fill: Fill in missing values with previous or next value

In tidyverse/tidyr: Tidy Messy Data

Fill in missing values with previous or next value

Description

Fills missing values in selected columns using the next or previous entry. This is useful in the common output format where values are not repeated, and are only recorded when they change.

Usage

fill(data, ..., .by = NULL, .direction = c("down", "up", "downup", "updown"))

Arguments

data	A data frame.
...	<tidy-select> Columns to fill.
.by	<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.
.direction	Direction in which to fill missing values. Currently either "down" (the default), "up", "downup" (i.e. first down and then up) or "updown" (first up and then down).

Details

Missing values are replaced in atomic vectors; NULLs are replaced in lists.

Grouped data frames

With grouped data frames created by dplyr::group_by(), fill() will be applied within each group, meaning that it won't fill across group boundaries. This can also be accomplished using the .by argument to fill(), which creates a temporary grouping for just this operation.

Examples

# direction = "down" --------------------------------------------------------
# Value (year) is recorded only when it changes
sales <- tibble::tribble(
  ~quarter, ~year, ~sales,
  "Q1",    2000,    66013,
  "Q2",      NA,    69182,
  "Q3",      NA,    53175,
  "Q4",      NA,    21001,
  "Q1",    2001,    46036,
  "Q2",      NA,    58842,
  "Q3",      NA,    44568,
  "Q4",      NA,    50197,
  "Q1",    2002,    39113,
  "Q2",      NA,    41668,
  "Q3",      NA,    30144,
  "Q4",      NA,    52897,
  "Q1",    2004,    32129,
  "Q2",      NA,    67686,
  "Q3",      NA,    31768,
  "Q4",      NA,    49094
)
# `fill()` defaults to replacing missing data from top to bottom
sales %>% fill(year)

# direction = "up" ----------------------------------------------------------
# Value (pet_type) is missing above
tidy_pets <- tibble::tribble(
  ~rank, ~pet_type, ~breed,
  1L,        NA,    "Boston Terrier",
  2L,        NA,    "Retrievers (Labrador)",
  3L,        NA,    "Retrievers (Golden)",
  4L,        NA,    "French Bulldogs",
  5L,        NA,    "Bulldogs",
  6L,     "Dog",    "Beagles",
  1L,        NA,    "Persian",
  2L,        NA,    "Maine Coon",
  3L,        NA,    "Ragdoll",
  4L,        NA,    "Exotic",
  5L,        NA,    "Siamese",
  6L,     "Cat",    "American Short"
)

# For values that are missing above you can use `.direction = "up"`
tidy_pets %>%
  fill(pet_type, .direction = "up")

# direction = "downup" ------------------------------------------------------
# Value (n_squirrels) is missing above and below within a group
squirrels <- tibble::tribble(
  ~group,    ~name,     ~role,     ~n_squirrels,
  1,      "Sam",    "Observer",   NA,
  1,     "Mara", "Scorekeeper",    8,
  1,    "Jesse",    "Observer",   NA,
  1,      "Tom",    "Observer",   NA,
  2,     "Mike",    "Observer",   NA,
  2,  "Rachael",    "Observer",   NA,
  2,  "Sydekea", "Scorekeeper",   14,
  2, "Gabriela",    "Observer",   NA,
  3,  "Derrick",    "Observer",   NA,
  3,     "Kara", "Scorekeeper",    9,
  3,    "Emily",    "Observer",   NA,
  3, "Danielle",    "Observer",   NA
)

# The values are inconsistently missing by position within the `group`.
# Use `.direction = "downup"` to fill missing values in both directions
# and `.by = group` to apply the fill per group.
squirrels %>%
  fill(n_squirrels, .direction = "downup", .by = group)

# If you want, you can also supply a data frame grouped with `group_by()`,
# but don't forget to `ungroup()`!
squirrels %>%
  dplyr::group_by(group) %>%
  fill(n_squirrels, .direction = "downup") %>%
  dplyr::ungroup()

tidyverse/tidyr documentation built on Oct. 30, 2024, 1:53 a.m.