fill_gaps: Turn implicit missing values into explicit missing values

In tsibble: Tidy Temporal Data Frames and Tools

Turn implicit missing values into explicit missing values

Description

Usage

fill_gaps(.data, ..., .full = FALSE, .start = NULL, .end = NULL)

Arguments

.data	A tsibble.
...	A set of name-value pairs. The values provided will only replace missing values that were marked as "implicit", and will leave previously existing NA untouched. empty: filled with default NA. filled by values or functions.
.full	FALSE inserts NA for each keyed unit within its own period. TRUE fills NA over the entire time span of the data (a.k.a. fully balanced panel). start() pad NA to the same starting point (i.e. ⁠min(<index>)⁠) across units. end() pad NA to the same ending point (i.e. ⁠max(<index>)⁠) across units.
.start, .end	Set custom starting/ending time that allows to expand the existing time spans.

See Also

tidyr::fill, tidyr::replace_na for handling missing values NA.

Other implicit gaps handling: count_gaps(), has_gaps(), scan_gaps()

Examples

harvest <- tsibble(
  year = c(2010, 2011, 2013, 2011, 2012, 2014),
  fruit = rep(c("kiwi", "cherry"), each = 3),
  kilo = sample(1:10, size = 6),
  key = fruit, index = year
)

# gaps as default `NA`
fill_gaps(harvest, .full = TRUE)
fill_gaps(harvest, .full = start())
fill_gaps(harvest, .full = end())
fill_gaps(harvest, .start = 2009, .end = 2016)
full_harvest <- fill_gaps(harvest, .full = FALSE)
full_harvest

# replace gaps with a specific value
harvest %>%
  fill_gaps(kilo = 0L)

# replace gaps using a function by variable
harvest %>%
  fill_gaps(kilo = sum(kilo))

# replace gaps using a function for each group
harvest %>%
  group_by_key() %>%
  fill_gaps(kilo = sum(kilo))

# leaves existing `NA` untouched
harvest[2, 3] <- NA
harvest %>%
  group_by_key() %>%
  fill_gaps(kilo = sum(kilo, na.rm = TRUE))

# replace NA
pedestrian %>%
  group_by_key() %>%
  fill_gaps(Count = as.integer(median(Count)))

if (!requireNamespace("tidyr", quietly = TRUE)) {
  stop("Please install the 'tidyr' package to run these following examples.")
}
# use fill() to fill `NA` by previous/next entry
pedestrian %>%
  group_by_key() %>%
  fill_gaps() %>%
  tidyr::fill(Count, .direction = "down")

tsibble documentation built on June 27, 2024, 5:06 p.m.