquo(col)
enq_vars <- enquos(...)
if(length(enq_vars) > 0) {
filter(data, !! enq_col == AA | !! enq_col == BB)
}
else{data}
}
testfunc(iris, Species, AA, BB)
This is great but is only applicable to cases where I have two variables names AA and BB. If I substitute in
filter(data, !! enq_col == !! enq_vars[[1]] | !! enq_col == !! enq_vars[[2]])
I get halfway there but I am still limited to two variables. In the vignette I see that !!! can be used with listed quosures and they pass that straight into group_by() but when I try
filter(data, !! enq_col == !!! enq_vars)
I get:
Error: Can't use !!! at top level
Similarly, quo_name(enq_col) will give me the name of the expression but is there an easy way to get a vector of names from enquos(...) ?
What am I doing wrong?
I guess I can use map_chr() from purrr:
filter(data, !! enq_col %in% map_chr(enq_vars, ~ eval_tidy(.x)))
as a workaround but I feel like there might be a tidyeval approach.
I understand what you are trying to do, but this example doesn't show the problem you are trying to solve. Since you are already working with strings, you can safely skip tidyeval and go straight to c(...) as in my example:
``` r
library(tidyverse)
library(rlang)
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, %||%, as_function, flatten, flatten_chr, flatten_dbl,
#> flatten_int, flatten_lgl, invoke, list_along, modify, prepend,
#> rep_along, splice
AA <- "setosa"
BB <- "versicolor"
testfunc <- function(data, col, ...){
enq_col <- enquo(col)
enq_vars <- c(...)
if(length(enq_vars) > 0) {
filter(data, !! enq_col %in% enq_vars)
}
else{data}
}
testfunc(iris, Species, AA, BB, "virginica")
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> 11 5.4 3.7 1.5 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3.0 1.4 0.1 setosa
#> 14 4.3 3.0 1.1 0.1 setosa
#> 15 5.8 4.0 1.2 0.2 setosa
#> 16 5.7 4.4 1.5 0.4 setosa
#> 17 5.4 3.9 1.3 0.4 setosa
#> 18 5.1 3.5 1.4 0.3 setosa
#> 19 5.7 3.8 1.7 0.3 setosa
#> 20 5.1 3.8 1.5 0.3 setosa
#> 21 5.4 3.4 1.7 0.2 setosa
#> 22 5.1 3.7 1.5 0.4 setosa
#> 23 4.6 3.6 1.0 0.2 setosa
#> 24 5.1 3.3 1.7 0.5 setosa
#> 25 4.8 3.4 1.9 0.2 setosa
#> 26 5.0 3.0 1.6 0.2 setosa
#> 27 5.0 3.4 1.6 0.4 setosa
#> 28 5.2 3.5 1.5 0.2 setosa
#> 29 5.2 3.4 1.4 0.2 setosa
#> 30 4.7 3.2 1.6 0.2 setosa
#> 31 4.8 3.1 1.6 0.2 setosa
#> 32 5.4 3.4 1.5 0.4 setosa
#> 33 5.2 4.1 1.5 0.1 setosa
#> 34 5.5 4.2 1.4 0.2 setosa
#> 35 4.9 3.1 1.5 0.2 setosa
#> 36 5.0 3.2 1.2 0.2 setosa
#> 37 5.5 3.5 1.3 0.2 setosa
#> 38 4.9 3.6 1.4 0.1 setosa
#> 39 4.4 3.0 1.3 0.2 setosa
#> 40 5.1 3.4 1.5 0.2 setosa
#> 41 5.0 3.5 1.3 0.3 setosa
#> 42 4.5 2.3 1.3 0.3 setosa
#> 43 4.4 3.2 1.3 0.2 setosa
#> 44 5.0 3.5 1.6 0.6 setosa
#> 45 5.1 3.8 1.9 0.4 setosa
#> 46 4.8 3.0 1.4 0.3 setosa
#> 47 5.1 3.8 1.6 0.2 setosa
#> 48 4.6 3.2 1.4 0.2 setosa
#> 49 5.3 3.7 1.5 0.2 setosa
#> 50 5.0 3.3 1.4 0.2 setosa
#> 51 7.0 3.2 4.7 1.4 versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor
#> 54 5.5 2.3 4.0 1.3 versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor
#> 62 5.9 3.0 4.2 1.5 versicolor
#> 63 6.0 2.2 4.0 1.0 versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor
#> 72 6.1 2.8 4.0 1.3 versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor
#> 78 6.7 3.0 5.0 1.7 versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor
#> 82 5.5 2.4 3.7 1.0 versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor
