Equivalent dplyr
functions for mutating, selecting and renaming a data set
act in the normal way. mutates / selects / rename generally don't add
anything in documentation so the default behaviour is to miss these out of
the history. This can be overridden with the .messages, or .headline values
in which case they behave just like a comment()
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.
# S3 method for trackr_df
arrange(.data, ..., .messages = "", .headline = "", .tag = NULL)
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.
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.
a headline glue spec. The glue code can use any global variable, grouping variable, {.new_cols}, {.dropped_cols}, {.cols} or {.strata}. Defaults to nothing.
if you want the summary data from this step in the future then give it a name with .tag.
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.
dplyr::arrange()
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.
# arrange
# In this case we sort the data descending and show the first value
# is the same as the maximum value.
iris %>%
track() %>%
arrange(
desc(Petal.Width),
.messages="{.count} items, columns: {.cols}",
.headline="Reordered dataframe:") %>%
history()
#> dtrackr history:
#> number of flowchart steps: 2 (approx)
#> tags defined: <none>
#> items excluded so far: <not capturing exclusions>
#> last entry / entries:
#> └ "Reordered dataframe:", "150 items, columns: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species"