complete.dtplyr_step: Complete a data frame with missing combinations of data

In dtplyr: Data Table Back-End for 'dplyr'

Complete a data frame with missing combinations of data

Description

This is a method for the tidyr complete() generic. This is a wrapper around dtplyr translations for expand(), full_join(), and replace_na() that's useful for completing missing combinations of data.

Usage

## S3 method for class 'dtplyr_step'
complete(data, ..., fill = list())

Arguments

data	A lazy_dt().
...	<data-masking> Specification of columns to expand or complete. Columns can be atomic vectors or lists. To find all unique combinations of x, y and z, including those not present in the data, supply each variable as a separate argument: expand(df, x, y, z) or complete(df, x, y, z). To find only the combinations that occur in the data, use nesting: expand(df, nesting(x, y, z)). You can combine the two forms. For example, expand(df, nesting(school_id, student_id), date) would produce a row for each present school-student combination for all possible dates. When used with factors, expand() and complete() use the full set of levels, not just those that appear in the data. If you want to use only the values seen in the data, use forcats::fct_drop(). When used with continuous variables, you may need to fill in values that do not appear in the data: to do so use expressions like year = 2010:2020 or year = full_seq(year,1).
fill	A named list that for each variable supplies a single value to use instead of NA for missing combinations.

Examples

library(tidyr)
tbl <- tibble(x = 1:2, y = 1:2, z = 3:4)
dt <- lazy_dt(tbl)

dt %>%
  complete(x, y)

dt %>%
  complete(x, y, fill = list(z = 10L))

dtplyr documentation built on March 31, 2023, 9:13 p.m.