colwise.R
In dplyr: A Grammar of Data Manipulation

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
set.seed(1014)

## ----eval = FALSE-------------------------------------------------------------
# df |>
#   group_by(g1, g2) |>
#   summarise(a = mean(a), b = mean(b), c = mean(c), d = mean(d))

## ----eval = FALSE-------------------------------------------------------------
# df |>
#   group_by(g1, g2) |>
#   summarise(across(a:d, mean))

## ----setup--------------------------------------------------------------------
library(dplyr, warn.conflicts = FALSE)

## -----------------------------------------------------------------------------
starwars |>
  summarise(across(where(is.character), n_distinct))

starwars |>
  group_by(species) |>
  filter(n() > 1) |>
  summarise(across(c(sex, gender, homeworld), n_distinct))

starwars |>
  group_by(homeworld) |>
  filter(n() > 1) |>
  summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE)))

## -----------------------------------------------------------------------------
df <- data.frame(g = c(1, 1, 2), x = c(-1, 1, 3), y = c(-1, -4, -9))
df |>
  group_by(g) |>
  summarise(across(where(is.numeric), sum))

## -----------------------------------------------------------------------------
min_max <- list(
  min = ~min(.x, na.rm = TRUE),
  max = ~max(.x, na.rm = TRUE)
)
starwars |> summarise(across(where(is.numeric), min_max))
starwars |> summarise(across(c(height, mass, birth_year), min_max))

## -----------------------------------------------------------------------------
starwars |> summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}"))
starwars |> summarise(across(c(height, mass, birth_year), min_max, .names = "{.fn}.{.col}"))

## -----------------------------------------------------------------------------
starwars |> summarise(
  across(c(height, mass, birth_year), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
  across(c(height, mass, birth_year), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
)

## -----------------------------------------------------------------------------
starwars |> summarise(
  tibble(
    across(where(is.numeric), ~min(.x, na.rm = TRUE), .names = "min_{.col}"),
    across(where(is.numeric), ~max(.x, na.rm = TRUE), .names = "max_{.col}")
  )
)

## -----------------------------------------------------------------------------
starwars |>
  summarise(across(where(is.numeric), min_max, .names = "{.fn}.{.col}")) |>
  relocate(starts_with("min"))

## -----------------------------------------------------------------------------
df <- tibble(x = 1:3, y = 3:5, z = 5:7)
mult <- list(x = 1, y = 10, z = 100)

df |> mutate(across(all_of(names(mult)), ~ .x * mult[[cur_column()]]))

## -----------------------------------------------------------------------------
df <- data.frame(x = c(1, 2, 3), y = c(1, 4, 9))

df |>
  summarise(n = n(), across(where(is.numeric), sd))

## -----------------------------------------------------------------------------
df |>
  summarise(across(where(is.numeric), sd), n = n())

## -----------------------------------------------------------------------------
df |>
  summarise(n = n(), across(where(is.numeric) & !n, sd))

## -----------------------------------------------------------------------------
df |>
  summarise(
    tibble(n = n(), across(where(is.numeric), sd))
  )

## -----------------------------------------------------------------------------
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}
df <- tibble(x = 1:4, y = rnorm(4))
df |> mutate(across(where(is.numeric), rescale01))

## -----------------------------------------------------------------------------
starwars |> distinct(pick(contains("color")))

## -----------------------------------------------------------------------------
starwars |> count(pick(contains("color")), sort = TRUE)

## -----------------------------------------------------------------------------
starwars |>
  filter_out(if_any(everything(), is.na))

## -----------------------------------------------------------------------------
starwars |>
  filter_out(if_all(everything(), is.na))

## ----eval = FALSE-------------------------------------------------------------
# df |>
#   group_by(g1, g2) |>
#   summarise(
#     across(where(is.numeric), mean),
#     across(where(is.factor), nlevels),
#     n = n(),
#   )

## ----results = FALSE----------------------------------------------------------
df |> mutate_if(is.numeric, ~mean(.x, na.rm = TRUE))
# ->
df |> mutate(across(where(is.numeric), ~mean(.x, na.rm = TRUE)))

df |> mutate_at(vars(c(x, starts_with("y"))), mean)
# ->
df |> mutate(across(c(x, starts_with("y")), mean))

df |> mutate_all(mean)
# ->
df |> mutate(across(everything(), mean))

## -----------------------------------------------------------------------------
df <- tibble(x = c("a", "b"), y = c(1, 1), z = c(-1, 1))

# Find all rows where EVERY numeric variable is greater than zero
df |> filter(if_all(where(is.numeric), ~ .x > 0))

# Find all rows where ANY numeric variable is greater than zero
df |> filter(if_any(where(is.numeric), ~ .x > 0))

## -----------------------------------------------------------------------------
df <- tibble(x = 2, y = 4, z = 8)
df |> mutate_all(~ .x / y)

df |> mutate(across(everything(), ~ .x / y))