dtrackr (version 0.4.6)

p_transmute: dplyr modifying operations

Description

See dplyr::mutate(), dplyr::add_count(), dplyr::add_tally(), dplyr::transmute(), dplyr::select(), dplyr::relocate(), dplyr::rename() dplyr::rename_with(), dplyr::arrange() for more details on underlying functions. dtrackr provides equivalent functions for mutating, selecting and renaming a data set which act in the same way as dplyr. mutate / select / rename generally don't add anything in terms of provenance of data so the default behaviour is to miss these out of the dtrackr history. This can be overridden with the .messages, or .headline values in which case they behave just like a comment().

Usage

p_transmute(.data, ..., .messages = "", .headline = "", .tag = NULL)

Value

the .data dataframe after being modified by the dplyr equivalent function, but with the history graph updated with a new stage if the .messages or .headline parameter is not empty.

Arguments

.data

A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.

...

<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.

.messages

a set of glue specs. The glue code can use any global variable, grouping variable, {.new_cols} or {.dropped_cols} for changes to columns, {.cols} for the output column names, or {.strata}. Defaults to nothing.

.headline

a headline glue spec. The glue code can use any global variable, grouping variable, {.new_cols}, {.dropped_cols}, {.cols} or {.strata}. Defaults to nothing.

.tag

if you want the summary data from this step in the future then give it a name with .tag.

See Also

dplyr::transmute()

Examples

library(dplyr)
library(dtrackr)

# mutate and other functions are unitary operations that generally change
# the structure but not size of a dataframe. In dtrackr these are by ignored
# by default but we can change that so that their behaviour is obvious.

# In this example we compare the column names of the input and the
# output to identify the new columns created by the transmute operation as
# the `.new_cols` variable
# Here we do the same for a transmute()
iris %>%
  track() %>%
  group_by(Species, .add=TRUE) %>%
  transmute(
    sepal.w = Sepal.Width-1,
    sepal.l = Sepal.Length+1,
    .messages="{.new_cols}",
    .headline="New columns from transmute:") %>%
  history()