![[Superseded]](figures/lifecycle-superseded.svg)
do() is superseded as of dplyr 1.0.0, because its syntax never really
felt like it belonged with the rest of dplyr. It's replaced by a combination
of summarise() (which can now produce multiple rows and multiple columns),
nest_by() (which creates a rowwise tibble of nested data),
and across() (which allows you to access the data for the "current" group).
do(.data, ...)Arguments
- .data
a tbl
- ...
Expressions to apply to each group. If named, results will be stored in a new column. If unnamed, must return a data frame. You can use
.to refer to the current group. You can not mix named and unnamed arguments.
Examples
# do() with unnamed arguments becomes summarise()
# . becomes across()
by_cyl <- mtcars %>% group_by(cyl)
by_cyl %>% do(head(., 2))
#> # A tibble: 6 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 4 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
# ->
by_cyl %>% summarise(head(across(), 2))
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups`
#> argument.
#> # A tibble: 6 × 11
#> # Groups: cyl [3]
#> cyl mpg disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 22.8 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 4 24.4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 6 21 160 110 3.9 2.62 16.5 0 1 4 4
#> 4 6 21 160 110 3.9 2.88 17.0 0 1 4 4
#> 5 8 18.7 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 8 14.3 360 245 3.21 3.57 15.8 0 0 3 4
by_cyl %>% slice_head(n = 2)
#> # A tibble: 6 × 11
#> # Groups: cyl [3]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 4 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
# Can refer to variables directly
by_cyl %>% do(mean = mean(.$vs))
#> # A tibble: 3 × 2
#> # Rowwise:
#> cyl mean
#> <dbl> <list>
#> 1 4 <dbl [1]>
#> 2 6 <dbl [1]>
#> 3 8 <dbl [1]>
# ->
by_cyl %>% summarise(mean = mean(vs))
#> # A tibble: 3 × 2
#> cyl mean
#> <dbl> <dbl>
#> 1 4 0.909
#> 2 6 0.571
#> 3 8 0
# do() with named arguments becomes nest_by() + mutate() & list()
models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .))
# ->
models <- mtcars %>%
nest_by(cyl) %>%
mutate(mod = list(lm(mpg ~ disp, data = data)))
models %>% summarise(rsq = summary(mod)$r.squared)
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups`
#> argument.
#> # A tibble: 3 × 2
#> # Groups: cyl [3]
#> cyl rsq
#> <dbl> <dbl>
#> 1 4 0.648
#> 2 6 0.0106
#> 3 8 0.270
# use broom to turn models into data
models %>% do(data.frame(
var = names(coef(.$mod)),
coef(summary(.$mod)))
)
#> # A tibble: 6 × 5
#> # Rowwise:
#> var Estimate Std..Error t.value Pr...t..
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 40.9 3.59 11.4 0.00000120
#> 2 disp -0.135 0.0332 -4.07 0.00278
#> 3 (Intercept) 19.1 2.91 6.55 0.00124
#> 4 disp 0.00361 0.0156 0.232 0.826
#> 5 (Intercept) 22.0 3.35 6.59 0.0000259
#> 6 disp -0.0196 0.00932 -2.11 0.0568
# ->
models %>% summarise(broom::tidy(mod))
#> `summarise()` has grouped output by 'cyl'. You can override using the `.groups`
#> argument.
#> # A tibble: 6 × 6
#> # Groups: cyl [3]
#> cyl term estimate std.error statistic p.value
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 4 (Intercept) 40.9 3.59 11.4 0.00000120
#> 2 4 disp -0.135 0.0332 -4.07 0.00278
#> 3 6 (Intercept) 19.1 2.91 6.55 0.00124
#> 4 6 disp 0.00361 0.0156 0.232 0.826
#> 5 8 (Intercept) 22.0 3.35 6.59 0.0000259
#> 6 8 disp -0.0196 0.00932 -2.11 0.0568