#> 84 6.0 2.7 5.1 1.6 versicolor
#> 85 5.4 3.0 4.5 1.5 versicolor
#> 86 6.0 3.4 4.5 1.6 versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor
#> 99 5.1 2.5 3.0 1.1 versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor
#> 101 6.3 3.3 6.0 2.5 virginica
#> 102 5.8 2.7 5.1 1.9 virginica
#> 103 7.1 3.0 5.9 2.1 virginica
#> 104 6.3 2.9 5.6 1.8 virginica
#> 105 6.5 3.0 5.8 2.2 virginica
#> 106 7.6 3.0 6.6 2.1 virginica
#> 107 4.9 2.5 4.5 1.7 virginica
#> 108 7.3 2.9 6.3 1.8 virginica
#> 109 6.7 2.5 5.8 1.8 virginica
#> 110 7.2 3.6 6.1 2.5 virginica
#> 111 6.5 3.2 5.1 2.0 virginica
#> 112 6.4 2.7 5.3 1.9 virginica
#> 113 6.8 3.0 5.5 2.1 virginica
#> 114 5.7 2.5 5.0 2.0 virginica
#> 115 5.8 2.8 5.1 2.4 virginica
#> 116 6.4 3.2 5.3 2.3 virginica
#> 117 6.5 3.0 5.5 1.8 virginica
#> 118 7.7 3.8 6.7 2.2 virginica
#> 119 7.7 2.6 6.9 2.3 virginica
#> 120 6.0 2.2 5.0 1.5 virginica
#> 121 6.9 3.2 5.7 2.3 virginica
#> 122 5.6 2.8 4.9 2.0 virginica
#> 123 7.7 2.8 6.7 2.0 virginica
#> 124 6.3 2.7 4.9 1.8 virginica
#> 125 6.7 3.3 5.7 2.1 virginica
#> 126 7.2 3.2 6.0 1.8 virginica
#> 127 6.2 2.8 4.8 1.8 virginica
#> 128 6.1 3.0 4.9 1.8 virginica
#> 129 6.4 2.8 5.6 2.1 virginica
#> 130 7.2 3.0 5.8 1.6 virginica
#> 131 7.4 2.8 6.1 1.9 virginica
#> 132 7.9 3.8 6.4 2.0 virginica
#> 133 6.4 2.8 5.6 2.2 virginica
#> 134 6.3 2.8 5.1 1.5 virginica
#> 135 6.1 2.6 5.6 1.4 virginica
#> 136 7.7 3.0 6.1 2.3 virginica
#> 137 6.3 3.4 5.6 2.4 virginica
#> 138 6.4 3.1 5.5 1.8 virginica
#> 139 6.0 3.0 4.8 1.8 virginica
#> 140 6.9 3.1 5.4 2.1 virginica
#> 141 6.7 3.1 5.6 2.4 virginica
#> 142 6.9 3.1 5.1 2.3 virginica
#> 143 5.8 2.7 5.1 1.9 virginica
#> 144 6.8 3.2 5.9 2.3 virginica
#> 145 6.7 3.3 5.7 2.5 virginica
#> 146 6.7 3.0 5.2 2.3 virginica
#> 147 6.3 2.5 5.0 1.9 virginica
#> 148 6.5 3.0 5.2 2.0 virginica
#> 149 6.2 3.4 5.4 2.3 virginica
#> 150 5.9 3.0 5.1 1.8 virginica
testfunc(iris, Species, AA, BB)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> 11 5.4 3.7 1.5 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3.0 1.4 0.1 setosa
#> 14 4.3 3.0 1.1 0.1 setosa
#> 15 5.8 4.0 1.2 0.2 setosa
#> 16 5.7 4.4 1.5 0.4 setosa
#> 17 5.4 3.9 1.3 0.4 setosa
#> 18 5.1 3.5 1.4 0.3 setosa
#> 19 5.7 3.8 1.7 0.3 setosa
#> 20 5.1 3.8 1.5 0.3 setosa
#> 21 5.4 3.4 1.7 0.2 setosa
#> 22 5.1 3.7 1.5 0.4 setosa
#> 23 4.6 3.6 1.0 0.2 setosa
#> 24 5.1 3.3 1.7 0.5 setosa
#> 25 4.8 3.4 1.9 0.2 setosa
#> 26 5.0 3.0 1.6 0.2 setosa
#> 27 5.0 3.4 1.6 0.4 setosa
#> 28 5.2 3.5 1.5 0.2 setosa
#> 29 5.2 3.4 1.4 0.2 setosa
#> 30 4.7 3.2 1.6 0.2 setosa
#> 31 4.8 3.1 1.6 0.2 setosa
#> 32 5.4 3.4 1.5 0.4 setosa
#> 33 5.2 4.1 1.5 0.1 setosa
#> 34 5.5 4.2 1.4 0.2 setosa
#> 35 4.9 3.1 1.5 0.2 setosa
#> 36 5.0 3.2 1.2 0.2 setosa
#> 37 5.5 3.5 1.3 0.2 setosa
#> 38 4.9 3.6 1.4 0.1 setosa
#> 39 4.4 3.0 1.3 0.2 setosa
#> 40 5.1 3.4 1.5 0.2 setosa
#> 41 5.0 3.5 1.3 0.3 setosa
#> 42 4.5 2.3 1.3 0.3 setosa
#> 43 4.4 3.2 1.3 0.2 setosa
#> 44 5.0 3.5 1.6 0.6 setosa
#> 45 5.1 3.8 1.9 0.4 setosa
#> 46 4.8 3.0 1.4 0.3 setosa
#> 47 5.1 3.8 1.6 0.2 setosa
#> 48 4.6 3.2 1.4 0.2 setosa
#> 49 5.3 3.7 1.5 0.2 setosa
#> 50 5.0 3.3 1.4 0.2 setosa
#> 51 7.0 3.2 4.7 1.4 versicolor
#> 52 6.4 3.2 4.5 1.5 versicolor
#> 53 6.9 3.1 4.9 1.5 versicolor
#> 54 5.5 2.3 4.0 1.3 versicolor
#> 55 6.5 2.8 4.6 1.5 versicolor
#> 56 5.7 2.8 4.5 1.3 versicolor
