tsibble (version 1.1.5)

fill_gaps: 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")