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

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

## ----include = FALSE----------------------------------------------------------
nest_by <- function(df, ...) {
  df |>
    group_by(...) |>
    summarise(data = list(pick(everything()))) |>
    rowwise(...)
}
# mtcars |> nest_by(cyl)

## -----------------------------------------------------------------------------
df <- tibble(x = 1:2, y = 3:4, z = 5:6)
df |> rowwise()

## -----------------------------------------------------------------------------
df |> mutate(m = mean(c(x, y, z)))
df |> rowwise() |> mutate(m = mean(c(x, y, z)))

## -----------------------------------------------------------------------------
df <- tibble(name = c("Mara", "Hadley"), x = 1:2, y = 3:4, z = 5:6)

df |>
  rowwise() |>
  summarise(m = mean(c(x, y, z)))

df |>
  rowwise(name) |>
  summarise(m = mean(c(x, y, z)))

## -----------------------------------------------------------------------------
df <- tibble(id = 1:6, w = 10:15, x = 20:25, y = 30:35, z = 40:45)
df

## -----------------------------------------------------------------------------
rf <- df |> rowwise(id)

## -----------------------------------------------------------------------------
rf |> mutate(total = sum(c(w, x, y, z)))
rf |> summarise(total = sum(c(w, x, y, z)))

## -----------------------------------------------------------------------------
rf |> mutate(total = sum(c_across(w:z)))
rf |> mutate(total = sum(c_across(where(is.numeric))))

## -----------------------------------------------------------------------------
rf |>
  mutate(total = sum(c_across(w:z))) |>
  ungroup() |>
  mutate(across(w:z, ~ . / total))

## -----------------------------------------------------------------------------
df |> mutate(total = rowSums(pick(where(is.numeric), -id)))
df |> mutate(mean = rowMeans(pick(where(is.numeric), -id)))

## -----------------------------------------------------------------------------
df <- tibble(
  x = list(1, 2:3, 4:6)
)

## -----------------------------------------------------------------------------
df |> mutate(l = length(x))

## -----------------------------------------------------------------------------
df |> mutate(l = lengths(x))

## -----------------------------------------------------------------------------
df |> mutate(l = sapply(x, length))
df |> mutate(l = purrr::map_int(x, length))

## -----------------------------------------------------------------------------
df |>
  rowwise() |>
  mutate(l = length(x))

## -----------------------------------------------------------------------------
df <- tibble(g = 1:2, y = list(1:3, "a"))
gf <- df |> group_by(g)
rf <- df |> rowwise(g)

## -----------------------------------------------------------------------------
gf |> mutate(type = typeof(y), length = length(y))
rf |> mutate(type = typeof(y), length = length(y))

## -----------------------------------------------------------------------------
# grouped
out1 <- integer(2)
for (i in 1:2) {
  out1[[i]] <- length(df$y[i])
}
out1

# rowwise
out2 <- integer(2)
for (i in 1:2) {
  out2[[i]] <- length(df$y[[i]])
}
out2

## ----error = TRUE-------------------------------------------------------------
try({
gf |> mutate(y2 = y)
rf |> mutate(y2 = y)
rf |> mutate(y2 = list(y))
})

## -----------------------------------------------------------------------------
by_cyl <- mtcars |> nest_by(cyl)
by_cyl

## -----------------------------------------------------------------------------
mods <- by_cyl |> mutate(mod = list(lm(mpg ~ wt, data = data)))
mods

## -----------------------------------------------------------------------------
mods <- mods |> mutate(pred = list(predict(mod, data)))
mods

## -----------------------------------------------------------------------------
mods |> summarise(rmse = sqrt(mean((pred - data$mpg) ^ 2)))
mods |> summarise(rsq = summary(mod)$r.squared)
mods |> summarise(broom::glance(mod))

## -----------------------------------------------------------------------------
mods |> reframe(broom::tidy(mod))

## -----------------------------------------------------------------------------
df <- tribble(
  ~ n, ~ min, ~ max,
    1,     0,     1,
    2,    10,   100,
    3,   100,  1000,
)

## -----------------------------------------------------------------------------
df |>
  rowwise() |>
  mutate(data = list(runif(n, min, max)))

## ----error = TRUE-------------------------------------------------------------
try({
df |>
  rowwise() |>
  mutate(data = runif(n, min, max))
})

## -----------------------------------------------------------------------------
df <- expand.grid(mean = c(-1, 0, 1), sd = c(1, 10, 100))

df |>
  rowwise() |>
  mutate(data = list(rnorm(10, mean, sd)))

## -----------------------------------------------------------------------------
df <- tribble(
   ~rng,     ~params,
   "runif",  list(n = 10),
   "rnorm",  list(n = 20),
   "rpois",  list(n = 10, lambda = 5),
) |>
  rowwise()

df |>
  mutate(data = list(do.call(rng, params)))

## ----include = FALSE, eval = FALSE--------------------------------------------
# df <- rowwise(tribble(
#    ~rng,     ~params,
#    "runif",  list(min = -1, max = 1),
#    "rnorm",  list(),
#    "rpois",  list(lambda = 5),
# ))
# 
# # Has to happen in separate function to avoid eager unquoting
# f <- function(rng, params) purrr::exec(rng, n = 10, !!!params)
# df |>
#   mutate(data = list(f(rng, params)))

## -----------------------------------------------------------------------------
mtcars |>
  group_by(cyl) |>
  do(head(., 1))

## -----------------------------------------------------------------------------
mtcars |>
  group_by(cyl) |>
  reframe(head(pick(everything()), 1))

## -----------------------------------------------------------------------------
mtcars |>
  group_by(cyl) |>
  do(nrows = nrow(.))

## -----------------------------------------------------------------------------
mtcars |>
  group_by(cyl) |>
  summarise(nrows = nrow(pick(everything())))