tsibble (version 0.8.6)

fill_gaps: Turn implicit missing values into explicit missing values

Description

stable

Usage

fill_gaps(.data, ..., .full = FALSE)

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 to insert NA for each series within its own period. TRUE to fill NA over the entire time span of the data (a.k.a. fully balanced panel).

See Also

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

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

Examples

# NOT RUN {
library(dplyr)
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)
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")
# }