dtplyr (version 1.3.3)

expand.dtplyr_step: Expand data frame to include all possible combinations of values.

Description

This is a method for the tidyr expand() generic. It is translated to data.table::CJ().

Usage

# S3 method for dtplyr_step
expand(data, ..., .name_repair = "check_unique")

Arguments

data

A lazy_dt().

...

Specification of columns to expand. 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).

• 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.

Unlike the data.frame method, this method does not use the full set of levels, just those that appear in the data.

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).

.name_repair

One of "check_unique", "unique", "universal", "minimal", "unique_quiet", or "universal_quiet". See vec_as_names() for the meaning of these options.

Examples

library(tidyr)

fruits <- lazy_dt(tibble(
  type   = c("apple", "orange", "apple", "orange", "orange", "orange"),
  year   = c(2010, 2010, 2012, 2010, 2010, 2012),
  size  =  factor(
    c("XS", "S",  "M", "S", "S", "M"),
    levels = c("XS", "S", "M", "L")
  ),
  weights = rnorm(6, as.numeric(size) + 2)
))

# All possible combinations ---------------------------------------
# Note that only present levels of the factor variable `size` are retained.
fruits %>% expand(type)
fruits %>% expand(type, size)

# This is different from the data frame behaviour:
fruits %>% dplyr::collect() %>% expand(type, size)

# Other uses -------------------------------------------------------
fruits %>% expand(type, size, 2010:2012)

# Use `anti_join()` to determine which observations are missing
all <- fruits %>% expand(type, size, year)
all
all %>% dplyr::anti_join(fruits)

# Use with `right_join()` to fill in missing rows
fruits %>% dplyr::right_join(all)