R/step-subset-expand.R
expand.dtplyr_step.RdThis is a method for the tidyr expand() generic. It is translated to
data.table::CJ().
# S3 method for class 'dtplyr_step'
expand(data, ..., .name_repair = "check_unique")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).
Treatment of problematic column names:
"minimal": No name repair or checks, beyond basic existence,
"unique": Make sure names are unique and not empty,
"check_unique": (default value), no name repair, but check they are
unique,
"universal": Make the names unique and syntactic
a function: apply custom name repair (e.g., .name_repair = make.names
for names in the style of base R).
A purrr-style anonymous function, see rlang::as_function()
This argument is passed on as repair to vctrs::vec_as_names().
See there for more details on these terms and the strategies used
to enforce them.
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)
#> Source: local data table [2 x 1]
#> Call: `_DT9`[, CJ(type = type, unique = TRUE)]
#>
#> type
#> <chr>
#> 1 apple
#> 2 orange
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
fruits %>% expand(type, size)
#> Source: local data table [6 x 2]
#> Call: `_DT9`[, CJ(type = type, size = size, unique = TRUE)]
#>
#> type size
#> <chr> <fct>
#> 1 apple XS
#> 2 apple S
#> 3 apple M
#> 4 orange XS
#> 5 orange S
#> 6 orange M
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# This is different from the data frame behaviour:
fruits %>% dplyr::collect() %>% expand(type, size)
#> # A tibble: 8 × 2
#> type size
#> <chr> <fct>
#> 1 apple XS
#> 2 apple S
#> 3 apple M
#> 4 apple L
#> 5 orange XS
#> 6 orange S
#> 7 orange M
#> 8 orange L
# Other uses -------------------------------------------------------
fruits %>% expand(type, size, 2010:2012)
#> Source: local data table [18 x 3]
#> Call: `_DT9`[, CJ(type = type, size = size, V3 = 2010:2012, unique = TRUE)]
#>
#> type size V3
#> <chr> <fct> <int>
#> 1 apple XS 2010
#> 2 apple XS 2011
#> 3 apple XS 2012
#> 4 apple S 2010
#> 5 apple S 2011
#> 6 apple S 2012
#> # ℹ 12 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Use `anti_join()` to determine which observations are missing
all <- fruits %>% expand(type, size, year)
all
#> Source: local data table [12 x 3]
#> Call: `_DT9`[, CJ(type = type, size = size, year = year, unique = TRUE)]
#>
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2010
#> 2 apple XS 2012
#> 3 apple S 2010
#> 4 apple S 2012
#> 5 apple M 2010
#> 6 apple M 2012
#> # ℹ 6 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
all %>% dplyr::anti_join(fruits)
#> Joining, by = c("type", "size", "year")
#> Source: local data table [8 x 3]
#> Call: `_DT9`[, CJ(type = type, size = size, year = year, unique = TRUE)][!`_DT9`,
#> on = .(type, size, year)]
#>
#> type size year
#> <chr> <fct> <dbl>
#> 1 apple XS 2012
#> 2 apple S 2010
#> 3 apple S 2012
#> 4 apple M 2010
#> 5 orange XS 2010
#> 6 orange XS 2012
#> # ℹ 2 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Use with `right_join()` to fill in missing rows
fruits %>% dplyr::right_join(all)
#> Joining, by = c("type", "year", "size")
#> Source: local data table [14 x 4]
#> Call: `_DT9`[`_DT9`[, CJ(type = type, size = size, year = year, unique = TRUE)],
#> on = .(type, year, size), allow.cartesian = TRUE]
#>
#> type year size weights
#> <chr> <dbl> <fct> <dbl>
#> 1 apple 2010 XS 3.43
#> 2 apple 2012 XS NA
#> 3 apple 2010 S NA
#> 4 apple 2012 S NA
#> 5 apple 2010 M NA
#> 6 apple 2012 M 3.86
#> # ℹ 8 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results