While going over some of the discussion related to my last post I came up with a really neat way to use wrapr::let() and rlang/tidyeval together.
Please read on to see the situation and example.Suppose we want to parameterize over a couple of names, one denoting a variable coming from the current environment and one denoting a column name. Further suppose we are worried the two names may be the same.
We can actually handle this quite neatly, using rlang/tidyeval to denote intent (in this case using “!!” to specify “take from environment instead of the data frame”) and allowing wrapr::let() to perform the substitutions.
suppressPackageStartupMessages(library("dplyr"))
library("wrapr")
mass_col_name = 'mass'
mass_const_name = 'mass'
mass %
transmute(height,
(!! MASS_CONST), # `mass` from environment
MASS_COL, # `mass` from data.frame
h100 = height * (!! MASS_CONST), # env
hm = height * MASS_COL # data
) %>%
head()
)
#> # A tibble: 6 x 5
#> height `(100)` mass h100 hm
#>
#> 1 172 100 77 17200 13244
#> 2 167 100 75 16700 12525
#> 3 96 100 32 9600 3072
#> 4 202 100 136 20200 27472
#> 5 150 100 49 15000 7350
#> 6 178 100 120 17800 21360
All in all, that is pretty neat.
(Note: rlang/tidyeval uses “(!! )” deference notation in a number of ways, here we are only using it to specify environment, not for substitution.)
Categories: Opinion Programming Statistics
jmount
Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.
Some new
wrapr::let()features (found in the development version) includeeval=FALSEanddebugPrint=TRUEmodes. With these options you can see what would be executed or what is being executed. These are great for learningwrapr::let()and debugging.For example in our above example we could run:
let( c(MASS_COL = mass_col_name, MASS_CONST = mass_const_name), starwars %>% transmute(height, (!! MASS_CONST), # `mass` from environment MASS_COL, # `mass` from data.frame h100 = height * (!! MASS_CONST), # env hm = height * MASS_COL # data ) %>% head(), eval = FALSE )This results in:
which is exactly the code re-written by
wrapr::let()has prepared for execution (one can even pass it toeval()for execution). This is an excellent way to see whatwrapr::let()does, and work out if it does what you want. The “(!(!mass))” is just howRrepresents(!! mass), and as you see executes the same.With
debugPrint=TRUEwrapr::let()both prints the replaced expression and then executes as usual.If you want to be very strict (and completely unambiguous) you can use the
.data$pronoun form to force references to thedata.frame. We show this below.suppressPackageStartupMessages(library("dplyr")) library("wrapr") mass_col_name = 'mass' mass_const_name = 'mass' mass <- 100 let( c(MASS_COL = mass_col_name, MASS_CONST = mass_const_name), debugPrint = TRUE, starwars %>% transmute(height, (!! MASS_CONST), # `mass` from environment const = .data$MASS_COL, # `mass` from data.frame h100 = height * (!! MASS_CONST), # env hm = height * .data$MASS_COL # data ) %>% head() )We do not currently recommend using the pronoun in the form
.data[[my_var]]. If you use `rlang`/`tidyeval` to perform substitutions *always* write something such as.data[[!!my_var]](some details here). This is due to complications described in `dplyr` issues 2904 and 2916.This is one of the reasons we advise using `wrapr::let()` for substitution, even if you are using `rlang`/`tidyeval` (hence why you might end up using them together).
The `rlang`/`tidyeval` substitution issues can be subtle and are possibly why the data-pronoun example in the actual `June 13, 2017 dplyr 0.7.0` announcement is not correct even using the development version of `dplyr` and `rlang`/`tidyeval` as of June 30, 2017.
Notice when we re-run the start of example the
data.frameis altered in an unexpected way (an extra column named “my_var” is added) and the data is grouped by the column “my_var“, and not by the column “homeworld” as in the earlier non-pronoun example (which presumably this example was supposed to match). This will be an issue if one tries to use or join this data after a `summarize()` step, as only named variables and the grouping variable survive `summarize()` (so the “homeworld” will not be present for downstream code expecting to use it).suppressPackageStartupMessages(library("dplyr")) packageVersion("dplyr") #> [1] '0.7.1.9000' packageVersion("rlang") #> [1] '0.1.1.9000' my_var <- "homeworld" starwars %>% group_by(.data[[my_var]]) #> # A tibble: 87 x 14 #> # Groups: my_var [49] #> name height mass hair_color skin_color eye_color #> <chr> <int> <dbl> <chr> <chr> <chr> #> 1 Luke Skywalker 172 77 blond fair blue #> 2 C-3PO 167 75 <NA> gold yellow #> 3 R2-D2 96 32 <NA> white, blue red #> 4 Darth Vader 202 136 none white yellow #> 5 Leia Organa 150 49 brown light brown #> 6 Owen Lars 178 120 brown, grey light blue #> 7 Beru Whitesun lars 165 75 brown light blue #> 8 R5-D4 97 32 <NA> white, red red #> 9 Biggs Darklighter 183 84 black light brown #> 10 Obi-Wan Kenobi 182 77 auburn, white fair blue-gray #> # ... with 77 more rows, and 8 more variables: birth_year <dbl>, #> # gender <chr>, homeworld <chr>, species <chr>, films <list>, #> # vehicles <list>, starships <list>, my_var <chr>