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().
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.
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.
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()
#> dtrackr history:
#> number of flowchart steps: 3 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> ├ [Species:setosa]: "New columns from transmute:", "sepal.w, sepal.l"
#> ├ [Species:versicolor]: "New columns from transmute:", "sepal.w, sepal.l"
#> └ [Species:virginica]: "New columns from transmute:", "sepal.w, sepal.l"