Why is using list() critical for .dots = setNames() uses in dplyr?
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I wrote an answer
about why setNames() shows up sometimes in standard evaluation with dplyr.
My explanation turned into a mini-tutorial on why those standard evaluation
functions have a .dots argument. The basic idea is that the usual variadic
argument ... is a series of expressions that get evaluated inside of the
dataframe.
library("dplyr")
# standardize and round
z_round <- . %>% scale %>% as.numeric %>% round(2)
# The two expressions defining zSL, zSW are the `...`
iris %>%
mutate_(zSL = ~ z_round(Sepal.Length),
zSW = ~ z_round(Sepal.Width)) %>%
tbl_df
#> # A tibble: 150 × 7
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species zSL zSW
#> <dbl> <dbl> <dbl> <dbl> <fctr> <dbl> <dbl>
#> 1 5.1 3.5 1.4 0.2 setosa -0.90 1.02
#> 2 4.9 3.0 1.4 0.2 setosa -1.14 -0.13
#> 3 4.7 3.2 1.3 0.2 setosa -1.38 0.33
#> 4 4.6 3.1 1.5 0.2 setosa -1.50 0.10
#> 5 5.0 3.6 1.4 0.2 setosa -1.02 1.25
#> 6 5.4 3.9 1.7 0.4 setosa -0.54 1.93
#> 7 4.6 3.4 1.4 0.3 setosa -1.50 0.79
#> 8 5.0 3.4 1.5 0.2 setosa -1.02 0.79
#> 9 4.4 2.9 1.4 0.2 setosa -1.74 -0.36
#> 10 4.9 3.1 1.5 0.1 setosa -1.14 0.10
#> # ... with 140 more rows
If we programmatically assemble or manipulate those expressions before calling
mutate_, we can’t use that ..., because we have a list of expressions, not
a series of individual expressions. We use the .dots argument instead.
exps <- list(
zSL = ~ z_round(Sepal.Length),
zSW = ~ z_round(Sepal.Width)
)
iris %>% mutate_(exps)
#> Error in UseMethod("as.lazy"): no applicable method for 'as.lazy' applied to an object of class "list"
iris %>% mutate_(.dots = exps) %>% tbl_df
#> # A tibble: 150 × 7
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species zSL zSW
#> <dbl> <dbl> <dbl> <dbl> <fctr> <dbl> <dbl>
#> 1 5.1 3.5 1.4 0.2 setosa -0.90 1.02
#> 2 4.9 3.0 1.4 0.2 setosa -1.14 -0.13
#> 3 4.7 3.2 1.3 0.2 setosa -1.38 0.33
#> 4 4.6 3.1 1.5 0.2 setosa -1.50 0.10
#> 5 5.0 3.6 1.4 0.2 setosa -1.02 1.25
#> 6 5.4 3.9 1.7 0.4 setosa -0.54 1.93
#> 7 4.6 3.4 1.4 0.3 setosa -1.50 0.79
#> 8 5.0 3.4 1.5 0.2 setosa -1.02 0.79
#> 9 4.4 2.9 1.4 0.2 setosa -1.74 -0.36
#> 10 4.9 3.1 1.5 0.1 setosa -1.14 0.10
#> # ... with 140 more rows
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