Question: I often want to perform `tidyr::spread` and `dplyr::summarise` in a "single step" to aggregate data by group. What I want is shown in `expected`. I can get `expected` by performing `summarise` and `spread` separately and combine the results with a `dplyr::full_join` but I'm looking for **alternative approaches that avoid full\_join**. Bona fide single-step approaches are not necessary.

```
df <- data.frame(
        id = rep(letters[1], 2),
        val1 = c(10, 20),
        val2 = c(100, 200),
        key = c("A", "B"),
        value = c(1, 2))

library(tidyverse)
result1 <- df %>%
              group_by(id) %>%
              summarise(
                val1 = min(val1),
                val2 = max(val2)
              )
# A tibble: 1 x 3
  # id      val1  val2
  # <fctr> <dbl> <dbl>
# 1 a       10.0   200

result2 <- df %>%
              select(id, key, value) %>%
              group_by(id) %>%
              spread(key, value)
# A tibble: 1 x 3
# Groups: id [1]
  # id         A     B
# * <fctr> <dbl> <dbl>
# 1 a       1.00  2.00

expected <- full_join(result1, result2, by="id")
# A tibble: 1 x 5
  # id      val1  val2     A     B
  # <fctr> <dbl> <dbl> <dbl> <dbl>
# 1 a       10.0   200  1.00  2.00

```

Answer: 
I suspect your data may have more edge cases that require some modification, but why don't you simply `spread` then `summarise`? You can specify the summary function separately per variable, so for `A` and `B` where you don't actually need to calculate anything (I'm assuming) you can just remove all the `NA`:

```
df %>%
  spread("key", "value") %>%
  group_by(id) %>%
  summarise(
    val1 = min(val1),
    val2 = max(val2),
    A = mean(A, na.rm = TRUE),
    B = mean(B, na.rm = TRUE)
    )
# A tibble: 1 x 5
  id     val1  val2     A     B
  <fct> <dbl> <dbl> <dbl> <dbl>
1 a      10.0   200  1.00  2.00

```
Self-answer: Here's an approach that works with `tidyr::nest` but it seems "messy" and not much better

```
df %>%
  group_by(id) %>%
  nest() %>%
  mutate(
    min_vals = map(data, ~.x %>% summarise(min_val = min(val1), max_val = max(val2))),
    data = map(data, ~select(.x, key, value) %>% spread(key, value))
  ) %>%
  unnest()

# A tibble: 1 x 5
  # id         A     B min_val max_val
  # <fctr> <dbl> <dbl>   <dbl>   <dbl>
# 1 a       1.00  2.00    10.0     200

```