tsibble (version 0.6.2)

fill_gaps: Turn implicit missing values into explicit missing values

Description

Turn implicit missing values into explicit missing values

Usage

fill_gaps(.data, ...)
# S3 method for tbl_ts
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 key 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 {
harvest <- tsibble(
  year = c(2010, 2011, 2013, 2011, 2012, 2014),
  fruit = rep(c("kiwi", "cherry"), each = 3),
  kilo = sample(1:10, size = 6),
  key = id(fruit), index = year
)

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

# use fill() to fill `NA` by previous/next entry
full_harvest %>% 
  group_by(fruit) %>% 
  fill(kilo, .direction = "down")

# 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(fruit) %>%
  fill_gaps(kilo = sum(kilo))

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

# replace NA ----
pedestrian %>%
  group_by(Sensor) %>%
  fill_gaps(Count = as.integer(median(Count)))
# }