Create and modify columns — mutate.dtplyr

This is a method for the dplyr mutate() generic. It is translated to the j argument of [.data.table, using := to modify "in place". If .before or .after is provided, the new columns are relocated with a call to data.table::setcolorder().

Usage

# S3 method for class 'dtplyr_step'
mutate(
  .data,
  ...,
  .by = NULL,
  .keep = c("all", "used", "unused", "none"),
  .before = NULL,
  .after = NULL
)

Arguments

.data

A lazy_dt().

...

<data-masking> Name-value pairs. The name gives the name of the column in the output.

The value can be:

A vector of length 1, which will be recycled to the correct length.
A vector the same length as the current group (or the whole data frame if ungrouped).
NULL, to remove the column.
A data frame or tibble, to create multiple columns in the output.

.by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

.keep

Control which columns from .data are retained in the output. Grouping columns and columns created by ... are always kept.

"all" retains all columns from .data. This is the default.
"used" retains only the columns used in ... to create new columns. This is useful for checking your work, as it displays inputs and outputs side-by-side.
"unused" retains only the columns not used in ... to create new columns. This is useful if you generate new columns, but no longer need the columns used to generate them.
"none" doesn't retain any extra columns from .data. Only the grouping variables and columns created by ... are kept.

Note: With dtplyr .keep will only work with column names passed as symbols, and won't work with other workflows (e.g. eval(parse(text = "x + 1")))

.before, .after

<tidy-select> Optionally, control where new columns should appear (the default is to add to the right hand side). See relocate() for more details.

Examples

library(dplyr, warn.conflicts = FALSE)

dt <- lazy_dt(data.frame(x = 1:5, y = 5:1))
dt %>%
  mutate(a = (x + y) / 2, b = sqrt(x^2 + y^2))
#> Source: local data table [5 x 4]
#> Call:   copy(`_DT23`)[, `:=`(a = (x + y)/2, b = sqrt(x^2 + y^2))]
#> 
#>       x     y     a     b
#>   <int> <int> <dbl> <dbl>
#> 1     1     5     3  5.10
#> 2     2     4     3  4.47
#> 3     3     3     3  4.24
#> 4     4     2     3  4.47
#> 5     5     1     3  5.10
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

# It uses a more sophisticated translation when newly created variables
# are used in the same expression
dt %>%
  mutate(x1 = x + 1, x2 = x1 + 1)
#> Source: local data table [5 x 4]
#> Call:   copy(`_DT23`)[, `:=`(c("x1", "x2"), {
#>     x1 <- x + 1
#>     x2 <- x1 + 1
#>     .(x1, x2)
#> })]
#> 
#>       x     y    x1    x2
#>   <int> <int> <dbl> <dbl>
#> 1     1     5     2     3
#> 2     2     4     3     4
#> 3     3     3     4     5
#> 4     4     2     5     6
#> 5     5     1     6     7
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results