This is a method for the tidyr pivot_wider() generic. It is translated to
data.table::dcast()
# S3 method for class 'dtplyr_step'
pivot_wider(
data,
id_cols = NULL,
names_from = name,
names_prefix = "",
names_sep = "_",
names_glue = NULL,
names_sort = FALSE,
names_repair = "check_unique",
values_from = value,
values_fill = NULL,
values_fn = NULL,
...
)A lazy_dt().
<tidy-select> A set of columns that
uniquely identify each observation. Typically used when you have
redundant variables, i.e. variables whose values are perfectly correlated
with existing variables.
Defaults to all columns in data except for the columns specified through
names_from and values_from. If a tidyselect expression is supplied, it
will be evaluated on data after removing the columns specified through
names_from and values_from.
<tidy-select> A pair of
arguments describing which column (or columns) to get the name of the
output column (names_from), and which column (or columns) to get the
cell values from (values_from).
If values_from contains multiple values, the value will be added to the
front of the output column.
String added to the start of every variable name. This is
particularly useful if names_from is a numeric vector and you want to
create syntactic variable names.
If names_from or values_from contains multiple
variables, this will be used to join their values together into a single
string to use as a column name.
Instead of names_sep and names_prefix, you can supply
a glue specification that uses the names_from columns (and special
.value) to create custom column names.
Should the column names be sorted? If FALSE, the default,
column names are ordered by first appearance.
What happens if the output has invalid column names?
The default, "check_unique" is to error if the columns are duplicated.
Use "minimal" to allow duplicates in the output, or "unique" to
de-duplicated by adding numeric suffixes. See vctrs::vec_as_names()
for more options.
Optionally, a (scalar) value that specifies what each
value should be filled in with when missing.
This can be a named list if you want to apply different fill values to different value columns.
A function, the default is length(). Note this is different
behavior than tidyr::pivot_wider(), which returns a list column by default.
Additional arguments passed on to methods.
library(tidyr)
fish_encounters_dt <- lazy_dt(fish_encounters)
fish_encounters_dt
#> Source: local data table [114 x 3]
#> Call: `_DT30`
#>
#> fish station seen
#> <fct> <fct> <int>
#> 1 4842 Release 1
#> 2 4842 I80_1 1
#> 3 4842 Lisbon 1
#> 4 4842 Rstr 1
#> 5 4842 Base_TD 1
#> 6 4842 BCE 1
#> # ℹ 108 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
fish_encounters_dt %>%
pivot_wider(names_from = station, values_from = seen)
#> Source: local data table [19 x 12]
#> Call: dcast(`_DT30`, formula = fish ~ station, value.var = "seen")
#>
#> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW
#> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 4842 1 1 1 1 1 1 1 1 1 1 1
#> 2 4843 1 1 1 1 1 1 1 1 1 1 1
#> 3 4844 1 1 1 1 1 1 1 1 1 1 1
#> 4 4845 1 1 1 1 1 NA NA NA NA NA NA
#> 5 4847 1 1 1 NA NA NA NA NA NA NA NA
#> 6 4848 1 1 1 1 NA NA NA NA NA NA NA
#> # ℹ 13 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Fill in missing values
fish_encounters_dt %>%
pivot_wider(names_from = station, values_from = seen, values_fill = 0)
#> Source: local data table [19 x 12]
#> Call: dcast(`_DT30`, formula = fish ~ station, value.var = "seen",
#> fill = 0)
#>
#> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE MAW
#> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 4842 1 1 1 1 1 1 1 1 1 1 1
#> 2 4843 1 1 1 1 1 1 1 1 1 1 1
#> 3 4844 1 1 1 1 1 1 1 1 1 1 1
#> 4 4845 1 1 1 1 1 0 0 0 0 0 0
#> 5 4847 1 1 1 0 0 0 0 0 0 0 0
#> 6 4848 1 1 1 1 0 0 0 0 0 0 0
#> # ℹ 13 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Generate column names from multiple variables
us_rent_income_dt <- lazy_dt(us_rent_income)
us_rent_income_dt
#> Source: local data table [104 x 5]
#> Call: `_DT31`
#>
#> GEOID NAME variable estimate moe
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 01 Alabama income 24476 136
#> 2 01 Alabama rent 747 3
#> 3 02 Alaska income 32940 508
#> 4 02 Alaska rent 1200 13
#> 5 04 Arizona income 27517 148
#> 6 04 Arizona rent 972 4
#> # ℹ 98 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
us_rent_income_dt %>%
pivot_wider(names_from = variable, values_from = c(estimate, moe))
#> Source: local data table [52 x 6]
#> Call: dcast(`_DT31`, formula = GEOID + NAME ~ variable, value.var = c("estimate",
#> "moe"))
#>
#> GEOID NAME estimate_income estimate_rent moe_income moe_rent
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 01 Alabama 24476 747 136 3
#> 2 02 Alaska 32940 1200 508 13
#> 3 04 Arizona 27517 972 148 4
#> 4 05 Arkansas 23789 709 165 5
#> 5 06 California 29454 1358 109 3
#> 6 08 Colorado 32401 1125 109 5
#> # ℹ 46 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# When there are multiple `names_from` or `values_from`, you can use
# use `names_sep` or `names_glue` to control the output variable names
us_rent_income_dt %>%
pivot_wider(
names_from = variable,
names_sep = ".",
values_from = c(estimate, moe)
)
#> Source: local data table [52 x 6]
#> Call: dcast(`_DT31`, formula = GEOID + NAME ~ variable, value.var = c("estimate",
#> "moe"), sep = ".")
#>
#> GEOID NAME estimate.income estimate.rent moe.income moe.rent
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 01 Alabama 24476 747 136 3
#> 2 02 Alaska 32940 1200 508 13
#> 3 04 Arizona 27517 972 148 4
#> 4 05 Arkansas 23789 709 165 5
#> 5 06 California 29454 1358 109 3
#> 6 08 Colorado 32401 1125 109 5
#> # ℹ 46 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Can perform aggregation with values_fn
warpbreaks_dt <- lazy_dt(as_tibble(warpbreaks[c("wool", "tension", "breaks")]))
warpbreaks_dt
#> Source: local data table [54 x 3]
#> Call: `_DT32`
#>
#> wool tension breaks
#> <fct> <fct> <dbl>
#> 1 A L 26
#> 2 A L 30
#> 3 A L 54
#> 4 A L 25
#> 5 A L 70
#> 6 A L 52
#> # ℹ 48 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
warpbreaks_dt %>%
pivot_wider(
names_from = wool,
values_from = breaks,
values_fn = mean
)
#> Source: local data table [3 x 3]
#> Call: dcast(`_DT32`, formula = tension ~ wool, value.var = "breaks",
#> fun.aggregate = function (x, ...)
#> UseMethod("mean"))
#>
#> tension A B
#> <fct> <dbl> <dbl>
#> 1 L 44.6 28.2
#> 2 M 24 28.8
#> 3 H 24.6 18.8
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results