roll_na_fill {timeplyr}

Fast grouped "locf" NA fill

Description

A fast and efficient by-group method for "last-observation-carried-forward" NA filling.

Usage

roll_na_fill(x, g = NULL, fill_limit = Inf)

.roll_na_fill(x, fill_limit = Inf)

Arguments

x

A vector.

g

An object use for grouping x This may be a vector or data frame for example.

fill_limit

(Optional) maximum number of consecutive NAs to fill per NA cluster. Default is Inf.

Details

Method

When supplying groups using g, this method uses radixorder(g) to specify how to loop through x, making this extremely efficient.

When x contains zero or all NA values, then x is returned with no copy made.

.roll_na_fill() is the same as roll_na_fill() but without a g argument and it performs no sanity checks. It is passed straight to c++ which makes it efficient for loops.

Value

A filled vector of x the same length as x.

Examples

library(timeplyr)
library(dplyr)
library(data.table)

words <- do.call(paste0,
                 do.call(expand.grid, rep(list(letters), 3)))
groups <- sample(words, size = 10^5, replace = TRUE)
x <- sample.int(10^2, 10^5, TRUE)
x[sample.int(10^5, 10^4)] <- NA

dt <- data.table(x, groups)

filled <- roll_na_fill(x, groups)

library(zoo)

  # Summary
# Latest version of vctrs with their vec_fill_missing
# Is the fastest but not most memory efficient
# For low repetitions and large vectors, data.table is best

# For large numbers of repetitions (groups) and data
# that is sorted by groups
# timeplyr is fastest

# No groups
bench::mark(e1 = dt[, filled1 := timeplyr::roll_na_fill(x)][]$filled1,
            e2 = dt[, filled2 := data.table::nafill(x, type = "locf")][]$filled2,
            e3 = dt[, filled3 := vctrs::vec_fill_missing(x)][]$filled3,
            e4 = dt[, filled4 := zoo::na.locf0(x)][]$filled4,
            e5 = dt[, filled5 := timeplyr::.roll_na_fill(x)][]$filled5)
# With group
bench::mark(e1 = dt[, filled1 := timeplyr::roll_na_fill(x, groups)][]$filled1,
            e2 = dt[, filled2 := data.table::nafill(x, type = "locf"), by = groups][]$filled2,
            e3 = dt[, filled3 := vctrs::vec_fill_missing(x), by = groups][]$filled3,
            e4 = dt[, filled4 := timeplyr::.roll_na_fill(x), by = groups][]$filled4)
# Data sorted by groups
setkey(dt, groups)
bench::mark(e1 = dt[, filled1 := timeplyr::roll_na_fill(x, groups)][]$filled1,
            e2 = dt[, filled2 := data.table::nafill(x, type = "locf"), by = groups][]$filled2,
            e3 = dt[, filled3 := vctrs::vec_fill_missing(x), by = groups][]$filled3,
            e4 = dt[, filled4 := timeplyr::.roll_na_fill(x), by = groups][]$filled4)