RDocumentation: nest

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if(!require('tidyr')) {
    install.packages('tidyr')
    library('tidyr')
}

df <- tibble(x = c(1, 1, 1, 2, 2, 3), y = 1:6, z = 6:1)
# Note that we get one row of output for each unique combination of
# non-nested variables
df %>% nest(data = c(y, z))
# chop does something similar, but retains individual columns
df %>% chop(c(y, z))

# use tidyselect syntax and helpers, just like in dplyr::select()
df %>% nest(data = any_of(c("y", "z")))

iris %>% nest(data = !Species)
nest_vars <- names(iris)[1:4]
iris %>% nest(data = any_of(nest_vars))
iris %>%
  nest(petal = starts_with("Petal"), sepal = starts_with("Sepal"))
iris %>%
  nest(width = contains("Width"), length = contains("Length"))

# Nesting a grouped data frame nests all variables apart from the group vars
library(dplyr)
fish_encounters %>%
  group_by(fish) %>%
  nest()

# Nesting is often useful for creating per group models
mtcars %>%
  group_by(cyl) %>%
  nest() %>%
  mutate(models = lapply(data, function(df) lm(mpg ~ wt, data = df)))

# unnest() is primarily designed to work with lists of data frames
df <- tibble(
  x = 1:3,
  y = list(
    NULL,
    tibble(a = 1, b = 2),
    tibble(a = 1:3, b = 3:1)
  )
)
df %>% unnest(y)
df %>% unnest(y, keep_empty = TRUE)

# If you have lists of lists, or lists of atomic vectors, instead
# see hoist(), unnest_wider(), and unnest_longer()

#' # You can unnest multiple columns simultaneously
df <- tibble(
 a = list(c("a", "b"), "c"),
 b = list(1:2, 3),
 c = c(11, 22)
)
df %>% unnest(c(a, b))

# Compare with unnesting one column at a time, which generates
# the Cartesian product
df %>% unnest(a) %>% unnest(b)