#> 57 6.3 3.3 4.7 1.6 versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor
#> 59 6.6 2.9 4.6 1.3 versicolor
#> 60 5.2 2.7 3.9 1.4 versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor
#> 62 5.9 3.0 4.2 1.5 versicolor
#> 63 6.0 2.2 4.0 1.0 versicolor
#> 64 6.1 2.9 4.7 1.4 versicolor
#> 65 5.6 2.9 3.6 1.3 versicolor
#> 66 6.7 3.1 4.4 1.4 versicolor
#> 67 5.6 3.0 4.5 1.5 versicolor
#> 68 5.8 2.7 4.1 1.0 versicolor
#> 69 6.2 2.2 4.5 1.5 versicolor
#> 70 5.6 2.5 3.9 1.1 versicolor
#> 71 5.9 3.2 4.8 1.8 versicolor
#> 72 6.1 2.8 4.0 1.3 versicolor
#> 73 6.3 2.5 4.9 1.5 versicolor
#> 74 6.1 2.8 4.7 1.2 versicolor
#> 75 6.4 2.9 4.3 1.3 versicolor
#> 76 6.6 3.0 4.4 1.4 versicolor
#> 77 6.8 2.8 4.8 1.4 versicolor
#> 78 6.7 3.0 5.0 1.7 versicolor
#> 79 6.0 2.9 4.5 1.5 versicolor
#> 80 5.7 2.6 3.5 1.0 versicolor
#> 81 5.5 2.4 3.8 1.1 versicolor
#> 82 5.5 2.4 3.7 1.0 versicolor
#> 83 5.8 2.7 3.9 1.2 versicolor
#> 84 6.0 2.7 5.1 1.6 versicolor
#> 85 5.4 3.0 4.5 1.5 versicolor
#> 86 6.0 3.4 4.5 1.6 versicolor
#> 87 6.7 3.1 4.7 1.5 versicolor
#> 88 6.3 2.3 4.4 1.3 versicolor
#> 89 5.6 3.0 4.1 1.3 versicolor
#> 90 5.5 2.5 4.0 1.3 versicolor
#> 91 5.5 2.6 4.4 1.2 versicolor
#> 92 6.1 3.0 4.6 1.4 versicolor
#> 93 5.8 2.6 4.0 1.2 versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor
#> 95 5.6 2.7 4.2 1.3 versicolor
#> 96 5.7 3.0 4.2 1.2 versicolor
#> 97 5.7 2.9 4.2 1.3 versicolor
#> 98 6.2 2.9 4.3 1.3 versicolor
#> 99 5.1 2.5 3.0 1.1 versicolor
#> 100 5.7 2.8 4.1 1.3 versicolor
Created on 2018-03-14 by the reprex package (v0.2.0).
Notice, that you can use any number of variables in your ..., without using rlang.
4 Likes
I especially liked your error handler for 0-length vars, that says that you have field experience
3 Likes
Thanks @mishabalyasin and I see how using c(...) obtains the same result. However I was hoping to gain more insight into enquos(...)
How about this, another aspect of a quosure is the name. If I add that constraint in how do I access both the string and the name?
Note there are two outputs
library(tidyverse)
library(rlang)
#>
#> Attaching package: 'rlang'
#> The following objects are masked from 'package:purrr':
#>
#> %@%, %||%, as_function, flatten, flatten_chr, flatten_dbl,
#> flatten_int, flatten_lgl, invoke, list_along, modify, prepend,
#> rep_along, splice
#> The following object is masked from 'package:tibble':
#>
#> has_name
AA <- "setosa"
BB <- "versicolor"
testfunc <- function(data, col, ...){
enq_col <- enquo(col)
enq_vars <- enquos(...)
if(length(enq_vars) > 0) {
filter(data, !! enq_col %in% enq_vars)
}
else{data}
print(head(data))
print(map_chr(enq_vars, ~ quo_name(.x)))
}
testfunc(iris, Species, AA, BB)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#>
#> "AA" "BB"
Created on 2018-03-15 by the reprex package (v0.2.0).
And what is the result you want to get?
BTW, I have to say, enquos is a thing I also want to understand
danr
March 14, 2018, 8:03pm
7
I would say the general use case for enquo and friends would when you want to make a syntax that makes sense to a subject matter expert rather than a programmer
https://s3.amazonaws.com/danal-r/quopotl.html
There have been some changes to rlang since I wrote this so some of the example may break.
3 Likes
. So if there is a very clear use-case of what one can do with it, I'll be glad to use it as well.
I've made some examples here.